A self-adaptive hyper-heuristic based multi-objective optimisation approach for integrated supply chain scheduling problems

Abstract

Recent global changes have prompted manufacturers to shift their production systems to make-to-order (MTO) supply chain (SC), enabling them to adapt customised customer requirements with their rapidly changing behaviours, reduce inventory costs, and obtain competitive advantages in the market. However, traditional MTO-based scheduling approaches fail to consider all the SC stages required for optimal schedules. This study proposes an integrated SC scheduling problem (ISCSP), where supplier, manufacturer and batching decisions are simultaneously optimised in response to heterogeneous customer requirements with time window constraints. Both economic and environmental sustainability for the supply portfolio is considered while the manufacturer is modelled using the flexible job shop scheduling (FJS) problem. Since the proposed ISCSP is an extension of the FJS problem, this can also be considered an NP-hard problem, which cannot be solved by traditional optimisation techniques, particularly for larger instances. Thus, a self-adaptive multi-operator and multi-objective hyper-heuristic (SA(MO)2H) is designed, where the low-level heuristic utilises strengths of four solution updating heuristics and is intelligently guided by the reinforcement learning, to address the problem. The proposed SA(MO)2H integrates environmental sustainability into the evolutionary process to achieve the best possible supply portfolio, adopting the VIKORSORT approach. Finally, a rigorous experimental study on solving a wide range of instances is conducted to evaluate the performance of SA(MO)2H against its non-intelligent versions and five existing algorithms. Overall, the most beneficial facet of the developed ISCSP and SA(MO)2H is the visibility and meaningful managerial insights provided by the multi-portfolio solutions fostering the responsive relationship among SC stages.