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

Suzanne Bihan (), Gülgün Alpan () and Bernard Penz ()
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Suzanne Bihan: Université Grenoble Alpes, CNRS, Grenoble INP, G-SCOP
Gülgün Alpan: Université Grenoble Alpes, CNRS, Grenoble INP, G-SCOP
Bernard Penz: Université Grenoble Alpes, CNRS, Grenoble INP, G-SCOP

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 6, No 13, 3964 pages

Abstract: 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.

Keywords: Logistics; Transportation; Network design problem; Automotive industry; Input aggregation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02421-3

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