A quantitative study of data aggregation for a network design problem: a case of automotive distribution

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Suzanne Le Bihan, Gülgün Alpan, Bernard Penz
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Abstract

This paper presents a framework for a systematic analysis of the impact of data aggregation on a multi-product multi-period network design problem with batch cost. The optimization objective is to design the vehicle distribution network for an automotive manufacturer. Numerical experiments are conducted with real production data. Given the problem’s scale and complex constraints, data aggregation emerges as a natural strategy to help the convergence of resolution methods towards good solutions. We explore three aggregation dimensions: product type, spatial, and temporal, and for each of them, different levels. Addressing multiple aggregation dimensions is a novel approach that has not been extensively explored in current literature, especially within industrial settings. Our aggregation-disaggregation method reveals that data aggregation consistently leads to improved solutions within a constrained computation time, with temporal aggregation demonstrating the most significant reduction in problem size and solution improvement. Lastly, we give some managerial insights considering the industrial context.

Abstract Image

针对网络设计问题的数据汇总定量研究:以汽车分销为例
本文提出了一个框架,用于系统分析数据聚合对具有批量成本的多产品多周期网络设计问题的影响。优化目标是为一家汽车制造商设计汽车分销网络。利用真实生产数据进行了数值实验。考虑到问题的规模和复杂的约束条件,数据聚合成为一种自然的策略,有助于解决方法向好的解决方案收敛。我们探索了三个聚合维度:产品类型、空间和时间,并为每个维度探索了不同的层次。处理多个聚合维度是一种新颖的方法,在目前的文献中尚未得到广泛探讨,尤其是在工业环境中。我们的聚合-分解方法表明,数据聚合能在有限的计算时间内持续改进解决方案,其中时间聚合能最显著地减少问题规模并改进解决方案。最后,我们结合工业背景提出了一些管理见解。
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来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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