Robust cross-dock assignment problem with fuzzy chance constrained optimization approach

Amna Altaf, Adnen El Amraoui (), Francois Delmotte and Christophe Lecoutre
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Amna Altaf: UR 3926 Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), University of Artois
Adnen El Amraoui: UR 3926 Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), University of Artois
Francois Delmotte: UR 3926 Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), University of Artois
Christophe Lecoutre: CRIL-CNRS, UMR 8188, University of Artois

Operational Research, 2025, vol. 25, issue 4, No 18, 34 pages

Abstract: Abstract This paper presents an enhanced cross-dock assignment model with temporary storage, designed to optimize truck and dock assignments in a cross-docking facility. The initial deterministic model demonstrates efficient results, minimizing penalty, storage and operational costs. To address real-world uncertainties, the model is extended with a robust approach, introducing uncertainty in operational times, a parameter integral to the assignment constraints. This uncertainty is managed using Fuzzy Chance Constraint Optimization (FCCO) with triangular fuzzy numbers, which adapts to uncertain environments by applying possibility and necessity measures. Two variables, $$\pi$$ π and $$\phi$$ ϕ , are introduced to capture different levels of tolerance for uncertainty in operational times. Experimental results reveal the robust model’s effectiveness under varying uncertainty levels, offering insights into the trade-offs in cost and operational efficiency based on the chosen fuzzy parameters. This work provides a practical solution for managing uncertainty in cross-docking assignments, contributing to more resilient logistics optimization.

Keywords: Cross-dock; Truck to door assignment; Temporary storage; Fuzzy chance constrained optimization; Uncertainty; Optimization programming language. (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12351-025-00968-4

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