Abstract
We present a large-scale static and deterministic mixed-integer linear programming (MILP) model solving a two-echelon capacitated location-routing problem (2E-CLRP) with modal choice in the context of urban logistics services (ULS). This model aims to support the development of profitable ULS by guiding the strategic decision making of postal operators as they design an optimal facility network and vehicle fleet for the centralized consolidation and transportation of inbound and outbound urban freight flows. After comprehensively analyzing operating data from La Poste, we identify the key determinants of an optimal infrastructure and fleet design for the centralized coordination and consolidation of urban freight flows under a global service time constraint. Further, we discuss the optimal design’s sensitivity to changes in the input data and parameters of the 2E-CLRP model. The main theoretical contributions of this work are an optimal routing cost estimation formula and an optimization heuristic. Together these allow us to solve the large-scale MILP problem presented here within a reasonable time and with little loss of precision.
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