多阶段多产品调度问题的链式智能体遗传算法

Chain-like agent genetic algorithm for multi-stage multi-product scheduling problem

中文关键词  多阶段多产品调度问题  启发式规则  遗传算法  智能体系统

英文关键词  multi-stage multi-product scheduling problem  heuristic rule  genetic algorithm  agent system

中文摘要

      将遗传算法的编码方式与智能体系统的演化结构相结合, 提出一种求解多阶段多产品调度问题的链式智能体遗传算法. 算法采用基于订单序列的编码方式, 采用一种新的后向指派规则实现编码和可行调度间的一一对应. 通过各智能体与其邻域环境的竞争与合作以及自身的自学习操作实现种群的演化过程. 对多阶段多产品调度问题的仿真结果表明: 链式智能体遗传算法与新的后向指派规则相结合, 不仅增加了种群多样性, 而且提高了算法的收敛性能, 是求解多阶段多产品调度问题的有效算法.

英文摘要

      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.