A Hybrid Evolutionary Hyper-Heuristic Approach for Intercell Scheduling Considering Transportation Capacity


The problem of intercell scheduling considering transportation capacity with the objective of minimizing total weighted tardiness is addressed during this paper, that in nature is the coordination of production and transportation. Since it is a sensible call-making problem with high complexity and massive drawback instances, a hybrid evolutionary hyper-heuristic (HEH) approach, which combines heuristic generation and heuristic choice, is developed in this paper. So as to increase the variety and effectiveness of heuristic rules, genetic programming is used to automatically generate new rules based on the attributes of elements, machines, and vehicles. The new rules are added to the candidate rule set, and a rule selection genetic algorithm is developed to decide on applicable rules for machines and vehicles. Finally, scheduling solutions are obtained using the chosen rules. A comparative evaluation is conducted, with some state-of-the-art hyper-heuristic approaches which lack some of the methods proposed in HEH, with a meta-heuristic approach that's suitable for large scale scheduling issues, and with adaptations of some well-known heuristic rules. Computational results show that the new rules generated in HEH have similarities to the most effective-performing human-created rules, however are additional effective because of the evolutionary processes in HEH. Moreover, the HEH approach has blessings over other approaches in both computational efficiency and answer quality, and is particularly suitable for issues with massive instance sizes.

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