{"title":"多式联运全球供应链设计的双目标鲁棒优化和启发式框架","authors":"Bahman Manafi , Hakan Sayan","doi":"10.1016/j.jii.2025.100895","DOIUrl":null,"url":null,"abstract":"<div><div>The design of resilient and responsive supply chain networks has become increasingly critical amid demand uncertainty and operational disruptions, especially in complex sectors such as home appliance manufacturing. This study presents a bi-objective mixed-integer linear programming (BOMILP) model that integrates multimodal transportation planning, strategic facility location, inventory control, and capacity management. The model aims to simultaneously (1) minimize total operational costs, including transportation, inventory, and shortage costs, and (2) maximize customer responsiveness, evaluated through service levels and fulfillment lead times. To address uncertainty, a hybrid approach is developed by combining machine learning-based forecasting and scenario-based robust optimization. Long Short-Term Memory (LSTM) networks forecast demand fluctuations using historical and external data, while the robust model ensures effective resource allocation across multiple demand scenarios. To solve the complex BOMILP model efficiently, a hybrid solution methodology is proposed, integrating the Hybrid Augmented ε-Constraint Method (HA-ε) with a Greedy Randomized Adaptive Search Procedure and Adaptive Variable Neighborhood Search (GRASP-AVNS) heuristic. Resilience is embedded through strategies such as redundant capacities, logistics flexibility, and robust optimization. Resilience performance is assessed using indicators like cost stability, service reliability, and responsiveness under uncertainty. A real-world case study involving a multinational home appliance manufacturer in Turkey demonstrates the model’s effectiveness. Results indicate that the proposed framework reduces total costs by up to 15% and enhances responsiveness by over 20% under disruption scenarios. The hybrid GRASP-AVNS heuristic combined with the augmented ε-constraint method demonstrates strong performance in solving large-scale BOMILP problems under uncertainty. This research provides a scalable, data-driven decision-support tool for manufacturers aiming to balance cost-efficiency, responsiveness, and resilience in uncertain global supply chain environments.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100895"},"PeriodicalIF":10.4000,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Bi-objective robust optimization and heuristic framework for designing resilient and responsive global supply chains with multimodal transportation\",\"authors\":\"Bahman Manafi , Hakan Sayan\",\"doi\":\"10.1016/j.jii.2025.100895\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The design of resilient and responsive supply chain networks has become increasingly critical amid demand uncertainty and operational disruptions, especially in complex sectors such as home appliance manufacturing. This study presents a bi-objective mixed-integer linear programming (BOMILP) model that integrates multimodal transportation planning, strategic facility location, inventory control, and capacity management. The model aims to simultaneously (1) minimize total operational costs, including transportation, inventory, and shortage costs, and (2) maximize customer responsiveness, evaluated through service levels and fulfillment lead times. To address uncertainty, a hybrid approach is developed by combining machine learning-based forecasting and scenario-based robust optimization. Long Short-Term Memory (LSTM) networks forecast demand fluctuations using historical and external data, while the robust model ensures effective resource allocation across multiple demand scenarios. To solve the complex BOMILP model efficiently, a hybrid solution methodology is proposed, integrating the Hybrid Augmented ε-Constraint Method (HA-ε) with a Greedy Randomized Adaptive Search Procedure and Adaptive Variable Neighborhood Search (GRASP-AVNS) heuristic. Resilience is embedded through strategies such as redundant capacities, logistics flexibility, and robust optimization. Resilience performance is assessed using indicators like cost stability, service reliability, and responsiveness under uncertainty. A real-world case study involving a multinational home appliance manufacturer in Turkey demonstrates the model’s effectiveness. Results indicate that the proposed framework reduces total costs by up to 15% and enhances responsiveness by over 20% under disruption scenarios. The hybrid GRASP-AVNS heuristic combined with the augmented ε-constraint method demonstrates strong performance in solving large-scale BOMILP problems under uncertainty. This research provides a scalable, data-driven decision-support tool for manufacturers aiming to balance cost-efficiency, responsiveness, and resilience in uncertain global supply chain environments.</div></div>\",\"PeriodicalId\":55975,\"journal\":{\"name\":\"Journal of Industrial Information Integration\",\"volume\":\"47 \",\"pages\":\"Article 100895\"},\"PeriodicalIF\":10.4000,\"publicationDate\":\"2025-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Industrial Information Integration\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2452414X25001189\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Industrial Information Integration","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2452414X25001189","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
A Bi-objective robust optimization and heuristic framework for designing resilient and responsive global supply chains with multimodal transportation
The design of resilient and responsive supply chain networks has become increasingly critical amid demand uncertainty and operational disruptions, especially in complex sectors such as home appliance manufacturing. This study presents a bi-objective mixed-integer linear programming (BOMILP) model that integrates multimodal transportation planning, strategic facility location, inventory control, and capacity management. The model aims to simultaneously (1) minimize total operational costs, including transportation, inventory, and shortage costs, and (2) maximize customer responsiveness, evaluated through service levels and fulfillment lead times. To address uncertainty, a hybrid approach is developed by combining machine learning-based forecasting and scenario-based robust optimization. Long Short-Term Memory (LSTM) networks forecast demand fluctuations using historical and external data, while the robust model ensures effective resource allocation across multiple demand scenarios. To solve the complex BOMILP model efficiently, a hybrid solution methodology is proposed, integrating the Hybrid Augmented ε-Constraint Method (HA-ε) with a Greedy Randomized Adaptive Search Procedure and Adaptive Variable Neighborhood Search (GRASP-AVNS) heuristic. Resilience is embedded through strategies such as redundant capacities, logistics flexibility, and robust optimization. Resilience performance is assessed using indicators like cost stability, service reliability, and responsiveness under uncertainty. A real-world case study involving a multinational home appliance manufacturer in Turkey demonstrates the model’s effectiveness. Results indicate that the proposed framework reduces total costs by up to 15% and enhances responsiveness by over 20% under disruption scenarios. The hybrid GRASP-AVNS heuristic combined with the augmented ε-constraint method demonstrates strong performance in solving large-scale BOMILP problems under uncertainty. This research provides a scalable, data-driven decision-support tool for manufacturers aiming to balance cost-efficiency, responsiveness, and resilience in uncertain global supply chain environments.
期刊介绍:
The Journal of Industrial Information Integration focuses on the industry's transition towards industrial integration and informatization, covering not only hardware and software but also information integration. It serves as a platform for promoting advances in industrial information integration, addressing challenges, issues, and solutions in an interdisciplinary forum for researchers, practitioners, and policy makers.
The Journal of Industrial Information Integration welcomes papers on foundational, technical, and practical aspects of industrial information integration, emphasizing the complex and cross-disciplinary topics that arise in industrial integration. Techniques from mathematical science, computer science, computer engineering, electrical and electronic engineering, manufacturing engineering, and engineering management are crucial in this context.