A self-adaptive hyper-heuristic based multi-objective optimisation approach for integrated supply chain scheduling problems
作者：
Highlights：
• A multi-objective mathematical model is developed for optimal operational performance.
• Supply, production, and batching decision portfolios are integrated into the proposed model.
• VIKORSORT is employed to quantify suppliers’ environmental performance.
• An advanced self-adaptive hyper-heuristic approach is proposed for the ISCSPs.
• Reinforcement learning is employed for choosing the right heuristic intelligently.
摘要
•A multi-objective mathematical model is developed for optimal operational performance.•Supply, production, and batching decision portfolios are integrated into the proposed model.•VIKORSORT is employed to quantify suppliers’ environmental performance.•An advanced self-adaptive hyper-heuristic approach is proposed for the ISCSPs.•Reinforcement learning is employed for choosing the right heuristic intelligently.
论文关键词：Self-adaptive hyper-heuristic,Supply chain scheduling,Flexible job shop,Multi-objective hyper-heuristic,Reinforcement learning
论文评审过程：Received 24 January 2022, Revised 17 May 2022, Accepted 30 May 2022, Available online 4 June 2022, Version of Record 17 June 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.109190
