Short-Term Inventory-Aware Equipment Management in Service Networks

Logistics companies often operate a heterogeneous fleet of equipment to support their service network operations. This introduces a layer of planning complexity as facilities need to maintain appropriate levels of equipment types to support operations throughout the planning horizon. We formulate an optimization model that minimizes the cost of executing a load plan, assuming knowledge of the trailer inventory distribution in the network at the start of the planning horizon, by possibly substituting the equipment type assigned to loaded movements and by judiciously adding empty equipment repositioning movements. We introduce an integer programming based heuristic, which heavily relies on dynamic variable generation, for its solution. Computational experiments using instances from a major US package express carrier show the efficacy of the solution approach and show the benefit of an optimization-based approach to inventory-aware equipment management: a significant reduction in the cost of empty equipment repositioning movements to avoid equipment shortages (if possible).


Georgia Institute of Technology, 11/2022



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