4PL routing optimization under emergency conditions
Routing optimization is one of important problems in fourth party logistics (4PL) management. Under emergency conditions, effectively describing uncertain parameters for routing optimization entails major challenges because of a lack of historical data. Hence, this paper studies a fourth party logistics routing optimization problem (4PLROP) with uncertain delivery time under emergency conditions. A novel 4PLROP uncertain programming model (UPM) under emergency conditions is proposed by describing the uncertain delivery time as an uncertain variable based on the uncertainty theory (UT) first. Then, to justify the advantage of UPM for addressing this problem with little historical data, UPM is compared with the stochastic programming model (SPM) in which the uncertain delivery time is described as a stochastic variable based on probability theory. The comparison results show that the UPM solution can satisfy the belief degree constraint, which is used to describe the customer delivery time requirement, whereas the SPM solution cannot. Finally, numerical examples are used to verify the effectiveness of the proposed method. The results also suggest the proposed models advantages.
4plrop uncertainty theory emergency logistics 3pl chance-constrained programming cumulative prospect theory