Abstract

Emergency supplies scheduling model for single demand point and its constrained multi-objective particle swarm optimization algorithm

Author(s): Yong Lin, Dali Jiang, Li Zhang, Yisheng Wang

Aiming at the shortest emergency rescue completion time and the maximum mean fullload ratio of transportation, a newly multi-objective model of emergency supplies scheduling for single demand point is proposed. Through the analysis of the diversity, the finiteness, the maximum load and the maximum capacity of transportation, the suggested model is more comprehensive and realistic. In order to solve this model, a constrained multi-objective particle swarm optimization algorithm fused with multiple constraint handling techniques (CMOPSO-MCHT) is presented, in which integral iteration, nonnegative solution space limitation and hyper-plane constraints are constructed to update the velocity of each particle, a dynamic threshold constraint dominance rule is put forward to update the individual best location of each particle and a method of objective function modification is applied to update the global best location of the swarm. The results of numerical experiment show that the set of Pareto optimal solutions obtained by CMOPSO-MCHT has a much better convergence and spread.