Fourth-party logistics network design with service time constraint under stochastic demand

Mingqiang Yin, Min Huang (), Xiaohu Qian, Dazhi Wang, Xingwei Wang and Loo Hay Lee
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Mingqiang Yin: Northeastern University
Min Huang: Northeastern University
Xiaohu Qian: Shenzhen University
Dazhi Wang: Northeastern University
Xingwei Wang: Northeastern University
Loo Hay Lee: National University of Singapore

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 3, No 14, 1203-1227

Abstract: Abstract Due to the increasingly competitive nature of the global market, the capability of controlling delivery time is becoming a significant advantage for enterprises. A novel fourth-party logistics (4PL) network design problem with the objective of minimizing the overall cost under service time constraint and stochastic demand is proposed in the paper. To address this problem, a two-stage nonlinear stochastic programming model is proposed. The topological structure of the 4PL network is decided in the first stage, while the network flows are determined in the second stage. By using auxiliary variables to linearize the service time constraint and by adopting the sample average approximation (SAA) method to handle the stochastic demand, the two-stage nonlinear stochastic programming model is transformed into a mixed integer linear programming (MILP) model. To overcome the difficulties of solving the MILP model caused by a large number of demand scenarios and integer-valued decision variables, a variable separation (VS) strategy is presented to improve the dual decomposition and Lagrangian relaxation (DDLR) approach to propose a VSDDLR-SAA algorithm. Results of the numerical examples and a real-life case illustrate the effectiveness of the proposed model and VSDDLR-SAA algorithm. Comparison analysis of the 4PL network and the supply chain network shows that 4PL can deliver products within the prescribed time at a lower cost by cooperating with third-party logistics providers.

Keywords: 4PL network design; Service time; Stochastic demand; Sample average approximation method; Lagrangian relaxation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-021-01843-7

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