Abstract:This paper is motivated by the case of a forwarder dealing with inland transportation planning, from a seaport, of inbound containers filled with pallets having different destinations in the land‐side. Although the forwarder is not the owner nor controls any vehicle, he is required to plan both the assignment of containers to intermediate depots, where the pallets are unpacked, and the assignment of pallets to the vehicles used for the distribution from depots to consignees. We present a mathematical model sup… Show more
“…Location–allocation problems that make use of intermediate hubs for shipping goods from origins to destinations have been primarily addressed in the literature to deal with modern global freight logistics. For instance, Di Francesco et al [8] studied the problem of a forwarder that needs to ship containers filled with pallets to intermediate depots of a two echelon‐network where pallets are unpacked and sent to different destinations. Instead, Zhao et al [37] approached the location of consolidation centers in China as transshipment facilities to ship freight by rail routes from China to Europe.…”
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.
“…Location–allocation problems that make use of intermediate hubs for shipping goods from origins to destinations have been primarily addressed in the literature to deal with modern global freight logistics. For instance, Di Francesco et al [8] studied the problem of a forwarder that needs to ship containers filled with pallets to intermediate depots of a two echelon‐network where pallets are unpacked and sent to different destinations. Instead, Zhao et al [37] approached the location of consolidation centers in China as transshipment facilities to ship freight by rail routes from China to Europe.…”
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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