Multiperiod transshipment location–allocation problem with flow synchronization under stochastic handling operations

Abstract

The transshipment location–allocation problem consists of locating transshipment facilities (e.g., intermodal hubs) of a transportation network and allocating freight flows through them, from several origins to several destinations, to satisfy demand and supply constraints. The objective is to maximize the total net transportation utility given by the total shipping utility minus the total cost to locate the facilities. Moreover, flow synchronization at the facilities must also be ensured. Unfortunately, the flow synchronization depends on a broad set of unknown events, which could cause both unexpected reductions of the facility capacity and uncertain utility of handling operations. In this paper, we first want to evaluate how uncertainty on facility capacity and handling operations utility affects the transshipment location–allocation problem in terms of complexity, net gain, and optimal solutions. Moreover, we extend the problem from a single to a multi‐period setting to have a wider view of future scenarios realizations and consequently synchronize the flows by using different facilities on different periods. We propose a two‐stage stochastic programming formulation with recourse and analyze, over a ground set of instances, some well‐known economic indicators to derive managerial insights on the importance of addressing uncertainty for the problem. Finally, given the computational burden of solving the deterministic equivalent problem, we propose several heuristics based on progressive hedging and test their performance.

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“…Moreover, long-term planning must include some synchronization mechanisms, like adopting earliness and lateness penalties [75]. Another reason that makes synchronization important is dealing with unexpected events, like the possible loss of capacity [76]. Those network design decisions are essential to provide an efficient logistics network capable of adopting effective real-time procedures.…”

Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce

“…long time (Ivanov et al 2018) and exhibit unique characteristics, posing challenges to smooth and profitable operation (Golicic et al 2002;Lu and Liu 2015). Approaches for solving these problems include heuristics (Cooper 1964;Giusti, Manerba, and Tadei 2020;Rabbani, Mokhtarzadeh, and Manavizadeh 2021;Ben-Khedher and Yano 1994;Robinson, Narayanan, and Gao 2007;Ohta and Nakatani 2006;Harrabi, Driss, and Ghedira 2021;Penna, Subramanian, and Ochi 2013;Pan, Zhang, and Lim 2021) and reinforcement learning (Golowich, Narasimhan, and Parkes 2018;Allen, Pearn, and Monks 2021;Meisheri et al 2020;Asadi and Pinkley 2021;Baer et al 2019;Chen et al 2020;Nazari et al 2018;Sultana et al 2021).…”

Preface: Special issue on freight transportation and logistics

“…Finally, Giusti, Manerba, and Tadei [6] investigate a location‐allocation problem to maximize the total shipping utility minus the total cost to locate facilities. They aim to ensure flow synchronization at the facilities, but this aim is complicated by unexpected reductions of facility capacity and uncertain utility of handling operations.…”