Evolutionary Multiobjective Optimization for the Pickup and Delivery Problem with Time Windows and Demands

This paper studies an evolutionary algorithm to solve a new multiobjective optimization problem, the Pickup and Delivery Problem with Time Windows and Demands (PDPTW-D), which extends PDP and PDP-TW. With respect to multiple optimization objectives, PDP-TW-D is to find a set of Pareto-optimal routes for a fleet of vehicles in order to serve given transportation requests. The proposed algorithm uses a population of individuals, each of which represents a solution candidate, and evolves them through generations to seek the Pareto-optimal solutions with respect to given multiple objectives. In addition to evolution, the proposed algorithm allows individuals to learn and improve themselves in each generation with a local search algorithm. Experimental results demonstrate that the evolutionary and learning processes complement with each other in the proposed algorithm and can effectively obtain quality solutions to PDP-TW-D.