Fourth-Party Logistics Network Design with Demand Surge: A Greedy Scenario-Reduction and Scenario-Price Based Decomposition Algorithm

Abstract

In this paper, we study a novel network design problem for the fourth-party logistics network (4PLN) to cope with accidental demand surges. A chance-constrained stochastic programming model is established to minimize the overall cost for 4PLN under service-level targets, where the stochastic demand with surge characteristics is modeled as a compound distribution of a designed surge multiplier and Poisson process. To deal with the difficulties caused by chance constraints on customer demand under the compound distribution, we reformulate a mixed-integer linear programming (MILP) model, that can be solved straightly, based on the sample average approximation method. To address the enormous challenge posed by the coupling of the basic NP-hard network design problem and the large number of demand scenarios in the MILP version, the Scenario-Price based Decomposition Algorithm (P-DA) is designed based on the key idea of decomposing the above-coupled factors. To mitigate the performance deterioration brought on by large system scale and/or sample size, we expand our base algorithm to the Greedy Scenario-Reduction and Scenario-Price based Decomposition Algorithm (GR&P-DA) through the fast processing of chance constraints by introducing a greedy method. Computational results show the effectiveness of the proposed model and GR&P-DA, and the impact of model parameters such as demand level, surge multiplier, and rental price of the third-party logistics resource on 4PLN design are also revealed. What’s more, through the comparative analysis, we were surprised to discover that deploying resources in advance has not always had the advantage, a temporary “case-by-case” planning approach will give a more cost-saving scheme when surge frequency at a low level due to avoiding idle resources by not being stuck with a conservative strategy.

Keywords: Fourth-party logistics network (4PLN) design, Accidental demand surge, Chance-constrained stochastic programming, Decomposition algorithm