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A Review and Taxonomy of Interactive Optimization Methods in Operations Research

A Review and Taxonomy of Interactive Optimization Methods in Operations Research This article presents a review and a classification of interactive optimization methods. These interactive methods are used for solving optimization problems. The interaction with an end user or decision maker aims at improving the efficiency of the optimization procedure, enriching the optimization model, or informing the user regarding the solutions proposed by the optimization system. First, we present the challenges of using optimization methods as a tool for supporting decision making, and we justify the integration of the user in the optimization process. This integration is generally achieved via a dynamic interaction between the user and the system. Next, the different classes of interactive optimization approaches are presented. This detailed review includes trial and error, interactive reoptimization, interactive multiobjective optimization, interactive evolutionary algorithms, human-guided search, and other approaches that are less well covered in the research literature. On the basis of this review, we propose a classification that aims to better describe and compare interaction mechanisms. This classification offers two complementary views on interactive optimization methods. The first perspective focuses on the users contribution to the optimization process, and the second concerns the components of interactive optimization systems. Finally, on the basis of this review and classification, we identify some open issues and potential perspectives for interactive optimization methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

A Review and Taxonomy of Interactive Optimization Methods in Operations Research

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 ACM
ISSN
2160-6455
eISSN
2160-6463
DOI
10.1145/2808234
Publisher site
See Article on Publisher Site

Abstract

This article presents a review and a classification of interactive optimization methods. These interactive methods are used for solving optimization problems. The interaction with an end user or decision maker aims at improving the efficiency of the optimization procedure, enriching the optimization model, or informing the user regarding the solutions proposed by the optimization system. First, we present the challenges of using optimization methods as a tool for supporting decision making, and we justify the integration of the user in the optimization process. This integration is generally achieved via a dynamic interaction between the user and the system. Next, the different classes of interactive optimization approaches are presented. This detailed review includes trial and error, interactive reoptimization, interactive multiobjective optimization, interactive evolutionary algorithms, human-guided search, and other approaches that are less well covered in the research literature. On the basis of this review, we propose a classification that aims to better describe and compare interaction mechanisms. This classification offers two complementary views on interactive optimization methods. The first perspective focuses on the users contribution to the optimization process, and the second concerns the components of interactive optimization systems. Finally, on the basis of this review and classification, we identify some open issues and potential perspectives for interactive optimization methods.

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS) – Association for Computing Machinery

