Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm

The Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades. However, the main focus has been on the deterministic version where customer demands are fixed and known in advance. Uncertainty in demand has not received enough consideration. When demands are uncertain, several problems arise in the VRP. For example, there might be unmet customers’ demands, which eventually lead to profit loss. A reliable plan and set of routes, after solving the VRP, can significantly reduce the unmet demand costs, helping in obtaining customer satisfaction. This paper investigates a variant of an uncertain VRP in which the customers’ demands are supposed to be uncertain with unknown distributions. An advanced Particle Swarm Optimization (PSO) algorithm has been proposed to solve such a VRP. A novel decoding scheme has also been developed to increase the PSO efficiency. Comprehensive computational experiments, along with comparisons with other existing algorithms, have been provided to validate the proposed algorithms.

► An improved PSO approach is applied to solve the CVRP having stochastic demands.
► We have developed a novel decoding method for interpreting PSO solutions for the VRP.
► Our proposed method has less robustness cost than the exact robust algorithm.
► In all cases, the proposed method has produced a feasible solution.