Chain-like agent genetic algorithm for multi-stage multi-product scheduling problem
多阶段多产品调度问题的链式智能体遗传算法

Combined the coding characteristic of the genetic algorithm with the evolution structure in the multi-agent system, a chain-like agent genetic algorithm is proposed to solve the multi-stage multi-product scheduling problem. The order-sequences-based encoding means is adopted, and the one-to-one correspondence between the encoding and feasible scheduling is achieved by new post-assignment rules. The population evolution is implemented by the operators of agent such as competition and cooperation with the dynamic neighboring environment and self-learning operator with its own knowledge. The simulation results of multi-stage multi-product scheduling problem show that the combination of chainlike agent genetic algorithm with the new heuristic rule not only increases the diversity of the population but also improves the convergent performance. It is effective in solving the multi-stage multi-product scheduling problem.