An intercell scheduling approach considering transportation capacity

Intercell scheduling disrupts the cellular manufacturing philosophy of creating independent cells, but is essential for enterprises to reduce costs. Since intercell scheduling is in nature the coordination of intercell production and intercell transportation, the intercell scheduling problem is considered with transportation constraints in this paper. Hyper-heuristics are known for their computational efficiency but are lack in effectiveness since the candidate heuristic rules are usually manually set in advance. In this paper, a hybrid evolution-based hyper-heuristic algorithm is developed for the addressed intercell scheduling problem considering transportation capability. In order to improve the effectiveness of hyper-heuristics, genetic programming is introduced to generate new heuristic rules automatically based on the information of machines or vehicles, thus expanding the set of the candidate rules, and then, a rule selection genetic algorithm is developed to select appropriate rules from the obtained rule set, for the machines and vehicles, respectively. Finally, the scheduling solutions are generated according to the selected rules. The contribution of this work lies in (a) intercell transportation is considered in the intercell scheduling problem, and (b) heuristic generation is adopted in advance of the heuristic selection, constructing a more effective hyper-heuristic with both computation efficiency and optimization performance.