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Agile Infrastructure Design for Garment Industry

Agile Infrastructure Design for Garment Industry Fuzzy Inf. Eng. (2009)2:129-148 DOI 10.1007/s12543-009-0011-3 ORIGINAL ARTICLE Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge Received: 27 December 2008/ Revised: 3 April 2009/ Accepted: 10 May 2009/ © Springer and Fuzzy Information and Engineering Branch of the Operations Research Society of China Abstract The Information Economics and its platform can input the vigor into man- ufacturing industry, i.e., garment industry, help to increase its ability for good return, promote the level of technology and management of Garments from a labor-intensive industry, and move it to the accurate management. The platform is designed as Com- prehensive Information Platform by our research team. This paper is a general report series of papers the design of Agile Infrastructure for Collaborative Manufacturing and Agile Supply Chain. The key enabled technologies in the platform includes agile infrastructure and its application, business resources planning in manufacturing sys- tem, the design of data access model, optimal strategy for production planning, the dynamic load balance scheduling model, the intrusion detection technology and in- telligent communication strategy. All of them are elaborated in turn. The paper tries to introduce the research of Agile Infrastructure and its key technologies, showing how well they work in some traditional manual industries. Keywords Agile infrastructure · Manufacturing industry · Supply chain · CIP 1. Introduction With the growing trend of economic and information-based globalization, manufac- turing industry has to face the increasingly fierce competition as well as the frequent and unpredictable market fluctuations. An Agile theory thereupon emerges as the requirement of global market fluctuations. The life cycles of products are shortened and the pace of the product renewal is quickened, and the demands of customers are becoming more and more specific and diversified, thereupon, the producing and Jin-long Su () School of Electronics and Information, Tongji University, Shanghai 200092, P.R.China e-mail: su jinlong@yahoo.com.cn Zhong-hui Ouyang TSL School of Business and Information Technology, Quanzhou Normal University, Quanzhou 362000, P.R.China Wan-cheng Ge Chinese-German graduate school, Tongji University, Shanghai 200092, P.R.China 130 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) organizing models transfer from product-oriented to customer-oriented, requirement- oriented and service-oriented, and the aims of enterprises transfer from enterprise profit-driven to market and social profit-driven. The key for enterprise to gain its markets and customers is to improve some factors, such as time, quality, cost, ser- vice and environment. Agile Enterprise and Manufacturing Enterprise Alliance, as the running models of future enterprises, will fully make use of new technologies and coordinated operation which is more agile, compartmentalized, order-driven and dynamic to adapt to the markets. The relationship among Agile Manufacturing Enterprise, Agile Supply Chain and Agile Infrastructure is like the relation between the sharpness of knife and the knife itself, which cannot be divided apart. Agile Infrastructure for Manufacturing System is the platform where the Agile Enterprise, Agile Supply Chain, Agile Manufacturer, Virtual Enterprise are put to good use. The essential condition of Agile Enterprise and Virtual Enterprise is the Agile Infrastructure which is reliable, cross-enterprise, cross- industry and trans-regional. The Agile Infrastructure is established to normalize the managerial practices of enterprise, such as production, sale, policy-making, financial affairs and personnel affairs. The member enterprises can be inserted flexibly, just like the circuit module with standard output and input jacks. In the Agile Infrastructure, member enterprises run business with common rules and establish dynamic Agile En- terprise Alliance, i.e. Virtual Enterprise. The members of Agile Enterprise Alliance negotiate abiding by common rules and accomplish the task of production and sale, which is a game of cooperation. According to the outside market environment and the group intention of inside members, the Virtual Enterprises make identical judgment and macrocosmic layout. In this paper, the authors research into the construction of Agile Infrastructure for Collaborative Manufacturing and Agile Supply Chain and its key enabled technolo- gies. It is supported by the achievements of some projects, such as the “Demon- stration Projects of the Information-based Technology of Manufacturing Industry in Fujian” which is part of the major national supporting project of “the Eleventh Five- year Plan” (i.e. “Information-based Project in Manufacturing Industry”), the “Cre- ative and Information-based Demonstration Platform of Modern Port with Large Lo- gistics” (Project Number: RJZ20063500037) which is a major national project of software and integrated circuit, the “Research on Data Acquisition and Large Infor- mation Platform Construction of Distributed Information System” (JA06014) which is a project at provincial and ministerial level, the “Enterprise Informational Public Service Platform (EIPSP)” (2006H0106) which is a subject in textile and garment industry, “Research on the City Distributional System of GIS Platform” (2005J056). This paperđwhich lays emphasis upon the design of data access model, is one of the series of papers about the design of Agile Infrastructure for Collaborative Man- ufacturing and Agile Supply Chain by our research team. The overall organization of the paper is as follows. After the introduction, in Section 2 we present an exam- ple of Agile Infrastructure in application, the garment information platform, which is a practical platform for garment collaborative manufacturing. The relationships of the main GIP functions and the structure of GIP are also clarified in this section. In section 3, the design of business resources planning model in manufacturing system, Fuzzy Inf. Eng. (2009) 2:129-148 131 are touched upon. Then, the design of data access model is presented on Section 4. In Section 5, the authors elaborate optimal strategy for production planning. Section 6 is about the cooperative Games for Interval Inventory Decision Support in Supply Chain. Then, the dynamic load balance scheduling model, intrusion detection tech- nology, intelligent communication strategy are introduced in Section 7, Section 8 and Section 9, respectively. Finally in Section 10 the authors conclude the paper. 2. Agile Infrastructure and It’s Application Agile Infrastructure for Collaborative Manufacturing and Agile Supply Chain can standardize the output and input information of its member enterprises. As can be seen from Fig.1, our team had done a lot on Agile Infrastructure research, and named it as CIP (Comprehensive Information Platform). CIP stores the manufactur- ing and supply chain and human resource information in CIPns database and helps the enterprises to run their business in an Agile way designed by CIP, i.e. Agile Col- laborative Manufacturing Execution Systems (ACMES), Enterprise Resource Plan- ning (ERP), Customer Relationship Management (CRM), Product Data Management (PDM), Supply Chain Management (SCM). The Comprehensive Information Plat- form tries to affect every aspect of its members in their running mechanisms, and each registered enterprise becomes a standardized module of the Agile Infrastructure. All its members can build up Agile Manufacturing Enterprise, or construct Agile Sup- ply Chain temporarily or permanently. Thus the Enterprises can concentrate on their core competences and they are able to recombine rapidly their interior and exterior capabilities and resources, thereby to respond rapidly to the market opportunity. Fig.1: General architecture of CIP CIP theory has multiplications; one of them is GIP (Garments Information Plat- form) which is designed for Collaborative Manufacturing (CM) in garment industry in China. The garment industry was considered as the labor-intensive industry during the past several decades in China. The rough developing type was considered as its basic developing path. The expansion of information economics in garment industry changes these traditional views. In China, there are thousands of small garment factories which work for the same order form and cooperate to produce a same kind of overcoats or football shoes. Each 132 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) Fig.2: Relationship of the main GIP functions one of them only manufactures very small part of finished production or small num- ber of them. Those factories may not belong to the same company and most of them possibly are located in different places, thus, the information sharing becomes one of the bottle-neck of the garment industry. The information which needs to be commu- nicated includes material management, cost control, manpower arrangement, quality control and manufacture technology sharing. All of them are becoming crucial in the chain of garment industry. The garment industry can optimize the garments management by utilizing Gar- ments Information Platform Enterprise Resource Planning (GIP-ERP), can provide the advantages of manufacturing management by integrating GIP-ERP and GIP-PDM (Product Data Management), can combine the garments’ Computer Aided Design / Manage (GIP-CAD/CAM) with the integrated GIP Distribution Resource Planning (GIP-DRP), can change the traditional sales channel’s process, the garments Cus- tomer Relationship Management (GIP-CRM), can satisfy the relationship between company and customers, which can be seen in Fig.2. Fig.3: The design of agile infrastructure for garment industry Fuzzy Inf. Eng. (2009) 2:129-148 133 The garments Enterprise Resource Planning (ERP) is the centre of GIP, where CRM, CAD/CAM, DRP, PDM system combined together, the useful information of enterprises is perfectly shared. GIP-ERP is a system which can efficiently process information and effectively communicate with other parts in the GIP system. Thus the enterprise resource can be well managed and adjusted, and the efficiency of pro- duction and management of enterprise can be much improved. A structure map of GIP with more details is shown in Fig.3, where we can see the structure of GIP is complicated, including office automation system (OA), manage- ment information system (MIS), knowledge base, electronic commerce (EC), CAD / CAM/ CAE (3C) and product data management (PDM). 3. Business Resources Planning in Manufacturing System The garments Enterprise Resource Planning (ERP) is the centre of GIP, where CRM, CAD/CAM, DRP, PDM system combined together, the useful information of enter- prises is perfectly shared. GIP-ERP is a system which can efficiently process infor- mation and effectively communicate with other parts in the GIP system. Thus the enterprise resource can be well managed and adjusted, and the efficiency of produc- tion and management of enterprise can be much improved. A structure map of GIP with more details is shown in Fig.2, where we can see the structure of GIP is complicated, including office automation system (OA), manage- ment information system (MIS), knowledge base, electronic commerce (EC), CAD / CAM/ CAE (3C) and product data management (PDM). 