CAJ | 학술논문

在云制造环境下,因制造服务资源所在地域的差异性,多目标制造工作流调度不仅考虑制造服务所需时间、费用,还需考虑产品运输所需时间、费用,原有工作流调度算法无法有效优化运输代价.针对此问题,结合遗传算法全局搜索能力强与粒子群算法收敛速度快的特点,提出多目标混合遗传粒子群(MOGA - PSO)算法.仿真结果表明混合算法能够有效降低运输代价,使得工作流调度得到进一步优化,可适用于云制造环境.
재운제조배경하,인제조복무자원소재지역적차이성,다목표제조공작류조도불부고필제조복무소수시간、비용,환수고필산품운수소수시간、비용,원유공작류조도산법무법유효우화운수대개.침대차문제,결합유전산법전국수색능력강여입자군산법수렴속도쾌적특점,제출다목표혼합유전입자군(MOGA - PSO)산법.방진결과표명혼합산법능구유효강저운수대개,사득공작류조도득도진일보우화,가괄용우운제조배경.
@@@@In cloud manufacturing environment ,for the geographical distribution of resources ,the scheduling for manufacturing workflow with multiple goals should consider not only the cost of resources ,but also the cost of product transportation .A hybrid algorithm is proposed to solve the problem which took the advantage of global optimization with genetic algorithm and fast convergence with particle swarm algorithm .Compared to the results of normal GA and PSO ,simulation results show that the hybrid algorithm is an effective method for manufacturing workflow scheduling .