Hybrid adaptive predictive control for a dynamic pickup and delivery problem

Abstract

This paper presents a hybrid adaptive predictive control approach that includes future information in realtime routing decisions in the context of a dynamic pickup and delivery problem (DPDP). We recognize in this research that when the problem is dynamic, an additional stochastic effect has to be considered within the analytical expression of the objective function for vehicle scheduling and routing, which is the extra cost associated with potential rerouting arising from unknown requests in the future. The major contributions of this paper are: first, the development of a formal adaptive predictive control framework to model the DPDP, and second, the development and coding of an ad hoc particle swarm optimization (PSO) algorithm to efficiently solve it. Predictive state-space formulations are written on the relevant variables (vehicle load and departure time at stops) for the DPDP. Next, an objective function is stated to solve the real-time system when predicting one and two steps ahead in time. A problem-specific PSO algorithm is proposed and coded according to the dynamic formulation. Then, the PSO method is used to validate this approach through a simulated numerical example.