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Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and canhave multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promisingapproach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithmevolves dispatching rules (DRs) that are used to make decisions during the scheduling process(i.e., the so-called heuristic template). In DFJSS, there are two kinds of schedulingdecisions: the routing decision that allocates each operation to a machine to process it, andthe sequencing decision that selects the next job to be processed by each idle machine. Thetraditional heuristic template makes both routing and sequencing decisions in a non-delaymanner, which may have limitations in handling the dynamic environment. In this article, wepropose a novel heuristic template that delays the routing decisions rather than making themimmediately. This way, all the decisions can be made under the latest and most accurateinformation. We propose three different delayed routing strategies, and automatically evolvethe rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with DelayedRouting (GPHH-DR) on a multiobjective DFJSS that optimises the energy efficiency and meantardiness. The experimental results show that GPHH-DR significantly outperformed thestate-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristictemplate with delayed routing, which suggests the importance of delaying the routingdecisions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Evolutionary Computation MIT Press

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

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Publisher
MIT Press
Copyright
Copyright © MIT Press
ISSN
1063-6560
eISSN
1530-9304
DOI
10.1162/evco_a_00273
Publisher site
See Article on Publisher Site

Abstract

Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and canhave multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promisingapproach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithmevolves dispatching rules (DRs) that are used to make decisions during the scheduling process(i.e., the so-called heuristic template). In DFJSS, there are two kinds of schedulingdecisions: the routing decision that allocates each operation to a machine to process it, andthe sequencing decision that selects the next job to be processed by each idle machine. Thetraditional heuristic template makes both routing and sequencing decisions in a non-delaymanner, which may have limitations in handling the dynamic environment. In this article, wepropose a novel heuristic template that delays the routing decisions rather than making themimmediately. This way, all the decisions can be made under the latest and most accurateinformation. We propose three different delayed routing strategies, and automatically evolvethe rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with DelayedRouting (GPHH-DR) on a multiobjective DFJSS that optimises the energy efficiency and meantardiness. The experimental results show that GPHH-DR significantly outperformed thestate-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristictemplate with delayed routing, which suggests the importance of delaying the routingdecisions.

Journal

Evolutionary ComputationMIT Press

Published: Mar 3, 2021

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