3.1 Business Resources Planning in Manufacturing System Concerning the agile manufacturing technology based on Network, the author mainly analyzes the monolithic construction, designs characteristics and technological sup- port for the agile manufacturing system, and promotes the agile manufacture based on Network, which integrates the business operations across the traditional boundary of functional department by means of a serial integrated managements centering on resource to improve its sensitivity to market. Furthermore, the author researches into the structural design of network-based agile manufacturing model, production pro- cess and control logic, and applies the design of network-based agile manufacturing system to the garment industry, which achieves satisfactory results and offers new ways of thinking to the agile reform in manufacturing enterprises. To realize the multifunction of Agile Infrastructure for Manufacturing System (AIMS), the platform includes several modules which interact on each other, process the data information each other and share their information resources. 3.2 The Design of Distribution Resource Planning Subsystem Distribution Resource Planning Subsystem is an order-driven system, which sup- ports order by internet, Electronic Data Interchange and other traditional format of order. The subsystem has close relation with Customer Relationship Management (CRM) because both of them use the same order management function, customer information databases, customer feedback on product and service, and sales perfor- mance of staff. The subsystem also has relationship with product data management 134 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) Fig.4: BRP of EIPSP (PDM), Computer-Aided Design/Computer-Aided Manufacturing/ Computer Aided Engineering (CAD/CAM/CAE), which conduce to the service of pattern selection and Bill of Material (BOM) sheet creation. The flow chart begins with Operation of Quoted Price → Selection of Pattern → The Number of Measures of garments → Auditing → Reverse Auditing → Change the Quotation to Order form → Auditing → Reverse Auditing → Tracking Product Information of Order Form, Consignment by Order Form, Requisition sheet Creating. 3.3 The Design of Material Purchase Management Subsystem Integrated Infrastructure for Agile Enterprise (IIAE) helps the manufacturer and sup- plier share the data resource of product to realize the production capacity and the demand of market. The supplier can receive the information as soon as the manufac- turer set down a requisition sheet of material through e-mail, which greatly improves the efficiency of supply chain. The system also supports the warehouse information shared between manufacturer and supplier. As can be seen from Fig.6, flow chart of material purchase management subsystem begins in material purchase plan creation, which is always generated automatically after the complement of material requisitions in product module. The flow chart is Material Purchase→ Creat Purchase Plan→ Purchase Form Creat→ Requision sheet of Material & Create Billhead→ Purchase Account Settled. 3.4 The Design of Production Control Subsystem The Integrated Infrastructure for Agile Enterprise supports Master Production Sched- ule (MPS), Material Require Planning (MRP) and Capacity Requirements Planning Fuzzy Inf. Eng. (2009) 2:129-148 135 Fig.5: Flow chart of distribution resource planning subsystem Fig.6: Flow chart of material purchase management subsystem (CRP). The subsystem has functions of working schedule change, working period’s analysis and payroll costs statistics. The Production Planning Subsystem will help the enterprises to create an efficient product plan, precise shop management to control the raw materials and the quality of product, rigorous cost analysis to control the cost and sale price of product. All 136 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) contributes to quicken the pace of the enterprise operation, product flow optimization and enhances competitive power of agile enterprise. Fig.7: Flow chart of material requisition planning in production control As can be seen from Fig.7, material purchase operation and material used schedule operation in material management are needed before product schedule operation. And the manufacture process will not start until material purchase process is fulfilled. Fig.8: Flow chart of production planning subsystem As can be seen from Fig8, the system can help administer perform the whole pur- chase process. The procedure is production schedule → order form → the type of schedule sheet → the type number → workshop, and after choosing of those items, the production schedule is created. Then you can save the production schedule and define the number of the production schedule sheet, which set the quantity of product related to the order form. Both the product schedule management flow chart and its practical perform pro- cess indicate that the every operation of the flow chart are strictly controlled by the relative step before or after them. Fuzzy Inf. Eng. (2009) 2:129-148 137 Fig.9: Flow chart of production process As can be seen from Fig.9, the flow chart of Production Process is working pro- cedure of cutter and teams → note scanning → Cutters & Sartorius management → production order→ product material control→ production order auditing→ produc- tion order sheet printing → production order settled → production order settled & working procedure allocation → production note scanning → manufacturing sched- ule/laborage statistic/working procedure statistic. Fig.10: Note of cutter in production control Fig.11: Note of sartorius in production control Fig.10 and Fig.11 are the note of cutter in production control and the note of Sar- torius in production control, respectively. 4. Design of Data Access Model Concerning the data access strategy, we promote an optimized programming and al- gorithm based on the Extensible Markup Language (XML) and Model-View-Controller (MVC) model. As we know, data output and input is the most normal activity of Ag- ile Infrastructure. The data is always transferred to viewer and changed by customs at terminals, rather than stored back to the database. The data stream between database 138 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) and terminals is the main stream of the platform. Model-View-Controller model is design to separate the View module and logical module, which makes program more maintainable, extendable, transplantable and reusable. Trygve Reenskaug designed MVC model for Smalltalk platform in the late 1970s, which turns out to be a very useful and mature theory after thirty years’ evolution. Fig.12: MVC model2 The MVC model is always designed by J2EE, and there still not perform very well in the View-Controller separating, and there still hard to realize the MVC idea by J2EE program. The research team redefines the MVC Model2 based on XML and uses XML and Java to build an MVC model in data access strategy of Agile Infrastructure. In the research on data access of Agile Infrastructure, the author analyzes the main ways of implementation in the three parts of Model-View-Controller, and redesigns the program of MVC Model2 based on XML. In the programmer of MVC model, the whole process of data access of MVC model2 based on XML is described as follows. (see Fig.12) Firstly, the terminal browser sends signal of request about Uniform Resource Lo- cator (URL) address, and the front controller transmits the request through URL after the request of Hypertext Transfer Protocol (HTTP) arrives at the controller. Secondly, Path, the parameter of URL, accesses the page layout of system function and orientated the definition function. Together with the XML document of page logic, it is input to the XML interpreter. Thirdly, Funname, the parameter of URL, makes use of the application logic of system and accesses the system function Class through Reflection. The Java Class of systemic function logic acquires data by database accessing and takes back the logic running result. Fourthly, the XML interpreter forms HTML document which is identifiable to client, and returns to the client browser by HTTP agreement. The advantage of XML-based design of MVC model 2 is that system categorizes the functional page-layout and adopts different configuration rules for different kinds. Developers need only to make functional logic of page-layout in XML format, while the XML Parser in bottom layer of system forms the HTML document which is iden- tifiable to client by analyzing the XML document and transmits it to terminal. The aim of completely separating the layout technology from system logic is realized by the way of setting the View module with XML configuration technology, which Fuzzy Inf. Eng. (2009) 2:129-148 139 reduces the difficulties of system maintenance and secondary development. Other advantage is that the interface of system in infrastructure is changed by altering XML Parser rather than XML document. 5. Optimal Strategy for Production Planning ⎧ ⎫ ⎛ ⎞ ⎪ ⎪ ⎪ ⎜ a x (k)+ ⎟ ⎪ ⎪ ⎜ ⎟ ⎪ ⎪ ⎜ ⎟ ⎪ ⎜ ⎟ ⎪ ⎪ ⎪ ⎜ ⎟ ⎪ T + ⎪ ⎜ ⎟ ⎪ ⎜ ⎟ ⎪ b [T u (k)−ρ (k)] ⎪ ⎜ i i i ⎟ ⎪ ⎪ ⎪ ⎜ ⎟ ⎪ M N ⎪ ⎜ ⎟ ⎪ ⎜ ⎟ ⎪ ⎪ T + ⎪ ⎜ ⎟ ⎪ ⎪ ⎜ ⎟ ⎪ +b [ρ (k)− T u (k)] ⎜ i i i ⎟ ⎪ ⎪ ⎜ i ⎟ ⎪ ⎪ ⎨ ⎜ ⎟ ⎬ i=1 k=1 ⎜ ⎟ ⎜ ⎟ T + J = min . (1) ⎪ ⎜ ⎟ ⎪ +c [˜y (k)− z ˜ (k)] ⎪ ⎜ ⎟ ⎪ i i ⎜ i ⎟ ⎪ ⎪ ⎜ ⎟ ⎪ ⎪ ⎪ ⎝ ⎠ ⎪ ⎪ ⎪ T + ⎪ ⎪ +c ¯ [˜z (k)− y ˜ (k)] ⎪ ⎪ i i ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ + a x (N + 1) ⎩ ⎭ i=1 Optimal strategy for production planning of upriver-product-line can be realized by an algorithm based on Hopfield neural network, and optimality criterion is formula 1. The module is based on the demand and output of two-level product line, i.e. upriver-product-line and downriver-product-line. The production planning module of upriver-product-line based on limited cost of input buffer of downriver-product-line can be described as Equation (1). M represents the number of upriver-product-line, while N represents the number of the product-cycle of upriver-product-line in arranged. u (k) represents the num- ber of the workpiece output of upriver-product-line i in k product-cycle, which are n = n + m dimensional column vectors. n is the number of type of finished product gi i i i of No.i product-line in planning periods, while m is the number of type of subsidiaries of No.i product-line in planning periods. y ˜ (k) represents the total number of finished product of No.i product-line from No.i Periods to No.k Periods, which are n di- gi mensional column vectors. z ˜ (k) represents total demands of upriver-product-line for finished product and subsidiaries of downriver-product-line No.i from No.i Periods to No.k Periods. ρ (k) represents machining time of working center of upriver-product- line No.i during No.k Periods, which are f dimensional column vectors. f represents i i the number of workers in No.i product line. a represents the cost-coefficient related to finished product in No.i product-line, which are n dimensional column vectors. b gi i represents cost-coefficient related to call-back pay in upriver-product-line No.i, which are f dimensional column vectors. b represents cost-coefficient related to leave un- i i used equipments in upriver-product-line No.i, which are f dimensional column vec- tors. c represents cost-coefficient related to storage cost due to over demands of fin- ished product and subsidiaries output in upriver-product-line No.i, which are n + m i i dimensional column vectors. c ¯ represents cost-coefficient related to storage cost due to unmet demands of finished product and subsidiaries output in upriver-product-line No.i, which are n +m dimensional column vectors. T represents machining hours of i i i operator by machining n kind of workpiece during Period k in upriver-product-line gi No.i, which is a f ×n dimensional matrix. The algorithm can be seeing in Algorithm i gi 1. 