Published: Sep 23, 2015

Keywords: Combinatorial optimization

References

  • Fine-tuning of algorithms using fractional experimental designs and local search
    Adenso-Díaz, Belarmino; Laguna, Manuel
  • Mixed-initiative interaction
    Allen, James F.
  • Power to the people: The role of humans in interactive machine learning
    Amershi, Saleema; Cakmak, Maya; Knox, William Bradley; Kulesza, Todd
  • Human-guided simple search
    Anderson, David; Anderson, Emily; Lesh, Neal; Marks, Joe; Mirtich, Brian; Ratajczak, David; Ryall, Kathy
  • Cognitive biases and decision support systems development: A design science approach
    Arnott, David
  • Chapter complexity and approximation in reoptimization
    Ausiello, Giorgio; Bonifaci, Vicenzo; Escoffier, Bruno
  • A case-based micro interactive genetic algorithm (CBMIGA) for interactive learning and search: Methodology and application to groundwater monitoring design
    Babbar-Sebens, Meghna; Minsker, Barbara
  • Interactive genetic algorithm with mixed initiative interaction for multi-criteria ground water monitoring design
    Babbar-Sebens, Meghna; Minsker, Barbara
  • The legacy of the technology acceptance model and a proposal for a paradigm shift
    Bagozzi, Richard P.
  • Interactive evolution
    Banzhaf, Wolfgang
  • Human centered processes and decision support systems
    Barthélemy, Jean-Pierre; Bisdorff, Raymond; Coppin, Gilles
  • Interactive multiobjective optimization from a learning perspective
    Belton, Valerie; Branke, Jürgen; Eskelinen, Petri; Greco, Salvatore; Molina, Julián; Ruiz, Francisco; Słowiński, Roman
  • Robust Optimization
    Ben-Tal, Aharon; Ghaoui, Laurent El; Nemirovski, Arkadi
  • Linear programming with multiple objective functions: Step method (stem)
    Benayoun, R.; de Montgolfier, J.; Tergny, J.; Laritchev, O.
  • The interplay of optimization and machine learning research
    Bennett, Kristin P.; Parrado-Hernández, Emilio
  • Robust optimization—A comprehensive survey
    Beyer, Hans-Georg; Sendhoff, Bernhard
  • Neural network fitness functions for a musical IGA
    Biles, John A.; Anderson, Peter G.; Loggi, Laura W.
  • The Problem of Tuning Metaheuristics
    Birattari, Mauro
  • Metaheuristics in combinatorial optimization: Overview and conceptual comparison
    Blum, Christian; Roli, Andrea
  • Building criteria: A prerequisite for MCDA
    Bouyssou, Denis
  • Interactive optimization improves service and performance for yellow freight system
    Braklow, John W.; Graham, William W.; Hassler, Stephen M.; Peck, Ken E.; Powell, Warren B.
  • Consideration of Partial User Preferences in evolutionary multiobjective Optimization
    Branke, Jürgen
  • Multiobjective Optimization, Interactive and Evolutionary Approaches
    Branke, Jürgen; Deb, Kalyanmoy; Miettinen, Kaisa; (Eds, Roman Słowiński
  • Guidance in evolutionary multi-objective optimization
    Branke, Jürgen; Kaußler, Thomas; Schmeck, Hartmut
  • The state of the art of nurse rostering
    Burke, Edmund K.; De Causmaecker, Patrick; Berghe, Greet Vanden; Van Landeghem, Hendrik
  • Hyper-heuristics: A survey of the state of the art
    Burke, Edmund K.; Gendreau, Michel; Hyde, Matthew; Kendall, Graham; Ochoa, Gabriela; Özcan, Ender; Qu, Rong
  • Hyperinteractive evolutionary computation
    Bush, Benjamin James; Sayama, Hiroki
  • Mexar2: AI solves mission planner problems
    Cesta, Amedeo; Cortellessa, Gabriella; Fratini, Simone; Oddi, Angelo; Denis, Michel; Donati, Alessandro; Policella, Nicola; Rabenau, Erhard; Schulster, Jonathan
  • A CSP-based interactive decision aid for space mission planning
    Cesta, Amedeo; Cortellessa, Gabriella; Oddi, Angelo; Policella, Nicola
  • A mixed-initiative system for interactive tactical supply chain optimization
    Chéné, François; Gaudreault, Jonathan; Quimper, Claude-Guy
  • A case study in large-scale interactive optimization
    Chimani, Markus; Lesh, Neal; Mitzenmacher, Michael; Sidner, Candy; Tanaka, Hidetoshi
  • User acceptance of computer technology: A comparison of two theoretical models
    Davis, Fred D.; Bagozzi, Richard P.; Warshaw, Paul R.
  • User perceptions of decision support effectiveness: Two production planning experiments
    Davis, Fred D.; Kottemann, Jeffrey E.
  • Reference point based multi-objective optimization using evolutionary algorithms
    Deb, Kalyanmoy; Sundar, J.
