Facility location and capacity planning considering policy preference and uncertain demand under the One Belt One Road initiative

Yaping Fu, Di Wu, Yan Wang and Hongfeng Wang

Transportation Research Part A: Policy and Practice, 2020, vol. 138, issue C, 172-186

Abstract: Due to its importance in integrating facilities and smoothing supply chain, facility location and capacity planning problem (FLCPP) is no doubt a key issue in constructing an efficient logistics and transportation network. One Belt and One Road (OBOR) initiative aims at reconstructing the logistics and transportation network along the trade corridors and improving the trade connectivity among the developing and developed countries. Nevertheless, under the OBOR initiative, FLCPP is greatly influenced by the government policy for regional development and the uncertain demand incurred by the enormous difference of economic form, custom and culture awareness among the involved countries. This study focuses on a special FLCPP under the OBOR initiative, in which the uncertainties of both policy preference and customer demand are considered simultaneously. To cope with these two categories of uncertainties, this study proposes a simulation-based optimization approach that includes the rough simulation and fine simulation methods. The former aims at finding a set of feasible decisions by using fuzzy and stochastic simulation, while the latter is used to achieve the best decision from the obtained feasible decisions by hybridizing the stochastic simulation with optimal computing budget allocation and mathematical programming. In order to demonstrate the application and advantage of the proposed approach, simulation experiments are conducted and experimental results show that this approach performs well in dealing with the concerned problem as well as some managerial insights and policy suggestions are obtained according to the analysis and results.

Keywords: Facility location and capacity planning; Uncertain policy preference; Simulation-based optimization; Optimal computing budget allocation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.tra.2020.05.012

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