140 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) Algorithm 1 Production Planning Based on MAS Step1. let minCost=maxnumber; parameter number={}; Step2. for(inti = 1; i <= M; i++){ For(intk = 1; k <= N; k++){ Costpartone = a ∗ x (k) +b ∗ max[T u (k)−ρ (k)] i i i +b ∗ max[ρ (k)− T u (k)]}} i i i +c ∗ max[y ˜ (k)− z ˜ (k)] i i +c ¯ ∗ max[z ˜ (k)− y ˜ (k)]; i i Step3. for(int i = 1; i <= M; i++){ Costparttwo = a ∗ x ∗ (N + 1);} Costx=Costpartone+Costparttwo Step4 Get new Costx by change the plan by Hopfield Neuron network, then go to Step1, until no new extreme value in five circles, and let R to be the number of Costx ; Step5. for (int x = 1; x <= R; x++){ if minCost>Costx{ parameter number=x; minCost=costx;}; Step6. Output(minCost, correlation parameter) 6. Cooperative Games for Interval Inventory Decision Support in Supply Chain We use to do research on the efficiency enhancement of the supply chain as a whole system, and want to find a set of optimal strategies for all the participants and calcu- late the sum of profit of participators. But we forget that the highest profit of the sum of all players is always costed by the lower profit of some members of supply chain. So, if one of participants finds out that he has to sacrifice his profit for the others, he may quit and join another supply chain to search an equitable business status. To clarify this problem, we can see through an example of two-stage Supply Chain. In this cooperative game, each side may change its inventory decision without the permission of the opposite side. Then if there are any changes that will lead to more profits, the player will choose to change to satisfy their profit-push intention. A module of Two-Echelon Supply Chain has been given based on the research of G. P. Cachon and P. H. Zipkin [3], where the relationship of Supplier and Demander can be described as a Cooperation Game. According to Nash’s Equilibrium Discrim- inance theories, the Games must have an equilibrium point and the final choice of Supplier and Demander must constringe to the equilibrium point. Our research group design an algorithm based on a Two-Agent-Module. We suppose participants of the negotiation have Perfect Information and Complete Information, that is, the participators have the correlative information clearly and cor- rectly. The equilibrium searching process is a multi-agent Game playing process in the supply chain. By programming, we use agent to simulate the negotiation pro- cess in cooperative game. According to Nash Equilibrium Theory, there must be an equilibrium point in the negotiation process, and there will be no more profit got by Fuzzy Inf. Eng. (2009) 2:129-148 141 any change of the player. Equilibrium point is the best result under the agreement of the each side of the participants. According to inventory module in a Two-Stage Supply Chain of G. P. Cachon and P. H. Zipkin, the interval inventory decision of the S L dealer is within the [s , s ], and the interval of inventory decision of the supplier is r r within the [0, S ]. Here, S represents a lager Constant, which is satisfied enough to the franchiser. Fig.13: Negotiation process of cooperative games [4] The theorem about the Equilibrium point in the cooperative games had been proved by John F. Nash, that is, the reasonable outcomes of the two-person games u = (u , u ) 1 2 should meet the requirement that (u , u ) ∈ B, u ≥ n , u ≥ n , and let the product of 1 2 1 1 2 2 (u − n )&(u − n ) to be maximum. 1 1 2 2 As can be seen from figure 13, B is feasible solution set, N is the conflicting point, the X-axis represents the profit of enterprise II and the Y-axis represents the profit of enterprise I in the plane Cartesian coordinate system. m is the original decision of enterprise II, while n represents the original decision of enterprise I. Specifically, the region B means the feasible solution set where the cooperation game may strike a bargain in the negotiation procedure. During the negotiation procedure, the partic- ipants choose their decisions during the region B by mixed strategies in a strategic equivalence status; a new decision may suddenly cause the decision change of the other side. Finally, the numerical results of the cooperative game constringe to the Nash’s equilibrium point in through an argy-bargy procedure. Unless the plays reach the equilibrium point, the both sides will not satisfy with the result and will stop to change their inventory decision. A Multi-Agent System (MAS) algorithm will be helpful to the both sides of the player in the cooperative game, because it will help the partic- ipants to reach the equilibrium point directly and quickly without wasting any time in the negotiation procedure. The numerical results of the MAS algorithm are on the assumption that all the participants have equality of bargaining skill and information. That is, the value of game of the algorithm is in compatibility with “reasonable out- comes”. 142 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) Algorithm 2 Cooperative Games in Supply Chain Step1. Let n=0; S int LAST=S LAST=maxnumber; r s /* initialization*/ Step2. /* initializers*/ Let S = S initia number; s s S temporal number=S initia number; s s S = S initia number; r r S temporal number=S initia number; r r B (S , S ) = B (S )F (S ) S r s s r s +∞ Ls + B (S + S − x) f (x)dx s r s /* OOS(Out Of Stock) Cost Expectation of supply Agent */ Π (S , S ) = h F (x)dx+ B (S , S ) s r s s S r s /*Cost Expectation of supply Agent*/ Step3. /*demand Agent*/ Let S new=0; min (S , S ) =maxnumber; s r s (S , S ) = 0; s r s S = S temporal number; s s S = S temporal number; r r S L For (int S = S ; S ≤ S ; S ++){ r r r r r L L r r C (y) = αβ(μ − y)+ (h +αβ ) F (x)dx r r Π (S , S ) = C (S )F (S ) r r s r r s +∞ + C (S + S − x) f (x)dx r r s /*Cost Expectation of demand Agent*/ If min (S , S ) ≥ (S , S ){ r r s r r s max (S , S ) = (S , S ); S new=S ;}} r r s r r s r r S temporal number=S new; r r Step4. /*supply Agent*/ Let S new=0; min (S , S )= maxnumber; s s r s Let (S , S ) = 0; S = S temporal number; s r s s s S = S temporal number; r r for (int S = 1; S <= S ; S ++) s s s B (S , S ) = B (S )F (S ) S r s s r s +∞ + B (S + S − x) f (x)dx s r s /* OOS(Out Of Stock) Cost Expectation of supply Agent */ Π (S , S ) = h F (x)dx+ B (S , S ) s r s s S r s /*Cost Expectation of supply Agent*/ If min (S , S ) ≥ (S , S ){ s r s s r s min (S , S ) = (S , S ); S new=S ;}} s r s s r s s s S temporal number=S new; s s Step5. /*Nash’s Equilibrium Discriminance Agent*/ if (S LAST==S temporal number) s s and(S LAST==S temporal number) r r Fuzzy Inf. Eng. (2009) 2:129-148 143 {n++;}else n=0; S LAST=s temporal number; s s S LAST=S temporal number; r r If (n < 10){go to step3;} Step6. Output(S NASH=S LAST, S NASH=S LAST) s s r r The Module is composed of Supply Agent and Demand Agent, both of which have their own decisions based on the choice of the opposite side. The Algorithm constringes to the equilibrium point when any change of their decision will not bring them better profit respectively. The algorithm can be seen in Algorithm 2. 7. The Dynamic Load Balance Scheduling Model With the expansion of our clients, load balance control has become a necessary con- sideration for the Garments Information Platform infrastructure construction. The goal of our work is to achieve good load balance of server group and offer high- quality service for the registered users as well as guests. For GIP system heavy loads are mostly caused by registered users, since only registered users can ask for data procession requests which need longer reacting time, while guests can only access some introducing information of platform. Current load balancing solutions, which fall into two categories: probe-based and distribution point systems are capable of improving the throughput of network service clusters. However, both two strategies suffer from fundamental structural limitations. After considering the existing load balancing method, we design a composition for the particularity of the GIP system, with partitioning services for clients across user cookies. For registered users, who use the platform regularly with larger input and output data stream, the load balance controller system are designed as an distribution point system to offer faster and more stable service. On the other hand, for non- registered users, probe-based system is applied, which will allocate to each server in turn. Because non-registered users often make similar requests with small data streams, easy and stable algorithm can achieve sound performances. 