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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.
Evolutionary Computation – MIT Press
Published: Mar 3, 2021
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