摘要
为了求解多目标多生产线调度问题,结合PSO和GA算法的特点,提出了基于协同进化思想的多种群PSOGA混合优化算法(简称MC-HPSOGA)。以最小化最大完工时间、最大化生产线利用率和最大化客户满意度为目标函数,建立了多生产线作业协调调度问题的多目标批量调度数学模型,并且设计最小批量动态分批策略,将MC-HPSOGA算法应用于BSPT公司角磨机装配线的多目标多生产线调度问题实例中,通过与PSO和GA算法的比较,验证了算法和模型的有效性。
In order to solve the multi-objective multi-line scheduling problem,multi-population cooperative PSOGA hybrid optimization algorithm（MC-HPSOGA） was developed through combining PSO and GA algorithm based on the theory of co-evolutioa Considering minimized makespan,maximized production efficiency and maximized customer satisfaction as the objectives,a multi-objective batchscheduling mathematical model was established for multi-line optimal scheduling problem.After that,the MC-HPSOGA algorithm was applied in the multi-objective multi-line scheduling case of the angle grinder assembly-line in BSPT company.And minimum-batch dynamic-partial strategy was designed in this application.Finally,the effectiveness of the algorithm and the model was verified through the comparison with PSO and GA algorithm.
出处
《工业工程与管理》
北大核心
2011年第6期42-49,共8页
Industrial Engineering and Management
基金
国家自然科学基金资助项目(70971118)
浙江省自然科学基金资助项目(Y607456
Y6090475)
关键词
多生产线调度
批量调度
粒子群算法
遗传算法
混合优化算法
multi-line scheduling
batch scheduling
pso algorithm
ga algorithm
hybrid optimization algorithm