8. Intrusion Detection Technology For the increasing onslaught of computer attacks, which cause damages ranging from mere violation of confidentiality and issues of privacy up to actual financial losses, the information security infrastructure has become an indispensable part on Compre- hensive Information Platform design. One of the most important matters that we mainly concerned is the intrusion detec- tion technology, which is a new approach for providing a sense of security in existing computers and data, as can be seen from Fig.14. The approach is designed to identify, preferably in real time, the unauthorized use, misuse, and abuse of computer systems by both internal and external system penetrators. An effective intrusion detection algorithm will form a defense line in the whole system for information security. Nev- ertheless, every intrusion detection approach has their drawbacks. Signature-based 144 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) intrusion detection can identify the known intrusions but fail to detect the novel in- trusions, while anomaly-based intrusion detection has the potential to detect all intru- sions but unfortunately is limited by its unaccepted false alarm rate in most cases. Fig.14: The Intrusion Detection system (IDS) model of Common Intrusion Detection Frame ( CIDF) 9. Intelligent Communication Strategy Congestion takes place when too many users are using the same channel at the same time or too many data pass a channel at a time, which will both lead to data loss. Con- gestion control in telecommunication has been increasingly researched, and a series of papers deal with congestion control based on output feedback linearization theory and adaptive control techniques. As part of an overall strategy to relieve data con- gestion in GPRS telecommunication, we have been involved in developing methods for monitoring the extent of data congestion by using sequences of fuzzy neuron net- works, compressing and decompressing the transmitted data, predicting congestion and controlling the compression rate. Fig.15 shows the overall congestion control process in GPRS communication. First, a CFNN has been used to fuse two series of data which are derived separately from two different sensors. Secondly the fusion-image-series are sent to a wavelet- based FNN compression system, and the compression algorithm then dynamically compresses the image, compression rate being related to the congestion control and prediction system. The congestion monitoring system which includes the FNN-based congestion control and prediction is the third step of the whole process. The special compression algorithm applied in congestion control is a FNN based compression algorithm, whose compression rate can be easily and timely controlled, that means, we discard some of the less important information if necessary, in order to achieve very high compression rate when GPRS transmission ability is low. Thus, we can maintain a smooth connection and avoid the data traffic congestion at the cost of image quality. The image compression algorithm showed in congestion control has two characteristics: 1. controllable compression rate Fuzzy Inf. Eng. (2009) 2:129-148 145 Fig.15: Data procession strategy in the platform 2. better image quality under the same compression rate than other compression al- gorithms The first characteristic is achieved by wavelet transform for splitting, and the second one is done by FNN (fuzzy core neuron network) for vector-classification- compression algorithm. Data compression is one of the most important applications of wavelet transform [10]. Wavelet transform can be generated from digital filter banks. Wavelet trans- form hierarchically decomposes an input image into a series of successively lower resolution images and their associated detail images. Discrete-wavelet-translation of digital images is implemented by a set of filters, which are convolved with the image in rows and columns. An image is convolved with low-pass and high-pass filters and the odd samples of the filtered outputs are discarded resulting in down sampling the image by a factor of 2. The wavelet de- composition results in an approximation image and three detail images in horizontal, vertical, and diagonal directions. Decomposition into L levels of an original image results in a down sampled image of resolution 2L with respect to the original image as well as detail images. Images are analyzed by wavelet packets for splitting both the lower and the higher bands into several sub-images at a time. A set of wavelet packets is gained. The following wavelet packet basis function {w }(n = 0, 1,···∞) is generated from a given function w . w (l) = 2 h(k)w (2l− k), (2) 2n n w (l) = 2 g(k)w (2l− k). (3) 2n+1 n As can be seen from Equation (2) and (3), where the function w (l) can be identi- fied with the scaling functionφ, and w (l) with the mother waveletψ, h(k) and g(k)are the coefficients of the low-pass and high-pass filters, respectively. Two 1-D wavelet 146 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) packet basis functions are used to obtain the 2-D wavelet basis function through the tensor product along the horizontal and vertical directions. (see Fig.16) Fig.16: Splitting high and low frequency image by wavelet transform In this paper, we use Mallat algorithm as the wavelet transform algorithm. The reconstruction expression of Mallat can be seen as the following: 1 2 C (n, m) = C ( j, l)+ d ( j, l)h g + d ( j, l)g h K−1 ⎢ K n−2 j m−2l n−2 j m−2l ⎣ k k k,l∈Z k,l∈Z k,l∈Z ⎤ (4) + d ( j, l)g g . n−2 j m−2l⎥ k,l∈Z 1 2 3 Fig.16 shows that original image splits into a series of sub-images. D , D , D in j j j Fig.16 are represented as sub-images with high frequency characteristics in horizon- tal, vertical and diagonal directions respectively. The more times we use wavelet transform, the more sub-images we get. The more sub-images we acquire, the less image information we lose. But if we want to get more information by decomposi- tion we need to compress more useful information derived from the original image. For this reason, we hope the compression result can be controlled; therefore the con- gestion control algorithm may avoid transferring too much data while the wireless communications network being jammed. And if data traffic does not seem to happen, we may hope to send more information through the channel to transfer as high quality images as possible. The combination algorithm, as can be seen from Fig.17, based on FNN and wavelet transform, is right for GPRS communications. It can send smallest image data continuously, with sound image quality; it can control the compression rate timely and appropriately, while taking efforts to avoid data congestion. To compare our compression algorithm with other popular & traditional algo- rithms, another two typical compression algorithms, WENRAR transform and fractal image compression in DCT domain based on adaptive classified weighted ISM [12], are introduced and adopted to test the same pictures whose average performances are shown in table I. As can be seen from Table I, our image compression algorithm, Fuzzy Inf. Eng. (2009) 2:129-148 147 Fig.17: Image compression and decompression process which is based on FNN and wavelet transform, has shown much better performance than others in our experiments. Table 1: Comparison of Some Typical Compression Algorithm WENRAR Fractal Image Reconstruction Splitting Image By Translation Compression Image By Wavelet Mallat Wavelet in DCT Translation Translation then Domain Based on Compression Adaptive Classified Through FNN Weighted ISM [12] Vector-Classification Origin Data NO NO YES YES Lost Compression 1.57 10 30 30 Rate S/N(Single/ – – 6 12 Noise rate) 9. Conclusions In this paper, Comprehensive Information Platform designed for collaborative man- ufacturing was presented with an application case in garment industry. Specifically, the relation of GIP functions as well as the structure of CIP in the garment industry application was introduced. In addition, technical details about business resources planning model in manufacturing system, especially, the relation of subsystems are expressed clearly by figures, and the functions of subsystems were discussed in the following sections. The key enabled technologies in the platform includes agile in- 148 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) frastructure and it’s application, business resources planning in manufacturing sys- tem, the design of data access model, optimal strategy for production planning, the dynamic load balance scheduling model, the intrusion detection technology and intel- ligent communication strategy. All of them are elaborated in turn. More elaborating performance report will come up after long period of observational and practical use. All technical details touched upon run in application, and the main improvement in its performance can only come with application. Some parts of the Agile Infrastructure are almost completed, i.e. GIP, which seems to work well and more details can be seen on the internet on http://www.istqz.com. References 1. Su JL, Chen YM, Ouyang Z H (2006) GPRS congestion control algorithm based on fuzzy kernel neural networks. JINAN: the 6th International Conference on Intelligent Systems Design and Appli- cations (ISDA’06), IEEE Computer Society: 954-959 2. Su JL, Chen YM, Ouyang Z H (2006) GPRS communication system designed for high congestion risk circumstance. SINGAPORE: 9th International Conference on Control, Automation, Robotics and Vision (ICARCV’2006), IEEE Control Systems Society: 1962-1967 3. Su JL, Chen YM, Ouyang Z H (2007) An image compression algorithm with controllable com- pression rate, Advances in Soft Computing. Germany: 2007 International Conference on Fuzzy Information and Engineering (ICFIE’07). Springer-Verlag: 83-88 4. Su JL, Ouyang Z H, Chen YM (2007) Research on comprehensive information platform designed for collaborative manufacturing. Tibet: The Sixth International Conference on Information and Manage- ment Sciences (IMS2007) 5. Su JL, Ouyang Z H, Chen YM (2007) Design of agile infrastructure for manufacturing system with FNN based web-enabled technology solutions. BRAZIL: the 7th International Conference on Intelli- gent Systems Design and Applications (ISDA’07), IEEE Computer Society 6. Su JL, Ouyang Z H, Chen, YM (2008) The design of agile infrastructure for manufacturing system in garment industry, Chongqing: The 7th World Congress on Intelligent Control and Automation (WCICA’08), IEEE Robotics and Automation Society & IEEE Control System Society & National Natural Science Foundation 7. Su JL, Ouyang Z H, Chen YM (2008) Research on agile infrastructure for collaborative manufac- turing and agile supply chain, SINGAPORE: 2008 IEEE International Conferences on Cybernetics & Intelligent Systems (CIS) and Robotics, Automation & Mechatronics (RAM) (CIS-RAM 2008), IEEE Robotics & Automation (R&A) Singapore Chapter & IEEE Systems, Mana & Cybernetics (SMC) Singapore Chapter 8. Su JL, Chen YM (2008) Application of information systems in collaborative manufacturing, the 12th World Multiconference on Systemics, Cybernetics and Informatics (WMSCI 2008). Orlando, USA 9. XIE RS, SUN F, HAO YL (2002) Multi-wavelet transform and its application in signal filtering. ACTA Electronic Sinica 30(3):419-421 10. TANG Y, MO YL (2000) Image coding of tree-structured using 2D wavelet transform. Journal of Shanghai University 6(1):71-74 11. Mars Fan P (1997) Access flow control scheme for ATM networks using neural-network-traffic pre- diction. IEEE rocComm 144(5):295-300 12. Yi ZK, Zhu WL, Gu DR (1997) Image progressive transmission and lossless coding using fractal image coding. Journal of UEST of China 26(5):473-476 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Fuzzy Information and Engineering Taylor & Francis

Agile Infrastructure Design for Garment Industry

Agile Infrastructure Design for Garment Industry

Abstract

AbstractThe Information Economics and its platform can input the vigor into manufacturing industry, i.e., garment industry, help to increase its ability for good return, promote the level of technology and management of Garments from a labor-intensive industry, and move it to the accurate management. The platform is designed as Comprehensive Information Platform by our research team. This paper is a general report series of papers the design of Agile Infrastructure for Collaborative...
