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 (PDP-TW-D), which is applicable to operational optimization in various mobile network systems. With respect to multiple optimization objectives, PDP-TW-D is to find a set of Pareto-optimal routes for a fleet of vehicles (e.g., mobile robots, drones and autonomous heavy-haulage trucks) 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. In addition to the evolution-based global search process, the proposed algorithm allows individuals to improve their optimality in each generation with a local search process, which is designed based on iterative neighborhood search. Experimental results demonstrate that the integration of global and local search processes improves the optimality of individuals and expedites convergence speed. The proposed algorithm outperforms two well-known existing EMOAs, NSGA-II and MOEA/D, in relatively large-scale problems that have up to 400 pickup and delivery locations.