Hybrid Predictive Control for the Vehicle Dynamic Routing Problem based on Evolutionary Multiobjective Optimization (EMO)

Abstract

In this paper, a hybrid adaptive predictive control approach (HAPC) to solve a dynamic pickup and delivery problem (DPDP) is presented based on a dynamic objective function that includes two dimensions: user and operator costs. Because these two costs are opposite components, the problem was formulated and solved by using an Evolutionary Multiobjective Optimization (EMO) technique. The idea is to minimize both, user and operator costs. At every instant, the use of genetic algorithms is proposed to find the optimal Pareto front associated with the DPDP, whose Pareto Optimal set is a set of solutions of the problem. Since only one solution has to be applied to the system every time a new request appears, several criteria will be utilized in order to properly use the information provided by the dynamic optimal Pareto front. Illustrative experiments through simulation of the process are presented to show the potential benefits of the new approach. Thus, by using EMO, the trade off between the two conflicting objectives will become clear for the dispatcher when making dynamic routing decisions.