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10.1007/s12543-009-0011-3
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Fuzzy Inf. Eng. (2009)2:129-148 DOI 10.1007/s12543-009-0011-3 ORIGINAL ARTICLE Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge Received: 27 December 2008/ Revised: 3 April 2009/ Accepted: 10 May 2009/ © Springer and Fuzzy Information and Engineering Branch of the Operations Research Society of China Abstract The Information Economics and its platform can input the vigor into man- ufacturing industry, i.e., garment industry, help to increase its ability for good return, promote the level of technology and management of Garments from a labor-intensive industry, and move it to the accurate management. The platform is designed as Com- prehensive Information Platform by our research team. This paper is a general report series of papers the design of Agile Infrastructure for Collaborative Manufacturing and Agile Supply Chain. The key enabled technologies in the platform includes agile infrastructure and its application, business resources planning in manufacturing sys- tem, the design of data access model, optimal strategy for production planning, the dynamic load balance scheduling model, the intrusion detection technology and in- telligent communication strategy. All of them are elaborated in turn. The paper tries to introduce the research of Agile Infrastructure and its key technologies, showing how well they work in some traditional manual industries. Keywords Agile infrastructure · Manufacturing industry · Supply chain · CIP 1. Introduction With the growing trend of economic and information-based globalization, manufac- turing industry has to face the increasingly fierce competition as well as the frequent and unpredictable market fluctuations. An Agile theory thereupon emerges as the requirement of global market fluctuations. The life cycles of products are shortened and the pace of the product renewal is quickened, and the demands of customers are becoming more and more specific and diversified, thereupon, the producing and Jin-long Su () School of Electronics and Information, Tongji University, Shanghai 200092, P.R.China e-mail: su jinlong@yahoo.com.cn Zhong-hui Ouyang TSL School of Business and Information Technology, Quanzhou Normal University, Quanzhou 362000, P.R.China Wan-cheng Ge Chinese-German graduate school, Tongji University, Shanghai 200092, P.R.China 130 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) organizing models transfer from product-oriented to customer-oriented, requirement- oriented and service-oriented, and the aims of enterprises transfer from enterprise profit-driven to market and social profit-driven. The key for enterprise to gain its markets and customers is to improve some factors, such as time, quality, cost, ser- vice and environment. Agile Enterprise and Manufacturing Enterprise Alliance, as the running models of future enterprises, will fully make use of new technologies and coordinated operation which is more agile, compartmentalized, order-driven and dynamic to adapt to the markets. The relationship among Agile Manufacturing Enterprise, Agile Supply Chain and Agile Infrastructure is like the relation between the sharpness of knife and the knife itself, which cannot be divided apart. Agile Infrastructure for Manufacturing System is the platform where the Agile Enterprise, Agile Supply Chain, Agile Manufacturer, Virtual Enterprise are put to good use. The essential condition of Agile Enterprise and Virtual Enterprise is the Agile Infrastructure which is reliable, cross-enterprise, cross- industry and trans-regional. The Agile Infrastructure is established to normalize the managerial practices of enterprise, such as production, sale, policy-making, financial affairs and personnel affairs. The member enterprises can be inserted flexibly, just like the circuit module with standard output and input jacks. In the Agile Infrastructure, member enterprises run business with common rules and establish dynamic Agile En- terprise Alliance, i.e. Virtual Enterprise. The members of Agile Enterprise Alliance negotiate abiding by common rules and accomplish the task of production and sale, which is a game of cooperation. According to the outside market environment and the group intention of inside members, the Virtual Enterprises make identical judgment and macrocosmic layout. In this paper, the authors research into the construction of Agile Infrastructure for Collaborative Manufacturing and Agile Supply Chain and its key enabled technolo- gies. It is supported by the achievements of some projects, such as the “Demon- stration Projects of the Information-based Technology of Manufacturing Industry in Fujian” which is part of the major national supporting project of “the Eleventh Five- year Plan” (i.e. “Information-based Project in Manufacturing Industry”), the “Cre- ative and Information-based Demonstration Platform of Modern Port with Large Lo- gistics” (Project Number: RJZ20063500037) which is a major national project of software and integrated circuit, the “Research on Data Acquisition and Large Infor- mation Platform Construction of Distributed Information System” (JA06014) which is a project at provincial and ministerial level, the “Enterprise Informational Public Service Platform (EIPSP)” (2006H0106) which is a subject in textile and garment industry, “Research on the City Distributional System of GIS Platform” (2005J056). This paperđwhich lays emphasis upon the design of data access model, is one of the series of papers about the design of Agile Infrastructure for Collaborative Man- ufacturing and Agile Supply Chain by our research team. The overall organization of the paper is as follows. After the introduction, in Section 2 we present an exam- ple of Agile Infrastructure in application, the garment information platform, which is a practical platform for garment collaborative manufacturing. The relationships of the main GIP functions and the structure of GIP are also clarified in this section. In section 3, the design of business resources planning model in manufacturing system, Fuzzy Inf. Eng. (2009) 2:129-148 131 are touched upon. Then, the design of data access model is presented on Section 4. In Section 5, the authors elaborate optimal strategy for production planning. Section 6 is about the cooperative Games for Interval Inventory Decision Support in Supply Chain. Then, the dynamic load balance scheduling model, intrusion detection tech- nology, intelligent communication strategy are introduced in Section 7, Section 8 and Section 9, respectively. Finally in Section 10 the authors conclude the paper. 2. Agile Infrastructure and It’s Application Agile Infrastructure for Collaborative Manufacturing and Agile Supply Chain can standardize the output and input information of its member enterprises. As can be seen from Fig.1, our team had done a lot on Agile Infrastructure research, and named it as CIP (Comprehensive Information Platform). CIP stores the manufactur- ing and supply chain and human resource information in CIPns database and helps the enterprises to run their business in an Agile way designed by CIP, i.e. Agile Col- laborative Manufacturing Execution Systems (ACMES), Enterprise Resource Plan- ning (ERP), Customer Relationship Management (CRM), Product Data Management (PDM), Supply Chain Management (SCM). The Comprehensive Information Plat- form tries to affect every aspect of its members in their running mechanisms, and each registered enterprise becomes a standardized module of the Agile Infrastructure. All its members can build up Agile Manufacturing Enterprise, or construct Agile Sup- ply Chain temporarily or permanently. Thus the Enterprises can concentrate on their core competences and they are able to recombine rapidly their interior and exterior capabilities and resources, thereby to respond rapidly to the market opportunity. Fig.1: General architecture of CIP CIP theory has multiplications; one of them is GIP (Garments Information Plat- form) which is designed for Collaborative Manufacturing (CM) in garment industry in China. The garment industry was considered as the labor-intensive industry during the past several decades in China. The rough developing type was considered as its basic developing path. The expansion of information economics in garment industry changes these traditional views. In China, there are thousands of small garment factories which work for the same order form and cooperate to produce a same kind of overcoats or football shoes. Each 132 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) Fig.2: Relationship of the main GIP functions one of them only manufactures very small part of finished production or small num- ber of them. Those factories may not belong to the same company and most of them possibly are located in different places, thus, the information sharing becomes one of the bottle-neck of the garment industry. The information which needs to be commu- nicated includes material management, cost control, manpower arrangement, quality control and manufacture technology sharing. All of them are becoming crucial in the chain of garment industry. The garment industry can optimize the garments management by utilizing Gar- ments Information Platform Enterprise Resource Planning (GIP-ERP), can provide the advantages of manufacturing management by integrating GIP-ERP and GIP-PDM (Product Data Management), can combine the garments’ Computer Aided Design / Manage (GIP-CAD/CAM) with the integrated GIP Distribution Resource Planning (GIP-DRP), can change the traditional sales channel’s process, the garments Cus- tomer Relationship Management (GIP-CRM), can satisfy the relationship between company and customers, which can be seen in Fig.2. Fig.3: The design of agile infrastructure for garment industry Fuzzy Inf. Eng. (2009) 2:129-148 133 The garments Enterprise Resource Planning (ERP) is the centre of GIP, where CRM, CAD/CAM, DRP, PDM system combined together, the useful information of enterprises is perfectly shared. GIP-ERP is a system which can efficiently process information and effectively communicate with other parts in the GIP system. Thus the enterprise resource can be well managed and adjusted, and the efficiency of pro- duction and management of enterprise can be much improved. A structure map of GIP with more details is shown in Fig.3, where we can see the structure of GIP is complicated, including office automation system (OA), manage- ment information system (MIS), knowledge base, electronic commerce (EC), CAD / CAM/ CAE (3C) and product data management (PDM). 