  • A survey and annotated bibliography of multiobjective combinatorial optimization
    Ehrgott, Matthias; Gandibleux, Xavier
  • Increased diels-alderase activity through backbone remodeling guided by Foldit players
    Eiben, Christopher B.; Siegel, Justin B.; Bale, Jacob B.; Cooper, Seth; Khatib, Firas; Shen, Betty W.; Players, Foldit; Stoddard, Barry L.; Popovic, Zoran; Baker, David
  • Objective Trade-off Rate Information in Interactive Multiobjective Optimization Methods: A Survey of Theory and Applications
    Eskelinen, Petri
  • Interactive machine learning
    Fails, Jerry Alan; Olsen, Dan R.; Jr,
  • Interactive optimization
    Fisher, M. L.
  • An architecture for the integration of decision making support functionalities
    Forgionne, Guisseppi A.
  • Optimization for simulation: Theory vs
    Fu, Michael C.
  • Separate-and-Conquer rule learning
    Fürnkranz, Johannes
  • Solving Large-Scale Vehicle Routing Problems with Time Windows: The State-of-the-Art
    Gendreau, Michel; Tarantilis, Christos D.
  • Dominance-Based rough set approach to interactive multiobjective optimization
    Greco, Salvatore; Matarazzo, Benedetto; Słowiński, Roman
  • Wastewater treatment: New insight provided by interactive multiobjective optimization
    Hakanen, Jussi; Miettinen, Kaisa; Sahlstedt, Kristian
  • Tuning tabu search strategies via visual diagnosis
    Halim, Steven; Lau, Hoong Chuin
  • Human-Machine interaction for real-time linear optimization
    Hamel, Simon; Gaudreault, Jonathan; Quimper, Claude-Guy; Bouchard, Mathieu; Marier, Philippe
  • Introduction to Operations Research (7th ed
    Hillier, Frederick S.; Lieberman, Gerald J.
  • Sequential model-based parameter optimization: An experimental investigation of automated and interactive approaches
    Hutter, Frank; Bartz-Beielstein, Thomas; Hoos, Holger H.; Leyton-Brown, Kevin; Murphy, Kevin P.
  • Automated configuration of mixed integer programming solvers
    Hutter, Frank; Hoos, Holger H.; Leyton-Brown, Kevin
  • Interactive multiobjective evolutionary algorithms
    Jaszkiewicz, Andrzej; Branke, Jürgen
  • The ‘light beam search’ approach—An overview of methodology applications
    Jaszkiewicz, Andrzej; Słowiński, Roman
  • Visualization and optimization
    Jones, Christopher V.
  • Nurse scheduling: From academia to implementation or not? Interfaces 37, 4, 355--369
    Kellogg, Deborah L.; Walczak, Steven
  • Algorithm discovery by protein folding game players
    Khatib, Firas; Cooper, Seth; Tyka, Michael D.; Xu, Kefan; Makedon, Ilya; Popović, Zoran; Baker, David; Players, Foldit
  • Application of interactive genetic algorithm to fashion design
    Kim, Hee-Su; Cho, Sung-Bae
  • Human-guided tabu search
    Klau, Gunnar W.; Lesh, Neal; Marks, Joe; Mitzenmacher, Michael
  • Human-guided search
    Klau, Gunnar W.; Lesh, Neal; Marks, Joe; Mitzenmacher, Michael
  • Stochastic optimization
    Kleywegt, Anton J.; Shapiro, Alexander
  • A man-machine approach toward solving the traveling salesman problem
    Krolak, Patrick; Felts, Wayne; Marble, George
  • Fixing the program my computer learned: Barriers for end users, challenges for the machine
    Kulesza, Todd; Wong, Weng-Keen; Stumpf, Simone; Perona, Stephen; White, Rachel; Burnett, Margaret M.; Oberst, Ian; Ko, Andrew J.
  • Open-Phylo: A customizable crowd-computing platform for multiple sequence alignment
    Kwak, Daniel; Kam, Alfred; Becerra, David; Zhou, Qikuan; Hops, Adam; Zarour, Eleyine; Kam, Arthur; Sarmenta, Luis; Blanchette, Mathieu; Waldispühl, Jérôme
  • Fifty years of vehicle routing
    Laporte, Gilbert
  • Bilevel heat exchanger network synthesis with an interactive multi-objective optimization method
    Laukkanen, Timo; Tveit, Tor-Martin; Ojalehto, Vesa; Miettinen, Kaisa; Fogelholm, Carl-Johan
  • Trust in automation: Designing for appropriate reliance
    Lee, John D.; See, Katrina A.
  • Sparse fitness evaluation for reducing user burden in interactive genetic algorithm
    Lee, Joo-Young; Cho, Sung-Bae
  • Why do people use information technology? A critical review of the technology acceptance model
    Legris, Paul; Ingham, John; Collerette, Pierre
  • The concept of recoverable robustness, linear programming recovery, and railway applications
    Liebchen, Christian; Lübbecke, Marco; Möhring, Rolf; Stiller, Sebastian
