4.7 Article

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

Journal

TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
Volume 138, Issue -, Pages 172-186

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.tra.2020.05.012

Keywords

Facility location and capacity planning; Uncertain policy preference; Simulation-based optimization; Optimal computing budget allocation

Funding

  1. National Natural Science Foundation of China [71671032, 61703220, 61703290]
  2. Fundamental Research Funds for the Central Universities [N180408019]
  3. China Postdoctoral Science Foundation [2019T120569]
  4. Outstanding Youth Innovation Team Project of Colleges and Universities in Shandong Province [2020RWG011]

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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.

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