3. Business Resources Planning in Manufacturing System The garments Enterprise Resource Planning (ERP) is the centre of GIP, where CRM, CAD/CAM, DRP, PDM system combined together, the useful information of enter- prises is perfectly shared. GIP-ERP is a system which can efficiently process infor- mation and effectively communicate with other parts in the GIP system. Thus the enterprise resource can be well managed and adjusted, and the efficiency of produc- tion and management of enterprise can be much improved. A structure map of GIP with more details is shown in Fig.2, where we can see the structure of GIP is complicated, including office automation system (OA), manage- ment information system (MIS), knowledge base, electronic commerce (EC), CAD / CAM/ CAE (3C) and product data management (PDM). 3.1 Business Resources Planning in Manufacturing System Concerning the agile manufacturing technology based on Network, the author mainly analyzes the monolithic construction, designs characteristics and technological sup- port for the agile manufacturing system, and promotes the agile manufacture based on Network, which integrates the business operations across the traditional boundary of functional department by means of a serial integrated managements centering on resource to improve its sensitivity to market. Furthermore, the author researches into the structural design of network-based agile manufacturing model, production pro- cess and control logic, and applies the design of network-based agile manufacturing system to the garment industry, which achieves satisfactory results and offers new ways of thinking to the agile reform in manufacturing enterprises. To realize the multifunction of Agile Infrastructure for Manufacturing System (AIMS), the platform includes several modules which interact on each other, process the data information each other and share their information resources. 3.2 The Design of Distribution Resource Planning Subsystem Distribution Resource Planning Subsystem is an order-driven system, which sup- ports order by internet, Electronic Data Interchange and other traditional format of order. The subsystem has close relation with Customer Relationship Management (CRM) because both of them use the same order management function, customer information databases, customer feedback on product and service, and sales perfor- mance of staff. The subsystem also has relationship with product data management 134 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) Fig.4: BRP of EIPSP (PDM), Computer-Aided Design/Computer-Aided Manufacturing/ Computer Aided Engineering (CAD/CAM/CAE), which conduce to the service of pattern selection and Bill of Material (BOM) sheet creation. The flow chart begins with Operation of Quoted Price → Selection of Pattern → The Number of Measures of garments → Auditing → Reverse Auditing → Change the Quotation to Order form → Auditing → Reverse Auditing → Tracking Product Information of Order Form, Consignment by Order Form, Requisition sheet Creating. 3.3 The Design of Material Purchase Management Subsystem Integrated Infrastructure for Agile Enterprise (IIAE) helps the manufacturer and sup- plier share the data resource of product to realize the production capacity and the demand of market. The supplier can receive the information as soon as the manufac- turer set down a requisition sheet of material through e-mail, which greatly improves the efficiency of supply chain. The system also supports the warehouse information shared between manufacturer and supplier. As can be seen from Fig.6, flow chart of material purchase management subsystem begins in material purchase plan creation, which is always generated automatically after the complement of material requisitions in product module. The flow chart is Material Purchase→ Creat Purchase Plan→ Purchase Form Creat→ Requision sheet of Material & Create Billhead→ Purchase Account Settled. 3.4 The Design of Production Control Subsystem The Integrated Infrastructure for Agile Enterprise supports Master Production Sched- ule (MPS), Material Require Planning (MRP) and Capacity Requirements Planning Fuzzy Inf. Eng. (2009) 2:129-148 135 Fig.5: Flow chart of distribution resource planning subsystem Fig.6: Flow chart of material purchase management subsystem (CRP). The subsystem has functions of working schedule change, working period’s analysis and payroll costs statistics. The Production Planning Subsystem will help the enterprises to create an efficient product plan, precise shop management to control the raw materials and the quality of product, rigorous cost analysis to control the cost and sale price of product. All 136 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) contributes to quicken the pace of the enterprise operation, product flow optimization and enhances competitive power of agile enterprise. Fig.7: Flow chart of material requisition planning in production control As can be seen from Fig.7, material purchase operation and material used schedule operation in material management are needed before product schedule operation. And the manufacture process will not start until material purchase process is fulfilled. Fig.8: Flow chart of production planning subsystem As can be seen from Fig8, the system can help administer perform the whole pur- chase process. The procedure is production schedule → order form → the type of schedule sheet → the type number → workshop, and after choosing of those items, the production schedule is created. Then you can save the production schedule and define the number of the production schedule sheet, which set the quantity of product related to the order form. Both the product schedule management flow chart and its practical perform pro- cess indicate that the every operation of the flow chart are strictly controlled by the relative step before or after them. Fuzzy Inf. Eng. (2009) 2:129-148 137 Fig.9: Flow chart of production process As can be seen from Fig.9, the flow chart of Production Process is working pro- cedure of cutter and teams → note scanning → Cutters & Sartorius management → production order→ product material control→ production order auditing→ produc- tion order sheet printing → production order settled → production order settled & working procedure allocation → production note scanning → manufacturing sched- ule/laborage statistic/working procedure statistic. Fig.10: Note of cutter in production control Fig.11: Note of sartorius in production control Fig.10 and Fig.11 are the note of cutter in production control and the note of Sar- torius in production control, respectively. 4. Design of Data Access Model Concerning the data access strategy, we promote an optimized programming and al- gorithm based on the Extensible Markup Language (XML) and Model-View-Controller (MVC) model. As we know, data output and input is the most normal activity of Ag- ile Infrastructure. The data is always transferred to viewer and changed by customs at terminals, rather than stored back to the database. The data stream between database 138 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) and terminals is the main stream of the platform. Model-View-Controller model is design to separate the View module and logical module, which makes program more maintainable, extendable, transplantable and reusable. Trygve Reenskaug designed MVC model for Smalltalk platform in the late 1970s, which turns out to be a very useful and mature theory after thirty years’ evolution. Fig.12: MVC model2 The MVC model is always designed by J2EE, and there still not perform very well in the View-Controller separating, and there still hard to realize the MVC idea by J2EE program. The research team redefines the MVC Model2 based on XML and uses XML and Java to build an MVC model in data access strategy of Agile Infrastructure. In the research on data access of Agile Infrastructure, the author analyzes the main ways of implementation in the three parts of Model-View-Controller, and redesigns the program of MVC Model2 based on XML. In the programmer of MVC model, the whole process of data access of MVC model2 based on XML is described as follows. (see Fig.12) Firstly, the terminal browser sends signal of request about Uniform Resource Lo- cator (URL) address, and the front controller transmits the request through URL after the request of Hypertext Transfer Protocol (HTTP) arrives at the controller. Secondly, Path, the parameter of URL, accesses the page layout of system function and orientated the definition function. Together with the XML document of page logic, it is input to the XML interpreter. Thirdly, Funname, the parameter of URL, makes use of the application logic of system and accesses the system function Class through Reflection. The Java Class of systemic function logic acquires data by database accessing and takes back the logic running result. Fourthly, the XML interpreter forms HTML document which is identifiable to client, and returns to the client browser by HTTP agreement. The advantage of XML-based design of MVC model 2 is that system categorizes the functional page-layout and adopts different configuration rules for different kinds. Developers need only to make functional logic of page-layout in XML format, while the XML Parser in bottom layer of system forms the HTML document which is iden- tifiable to client by analyzing the XML document and transmits it to terminal. The aim of completely separating the layout technology from system logic is realized by the way of setting the View module with XML configuration technology, which Fuzzy Inf. Eng. (2009) 2:129-148 139 reduces the difficulties of system maintenance and secondary development. Other advantage is that the interface of system in infrastructure is changed by altering XML Parser rather than XML document. 5. Optimal Strategy for Production Planning ⎧ ⎫ ⎛ ⎞ ⎪ ⎪ ⎪ ⎜ a x (k)+ ⎟ ⎪ ⎪ ⎜ ⎟ ⎪ ⎪ ⎜ ⎟ ⎪ ⎜ ⎟ ⎪ ⎪ ⎪ ⎜ ⎟ ⎪ T + ⎪ ⎜ ⎟ ⎪ ⎜ ⎟ ⎪ b [T u (k)−ρ (k)] ⎪ ⎜ i i i ⎟ ⎪ ⎪ ⎪ ⎜ ⎟ ⎪ M N ⎪ ⎜ ⎟ ⎪ ⎜ ⎟ ⎪ ⎪ T + ⎪ ⎜ ⎟ ⎪ ⎪ ⎜ ⎟ ⎪ +b [ρ (k)− T u (k)] ⎜ i i i ⎟ ⎪ ⎪ ⎜ i ⎟ ⎪ ⎪ ⎨ ⎜ ⎟ ⎬ i=1 k=1 ⎜ ⎟ ⎜ ⎟ T + J = min . (1) ⎪ ⎜ ⎟ ⎪ +c [˜y (k)− z ˜ (k)] ⎪ ⎜ ⎟ ⎪ i i ⎜ i ⎟ ⎪ ⎪ ⎜ ⎟ ⎪ ⎪ ⎪ ⎝ ⎠ ⎪ ⎪ ⎪ T + ⎪ ⎪ +c ¯ [˜z (k)− y ˜ (k)] ⎪ ⎪ i i ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ + a x (N + 1) ⎩ ⎭ i=1 Optimal strategy for production planning of upriver-product-line can be realized by an algorithm based on Hopfield neural network, and optimality criterion is formula 1. The module is based on the demand and output of two-level product line, i.e. upriver-product-line and downriver-product-line. The production planning module of upriver-product-line based on limited cost of input buffer of downriver-product-line can be described as Equation (1). M represents the number of upriver-product-line, while N represents the number of the product-cycle of upriver-product-line in arranged. u (k) represents the num- ber of the workpiece output of upriver-product-line i in k product-cycle, which are n = n + m dimensional column vectors. n is the number of type of finished product gi i i i of No.i product-line in planning periods, while m is the number of type of subsidiaries of No.i product-line in planning periods. y ˜ (k) represents the total number of finished product of No.i product-line from No.i Periods to No.k Periods, which are n di- gi mensional column vectors. z ˜ (k) represents total demands of upriver-product-line for finished product and subsidiaries of downriver-product-line No.i from No.i Periods to No.k Periods. ρ (k) represents machining time of working center of upriver-product- line No.i during No.k Periods, which are f dimensional column vectors. f represents i i the number of workers in No.i product line. a represents the cost-coefficient related to finished product in No.i product-line, which are n dimensional column vectors. b gi i represents cost-coefficient related to call-back pay in upriver-product-line No.i, which are f dimensional column vectors. b represents cost-coefficient related to leave un- i i used equipments in upriver-product-line No.i, which are f dimensional column vec- tors. c represents cost-coefficient related to storage cost due to over demands of fin- ished product and subsidiaries output in upriver-product-line No.i, which are n + m i i dimensional column vectors. c ¯ represents cost-coefficient related to storage cost due to unmet demands of finished product and subsidiaries output in upriver-product-line No.i, which are n +m dimensional column vectors. T represents machining hours of i i i operator by machining n kind of workpiece during Period k in upriver-product-line gi No.i, which is a f ×n dimensional matrix. The algorithm can be seeing in Algorithm i gi 1. 