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<p>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, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\pi\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>π</mi> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\phi\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>ϕ</mi> </math></EquationSource> </InlineEquation>, 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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年10月23日

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  • Amna Altaf,
  • Adnen El Amraoui,
  • Francois Delmotte,
  • Christophe Lecoutre

摘要

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.