A matheuristic-based self-learning approach for distributed heterogeneous assembly flowshop scheduling with multiple assembly factories and make-to-order delivery

IF 8.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zikai Zhang , Shujun Yu , Qiuhua Tang , Liping Zhang , Zixiang Li , Lixin Cheng , Yingli Li
{"title":"A matheuristic-based self-learning approach for distributed heterogeneous assembly flowshop scheduling with multiple assembly factories and make-to-order delivery","authors":"Zikai Zhang ,&nbsp;Shujun Yu ,&nbsp;Qiuhua Tang ,&nbsp;Liping Zhang ,&nbsp;Zixiang Li ,&nbsp;Lixin Cheng ,&nbsp;Yingli Li","doi":"10.1016/j.swevo.2025.101996","DOIUrl":null,"url":null,"abstract":"<div><div>Concerns about mass personalized customization and customer services have highlighted the importance of make-to-order delivery in distributed manufacturing areas. These make-to-order delivery services are deeply intertwined with distributed assembly scheduling, where variations in customer demand significantly influence production costs and efficiency. To address this, we propose the distributed heterogeneous assembly flowshop scheduling with multiple assembly factories and make-to-order delivery. Our approach begins with a mixed-integer linear programming model aimed at minimizing the tardiness cost. Subsequently, a hybrid algorithm, incorporating mathematical programming, iterated greedy technique, and self-learning strategy, is designed to solve the model, and termed matheuristic-based self-learning iterated greedy (MSIG) algorithm. This algorithm features a matheuristic-based decoding mechanism and a problem-specific NEH heuristic to generate high-quality initial solution. The nested greedy phase involves the extraction of both customers and products to refine solution quality. Furthermore, the local search phase incorporates knowledge-based operators, rule-based operator candidate sets, and a self-learning selection strategy to enhance the algorithm’s exploratory capabilities. Finally, through comprehensive comparisons with nine existing heuristics and six state-of-the-art meta-heuristics, the superiority of the MSIG algorithm and the efficacy of its components are conclusively demonstrated.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 101996"},"PeriodicalIF":8.2000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650225001543","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Concerns about mass personalized customization and customer services have highlighted the importance of make-to-order delivery in distributed manufacturing areas. These make-to-order delivery services are deeply intertwined with distributed assembly scheduling, where variations in customer demand significantly influence production costs and efficiency. To address this, we propose the distributed heterogeneous assembly flowshop scheduling with multiple assembly factories and make-to-order delivery. Our approach begins with a mixed-integer linear programming model aimed at minimizing the tardiness cost. Subsequently, a hybrid algorithm, incorporating mathematical programming, iterated greedy technique, and self-learning strategy, is designed to solve the model, and termed matheuristic-based self-learning iterated greedy (MSIG) algorithm. This algorithm features a matheuristic-based decoding mechanism and a problem-specific NEH heuristic to generate high-quality initial solution. The nested greedy phase involves the extraction of both customers and products to refine solution quality. Furthermore, the local search phase incorporates knowledge-based operators, rule-based operator candidate sets, and a self-learning selection strategy to enhance the algorithm’s exploratory capabilities. Finally, through comprehensive comparisons with nine existing heuristics and six state-of-the-art meta-heuristics, the superiority of the MSIG algorithm and the efficacy of its components are conclusively demonstrated.
基于数学的分布式异构装配流程车间调度和按订单交付的自学习方法
对大规模个性化定制和客户服务的担忧凸显了在分布式制造领域按订单生产的重要性。这些按订单生产的交付服务与分布式装配调度密切相关,客户需求的变化会显著影响生产成本和效率。为了解决这个问题,我们提出了多装配厂和按订单生产的分布式异构装配流程车间调度。我们的方法从一个混合整数线性规划模型开始,目的是最小化延迟成本。随后,设计了一种结合数学规划、迭代贪婪技术和自学习策略的混合算法来求解该模型,称为基于数学的自学习迭代贪婪(MSIG)算法。该算法具有基于数学的解码机制和针对特定问题的NEH启发式来生成高质量的初始解。嵌套贪婪阶段包括提取客户和产品以改进解决方案质量。此外,局部搜索阶段结合了基于知识的算子、基于规则的算子候选集和自学习选择策略,以增强算法的探索能力。最后,通过与现有的9种启发式算法和6种最新的元启发式算法的综合比较,最终证明了MSIG算法的优越性及其组成部分的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Swarm and Evolutionary Computation
Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
16.00
自引率
12.00%
发文量
169
期刊介绍: Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信