140 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) Algorithm 1 Production Planning Based on MAS Step1. let minCost=maxnumber; parameter number={}; Step2. for(inti = 1; i <= M; i++){ For(intk = 1; k <= N; k++){ Costpartone = a ∗ x (k) +b ∗ max[T u (k)−ρ (k)] i i i +b ∗ max[ρ (k)− T u (k)]}} i i i +c ∗ max[y ˜ (k)− z ˜ (k)] i i +c ¯ ∗ max[z ˜ (k)− y ˜ (k)]; i i Step3. for(int i = 1; i <= M; i++){ Costparttwo = a ∗ x ∗ (N + 1);} Costx=Costpartone+Costparttwo Step4 Get new Costx by change the plan by Hopfield Neuron network, then go to Step1, until no new extreme value in five circles, and let R to be the number of Costx ; Step5. for (int x = 1; x <= R; x++){ if minCost>Costx{ parameter number=x; minCost=costx;}; Step6. Output(minCost, correlation parameter) 6. Cooperative Games for Interval Inventory Decision Support in Supply Chain We use to do research on the efficiency enhancement of the supply chain as a whole system, and want to find a set of optimal strategies for all the participants and calcu- late the sum of profit of participators. But we forget that the highest profit of the sum of all players is always costed by the lower profit of some members of supply chain. So, if one of participants finds out that he has to sacrifice his profit for the others, he may quit and join another supply chain to search an equitable business status. To clarify this problem, we can see through an example of two-stage Supply Chain. In this cooperative game, each side may change its inventory decision without the permission of the opposite side. Then if there are any changes that will lead to more profits, the player will choose to change to satisfy their profit-push intention. A module of Two-Echelon Supply Chain has been given based on the research of G. P. Cachon and P. H. Zipkin [3], where the relationship of Supplier and Demander can be described as a Cooperation Game. According to Nash’s Equilibrium Discrim- inance theories, the Games must have an equilibrium point and the final choice of Supplier and Demander must constringe to the equilibrium point. Our research group design an algorithm based on a Two-Agent-Module. We suppose participants of the negotiation have Perfect Information and Complete Information, that is, the participators have the correlative information clearly and cor- rectly. The equilibrium searching process is a multi-agent Game playing process in the supply chain. By programming, we use agent to simulate the negotiation pro- cess in cooperative game. According to Nash Equilibrium Theory, there must be an equilibrium point in the negotiation process, and there will be no more profit got by Fuzzy Inf. Eng. (2009) 2:129-148 141 any change of the player. Equilibrium point is the best result under the agreement of the each side of the participants. According to inventory module in a Two-Stage Supply Chain of G. P. Cachon and P. H. Zipkin, the interval inventory decision of the S L dealer is within the [s , s ], and the interval of inventory decision of the supplier is r r within the [0, S ]. Here, S represents a lager Constant, which is satisfied enough to the franchiser. Fig.13: Negotiation process of cooperative games [4] The theorem about the Equilibrium point in the cooperative games had been proved by John F. Nash, that is, the reasonable outcomes of the two-person games u = (u , u ) 1 2 should meet the requirement that (u , u ) ∈ B, u ≥ n , u ≥ n , and let the product of 1 2 1 1 2 2 (u − n )&(u − n ) to be maximum. 1 1 2 2 As can be seen from figure 13, B is feasible solution set, N is the conflicting point, the X-axis represents the profit of enterprise II and the Y-axis represents the profit of enterprise I in the plane Cartesian coordinate system. m is the original decision of enterprise II, while n represents the original decision of enterprise I. Specifically, the region B means the feasible solution set where the cooperation game may strike a bargain in the negotiation procedure. During the negotiation procedure, the partic- ipants choose their decisions during the region B by mixed strategies in a strategic equivalence status; a new decision may suddenly cause the decision change of the other side. Finally, the numerical results of the cooperative game constringe to the Nash’s equilibrium point in through an argy-bargy procedure. Unless the plays reach the equilibrium point, the both sides will not satisfy with the result and will stop to change their inventory decision. A Multi-Agent System (MAS) algorithm will be helpful to the both sides of the player in the cooperative game, because it will help the partic- ipants to reach the equilibrium point directly and quickly without wasting any time in the negotiation procedure. The numerical results of the MAS algorithm are on the assumption that all the participants have equality of bargaining skill and information. That is, the value of game of the algorithm is in compatibility with “reasonable out- comes”. 142 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) Algorithm 2 Cooperative Games in Supply Chain Step1. Let n=0; S int LAST=S LAST=maxnumber; r s /* initialization*/ Step2. /* initializers*/ Let S = S initia number; s s S temporal number=S initia number; s s S = S initia number; r r S temporal number=S initia number; r r B (S , S ) = B (S )F (S ) S r s s r s +∞ Ls + B (S + S − x) f (x)dx s r s /* OOS(Out Of Stock) Cost Expectation of supply Agent */ Π (S , S ) = h F (x)dx+ B (S , S ) s r s s S r s /*Cost Expectation of supply Agent*/ Step3. /*demand Agent*/ Let S new=0; min (S , S ) =maxnumber; s r s (S , S ) = 0; s r s S = S temporal number; s s S = S temporal number; r r S L For (int S = S ; S ≤ S ; S ++){ r r r r r L L r r C (y) = αβ(μ − y)+ (h +αβ ) F (x)dx r r Π (S , S ) = C (S )F (S ) r r s r r s +∞ + C (S + S − x) f (x)dx r r s /*Cost Expectation of demand Agent*/ If min (S , S ) ≥ (S , S ){ r r s r r s max (S , S ) = (S , S ); S new=S ;}} r r s r r s r r S temporal number=S new; r r Step4. /*supply Agent*/ Let S new=0; min (S , S )= maxnumber; s s r s Let (S , S ) = 0; S = S temporal number; s r s s s S = S temporal number; r r for (int S = 1; S <= S ; S ++) s s s B (S , S ) = B (S )F (S ) S r s s r s +∞ + B (S + S − x) f (x)dx s r s /* OOS(Out Of Stock) Cost Expectation of supply Agent */ Π (S , S ) = h F (x)dx+ B (S , S ) s r s s S r s /*Cost Expectation of supply Agent*/ If min (S , S ) ≥ (S , S ){ s r s s r s min (S , S ) = (S , S ); S new=S ;}} s r s s r s s s S temporal number=S new; s s Step5. /*Nash’s Equilibrium Discriminance Agent*/ if (S LAST==S temporal number) s s and(S LAST==S temporal number) r r Fuzzy Inf. Eng. (2009) 2:129-148 143 {n++;}else n=0; S LAST=s temporal number; s s S LAST=S temporal number; r r If (n < 10){go to step3;} Step6. Output(S NASH=S LAST, S NASH=S LAST) s s r r The Module is composed of Supply Agent and Demand Agent, both of which have their own decisions based on the choice of the opposite side. The Algorithm constringes to the equilibrium point when any change of their decision will not bring them better profit respectively. The algorithm can be seen in Algorithm 2. 7. The Dynamic Load Balance Scheduling Model With the expansion of our clients, load balance control has become a necessary con- sideration for the Garments Information Platform infrastructure construction. The goal of our work is to achieve good load balance of server group and offer high- quality service for the registered users as well as guests. For GIP system heavy loads are mostly caused by registered users, since only registered users can ask for data procession requests which need longer reacting time, while guests can only access some introducing information of platform. Current load balancing solutions, which fall into two categories: probe-based and distribution point systems are capable of improving the throughput of network service clusters. However, both two strategies suffer from fundamental structural limitations. After considering the existing load balancing method, we design a composition for the particularity of the GIP system, with partitioning services for clients across user cookies. For registered users, who use the platform regularly with larger input and output data stream, the load balance controller system are designed as an distribution point system to offer faster and more stable service. On the other hand, for non- registered users, probe-based system is applied, which will allocate to each server in turn. Because non-registered users often make similar requests with small data streams, easy and stable algorithm can achieve sound performances. 