Anti-predatory particle-swarm optimization with flexible inertial weight for unconstrained multilevel lot-sizing problems
多级生产批量规划问题的柔性惯量反捕食粒子群算法

Multilevel lot-sizing(MLLS) is a crucial problem in decision-making for the master production scheduling(MPP) of the material requirement plan(MRP), which is with broad industrial applications and has been considered the NP-hard combinatory optimization problem. Anti-predatory particle-swarm optimization(APSO), which is closely related to particle-swarm optimization(PSO), is a recently emerged meta-heuristics. An anti-predatory particle-swarm optimization with flexible inertial weight(WAPSO) is proposed to solve the unconstrained MLLS problem in a given assembly structure. A set of 12 small-sized benchmark data and a randomly generated medium size data are adopted to test the proposed algorithm. The experimental results are compared with those of genetic algorithm(GA) and Wagner-Whitin(WW) dynamic programming algorithm, the results show that WAPSO algorithm is an effective and suitable tool for solving the unconstrained MLLS problem in a given assembly structure.