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

IF 10.4 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Bahman Manafi , Hakan Sayan
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引用次数: 0

摘要

在需求不确定和运营中断的情况下,特别是在家电制造等复杂行业,弹性和响应性供应链网络的设计变得越来越重要。本研究提出了一个双目标混合整数线性规划(BOMILP)模型,该模型集成了多式联运规划、战略设施选址、库存控制和容量管理。该模型旨在同时(1)最小化总运营成本,包括运输、库存和短缺成本;(2)最大化客户响应,通过服务水平和交付周期进行评估。为了解决不确定性,将基于机器学习的预测和基于场景的鲁棒优化相结合,开发了一种混合方法。长短期记忆(LSTM)网络使用历史和外部数据预测需求波动,而稳健的模型确保在多种需求情景中有效分配资源。为了有效地求解复杂BOMILP模型,提出了一种将混合增广ε约束法(HA-ε)与贪婪随机自适应搜索法和自适应变量邻域搜索法(grip - avns)相结合的混合求解方法。弹性是通过诸如冗余容量、物流灵活性和稳健优化等策略嵌入的。弹性性能使用诸如成本稳定性、服务可靠性和不确定性下的响应性等指标进行评估。一个涉及土耳其一家跨国家电制造商的现实案例研究证明了该模型的有效性。结果表明,在中断情况下,提议的框架将总成本降低了15%,并将响应能力提高了20%以上。结合增强型ε约束方法的混合grip - avns启发式算法在求解不确定条件下的大规模BOMILP问题中表现出较强的性能。该研究为制造商在不确定的全球供应链环境中平衡成本效率、响应能力和弹性提供了一种可扩展的、数据驱动的决策支持工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Journal of Industrial Information Integration
Journal of Industrial Information Integration Decision Sciences-Information Systems and Management
CiteScore
22.30
自引率
13.40%
发文量
100
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.
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