  • Combating user fatigue in iGAs: Partial ordering, support vector machines, and synthetic fitness
    Llorà, Xavier; Sastry, Kumara; Goldberg, David E.; Gupta, Abhimanyu; Lakshmi, Lalitha
  • Addressing the gap in scheduling research: A review of optimization and heuristic methods in production scheduling
    Maccarthy, Bart L.; Liu, Jiyin
  • A perspective on bridging the gap between theory and practice in university timetabling
    McCollum, Barry
  • A heuristic approach to schedule reoptimization in the context of interactive optimization
    Meignan, David
  • An experimental investigation of reoptimization for shift scheduling
    Meignan, David
  • An interactive heuristic approach for the P-forest problem
    Meignan, David; Frayret, Jean-Marc; Pesant, Gilles
  • A heuristic approach to automated forest road location
    Meignan, David; Frayret, Jean-Marc; Pesant, Gilles; Blouin, Mathieu
  • Interactive optimization with long-term preferences inference on a shift scheduling problem
    Meignan, David; Knust, Sigrid
  • Using interactive multiobjective optimization in continuous casting of steel
    Miettinen, Kaisa
  • Survey of methods to visualize alternatives in multiple criteria decision making problems
    Miettinen, Kaisa
  • NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point
    Miettinen, Kaisa; Eskelinen, Petri; Ruiz, Francisco; Luque, Mariano
  • Interactive multiobjective optimization system WWW-NIMBUS on the Internet
    Miettinen, Kaisa; Mäkelä, Marko M.
  • Introduction to Multiobjective Optimization: Interactive Approaches
    Miettinen, Kaisa; Ruiz, Francisco; Wierzbicki, Andrzej P.
  • Trust between humans and machines, and the design of decision aids
    Muir, Bonnie M.
  • Evolutionary dynamic optimization: A survey of the state of the art
    Nguyen, Trung Thanh; Yang, Shengxiang; Branke, Juergen
  • Assisting human decision making with intelligent technologies
    Phillips-Wren, Gloria
  • Design and implementation of scheduling systems: Basic concepts
    Pinedo, Michael L.
  • A-Teams: An agent architecture for optimization and decision-support
    Rachlin, John; Goodwin, Richard; Murthy, Sesh; Akkiraju, Rama; Wu, Fred; Kumaran, Santhosh; Das, Raja
  • Optimization Problems
    Rothlauf, Franz
  • Main sources of inaccurate determination, uncertainty and imprecision in decision models
    Roy, Bernard
  • Paradigms and challenges
    Roy, Bernard
  • Interactive multiobjective optimization for 3D HDR brachytherapy applying IND-NIMBUS
    Ruotsalainen, Henri; Miettinen, Kaisa; Palmgren, Jan-Erik
  • The general pickup and delivery problem
    Savelsbergh, Martin W. P.; Sol, Marc
  • Solving tough problems with games
    Schrope, Mark
  • Investigating human-computer optimization
    Scott, Stacey D.; Lesh, Neal; Klau, Gunnar W.
  • Past, present, and future of decision support technology
    Shim, J. P.; Warkentin, Merrill; Courtney, James F.; Power, Daniel J.; Sharda, Ramesh; Carlsson, Christer
  • Interactive multiple objective optimization: Survey I—continuous case
    Shin, Wan S.; Ravindran, A.
  • Cognitive Biases affect the acceptance of tradeoff studies
    Smith, Eric D.; Piatelli-Palmarini, Massimo; Bahill, Terry
  • Introduction: optimization and machine learning
    Sra, Suvrit; Nowozin, Sebastien; Wright, Stephen J.
  • Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation
    Takagi, Hideyuki
  • Asynchronous teams: Cooperation schemes for autonomous agents
    Talukdar, Sarosh; Baerentzen, Lars; Gove, Andrew; De Souza, Pedro
  • Interactive multi-objective optimisation of configurations for an oxyfuel power plant process for CO2 capture
    Tveit, T.-M.; Laukkanen, T.; Ojalehto, V.; Miettinen, K.; Tveit, C.-J. Fogelholm Tor-Martin; Laukkanen, Timo; Ojalehto, Vesa; Miettinen, Kaisa; Fogelholm, Carl-Johan
  • Interactive optimization of bulk sugar deliveries
    van Vliet, André; Boender, C. Guus E.; Kan, Alexander H. G. Rinnooy
  • Business Intelligence: Data Mining and Optimization for Decision Making
    Vercellis, Carlo
  • Guided local search
    Voudouris, Christos; Tsang, Edward P. K.
  • Comparative evaluation of some interactive approaches to multicriterion optimization
    Wallenius, Jyrki
  • Model-Based decision support
    Wessels, Jaap; Wierzbicki, Andrzej P.
  • Reoptimization of NP-hard problems
    Zych, Anna