8. Intrusion Detection Technology For the increasing onslaught of computer attacks, which cause damages ranging from mere violation of confidentiality and issues of privacy up to actual financial losses, the information security infrastructure has become an indispensable part on Compre- hensive Information Platform design. One of the most important matters that we mainly concerned is the intrusion detec- tion technology, which is a new approach for providing a sense of security in existing computers and data, as can be seen from Fig.14. The approach is designed to identify, preferably in real time, the unauthorized use, misuse, and abuse of computer systems by both internal and external system penetrators. An effective intrusion detection algorithm will form a defense line in the whole system for information security. Nev- ertheless, every intrusion detection approach has their drawbacks. Signature-based 144 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) intrusion detection can identify the known intrusions but fail to detect the novel in- trusions, while anomaly-based intrusion detection has the potential to detect all intru- sions but unfortunately is limited by its unaccepted false alarm rate in most cases. Fig.14: The Intrusion Detection system (IDS) model of Common Intrusion Detection Frame ( CIDF) 9. Intelligent Communication Strategy Congestion takes place when too many users are using the same channel at the same time or too many data pass a channel at a time, which will both lead to data loss. Con- gestion control in telecommunication has been increasingly researched, and a series of papers deal with congestion control based on output feedback linearization theory and adaptive control techniques. As part of an overall strategy to relieve data con- gestion in GPRS telecommunication, we have been involved in developing methods for monitoring the extent of data congestion by using sequences of fuzzy neuron net- works, compressing and decompressing the transmitted data, predicting congestion and controlling the compression rate. Fig.15 shows the overall congestion control process in GPRS communication. First, a CFNN has been used to fuse two series of data which are derived separately from two different sensors. Secondly the fusion-image-series are sent to a wavelet- based FNN compression system, and the compression algorithm then dynamically compresses the image, compression rate being related to the congestion control and prediction system. The congestion monitoring system which includes the FNN-based congestion control and prediction is the third step of the whole process. The special compression algorithm applied in congestion control is a FNN based compression algorithm, whose compression rate can be easily and timely controlled, that means, we discard some of the less important information if necessary, in order to achieve very high compression rate when GPRS transmission ability is low. Thus, we can maintain a smooth connection and avoid the data traffic congestion at the cost of image quality. The image compression algorithm showed in congestion control has two characteristics: 1. controllable compression rate Fuzzy Inf. Eng. (2009) 2:129-148 145 Fig.15: Data procession strategy in the platform 2. better image quality under the same compression rate than other compression al- gorithms The first characteristic is achieved by wavelet transform for splitting, and the second one is done by FNN (fuzzy core neuron network) for vector-classification- compression algorithm. Data compression is one of the most important applications of wavelet transform [10]. Wavelet transform can be generated from digital filter banks. Wavelet trans- form hierarchically decomposes an input image into a series of successively lower resolution images and their associated detail images. Discrete-wavelet-translation of digital images is implemented by a set of filters, which are convolved with the image in rows and columns. An image is convolved with low-pass and high-pass filters and the odd samples of the filtered outputs are discarded resulting in down sampling the image by a factor of 2. The wavelet de- composition results in an approximation image and three detail images in horizontal, vertical, and diagonal directions. Decomposition into L levels of an original image results in a down sampled image of resolution 2L with respect to the original image as well as detail images. Images are analyzed by wavelet packets for splitting both the lower and the higher bands into several sub-images at a time. A set of wavelet packets is gained. The following wavelet packet basis function {w }(n = 0, 1,···∞) is generated from a given function w . w (l) = 2 h(k)w (2l− k), (2) 2n n w (l) = 2 g(k)w (2l− k). (3) 2n+1 n As can be seen from Equation (2) and (3), where the function w (l) can be identi- fied with the scaling functionφ, and w (l) with the mother waveletψ, h(k) and g(k)are the coefficients of the low-pass and high-pass filters, respectively. Two 1-D wavelet 146 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) packet basis functions are used to obtain the 2-D wavelet basis function through the tensor product along the horizontal and vertical directions. (see Fig.16) Fig.16: Splitting high and low frequency image by wavelet transform In this paper, we use Mallat algorithm as the wavelet transform algorithm. The reconstruction expression of Mallat can be seen as the following: 1 2 C (n, m) = C ( j, l)+ d ( j, l)h g + d ( j, l)g h K−1 ⎢ K n−2 j m−2l n−2 j m−2l ⎣ k k k,l∈Z k,l∈Z k,l∈Z ⎤ (4) + d ( j, l)g g . n−2 j m−2l⎥ k,l∈Z 1 2 3 Fig.16 shows that original image splits into a series of sub-images. D , D , D in j j j Fig.16 are represented as sub-images with high frequency characteristics in horizon- tal, vertical and diagonal directions respectively. The more times we use wavelet transform, the more sub-images we get. The more sub-images we acquire, the less image information we lose. But if we want to get more information by decomposi- tion we need to compress more useful information derived from the original image. For this reason, we hope the compression result can be controlled; therefore the con- gestion control algorithm may avoid transferring too much data while the wireless communications network being jammed. And if data traffic does not seem to happen, we may hope to send more information through the channel to transfer as high quality images as possible. The combination algorithm, as can be seen from Fig.17, based on FNN and wavelet transform, is right for GPRS communications. It can send smallest image data continuously, with sound image quality; it can control the compression rate timely and appropriately, while taking efforts to avoid data congestion. To compare our compression algorithm with other popular & traditional algo- rithms, another two typical compression algorithms, WENRAR transform and fractal image compression in DCT domain based on adaptive classified weighted ISM [12], are introduced and adopted to test the same pictures whose average performances are shown in table I. As can be seen from Table I, our image compression algorithm, Fuzzy Inf. Eng. (2009) 2:129-148 147 Fig.17: Image compression and decompression process which is based on FNN and wavelet transform, has shown much better performance than others in our experiments. Table 1: Comparison of Some Typical Compression Algorithm WENRAR Fractal Image Reconstruction Splitting Image By Translation Compression Image By Wavelet Mallat Wavelet in DCT Translation Translation then Domain Based on Compression Adaptive Classified Through FNN Weighted ISM [12] Vector-Classification Origin Data NO NO YES YES Lost Compression 1.57 10 30 30 Rate S/N(Single/ – – 6 12 Noise rate) 9. Conclusions In this paper, Comprehensive Information Platform designed for collaborative man- ufacturing was presented with an application case in garment industry. Specifically, the relation of GIP functions as well as the structure of CIP in the garment industry application was introduced. In addition, technical details about business resources planning model in manufacturing system, especially, the relation of subsystems are expressed clearly by figures, and the functions of subsystems were discussed in the following sections. The key enabled technologies in the platform includes agile in- 148 Jin-long Su · Zhong-hui Ouyang · Wan-cheng Ge (2009) frastructure and it’s application, business resources planning in manufacturing sys- tem, the design of data access model, optimal strategy for production planning, the dynamic load balance scheduling model, the intrusion detection technology and intel- ligent communication strategy. All of them are elaborated in turn. More elaborating performance report will come up after long period of observational and practical use. All technical details touched upon run in application, and the main improvement in its performance can only come with application. Some parts of the Agile Infrastructure are almost completed, i.e. GIP, which seems to work well and more details can be seen on the internet on http://www.istqz.com. References 1. Su JL, Chen YM, Ouyang Z H (2006) GPRS congestion control algorithm based on fuzzy kernel neural networks. JINAN: the 6th International Conference on Intelligent Systems Design and Appli- cations (ISDA’06), IEEE Computer Society: 954-959 2. Su JL, Chen YM, Ouyang Z H (2006) GPRS communication system designed for high congestion risk circumstance. SINGAPORE: 9th International Conference on Control, Automation, Robotics and Vision (ICARCV’2006), IEEE Control Systems Society: 1962-1967 3. Su JL, Chen YM, Ouyang Z H (2007) An image compression algorithm with controllable com- pression rate, Advances in Soft Computing. Germany: 2007 International Conference on Fuzzy Information and Engineering (ICFIE’07). Springer-Verlag: 83-88 4. Su JL, Ouyang Z H, Chen YM (2007) Research on comprehensive information platform designed for collaborative manufacturing. Tibet: The Sixth International Conference on Information and Manage- ment Sciences (IMS2007) 5. Su JL, Ouyang Z H, Chen YM (2007) Design of agile infrastructure for manufacturing system with FNN based web-enabled technology solutions. BRAZIL: the 7th International Conference on Intelli- gent Systems Design and Applications (ISDA’07), IEEE Computer Society 6. Su JL, Ouyang Z H, Chen, YM (2008) The design of agile infrastructure for manufacturing system in garment industry, Chongqing: The 7th World Congress on Intelligent Control and Automation (WCICA’08), IEEE Robotics and Automation Society & IEEE Control System Society & National Natural Science Foundation 7. Su JL, Ouyang Z H, Chen YM (2008) Research on agile infrastructure for collaborative manufac- turing and agile supply chain, SINGAPORE: 2008 IEEE International Conferences on Cybernetics & Intelligent Systems (CIS) and Robotics, Automation & Mechatronics (RAM) (CIS-RAM 2008), IEEE Robotics & Automation (R&A) Singapore Chapter & IEEE Systems, Mana & Cybernetics (SMC) Singapore Chapter 8. Su JL, Chen YM (2008) Application of information systems in collaborative manufacturing, the 12th World Multiconference on Systemics, Cybernetics and Informatics (WMSCI 2008). Orlando, USA 9. XIE RS, SUN F, HAO YL (2002) Multi-wavelet transform and its application in signal filtering. ACTA Electronic Sinica 30(3):419-421 10. TANG Y, MO YL (2000) Image coding of tree-structured using 2D wavelet transform. Journal of Shanghai University 6(1):71-74 11. Mars Fan P (1997) Access flow control scheme for ATM networks using neural-network-traffic pre- diction. IEEE rocComm 144(5):295-300 12. Yi ZK, Zhu WL, Gu DR (1997) Image progressive transmission and lossless coding using fractal image coding. Journal of UEST of China 26(5):473-476

Journal

Fuzzy Information and EngineeringTaylor & Francis

Published: Jun 1, 2009

Keywords: Agile infrastructure; Manufacturing industry; Supply chain; CIP

References