可重构流水线构形选择和作业调度集成优化

Integrated optimization of configuration selection and job scheduling for reconfigurable flowlines

针对可混流生产同零件族多种零件的可重构流水线(RFL),以最小化生产周期总成本和最小化拖期惩罚为目标,建立了RFL构形选择和作业调度集成优化的混合整数多目标规划数学模型.采用LINGO软件进行案例计算验证了模型的正确性.随后,融合拥挤距离计算和外部Pareto解集档案构建技术提出了一种快速获取集成优化问题Pareto解集的多目标粒子群算法(Mo PSO).在Mo PSO中,采用实数和整数混合编码的粒子表征RFL的构形和调度方案,所设计的粒子编码/解码方法和更新机制可保证粒子所对应解的可行性.将Mo PSO与NSGA-Ⅱ算法应用于3个案例,通过案例计算对比验证了Mo PSO算法的有效性.计算结果表明,Mo PSO获取的非支配解的质量和计算效率均优于NSGA-Ⅱ.

To achieve the reconfigurable flow line （RFL）in which multiple parts within the same part family can be produced simultaneously,a mixed integer multi-objective programming model is developed for integrated optimization of RFL configuration selection and job scheduling.The two objectives are to minimize total cost of concerned demand period and to minimize the penalty of tardiness.The validity of the model is illustrated by solving the model of a case using LINGO softaware.Then,a multi-objective particle swarm optimization （MoPSO）is proposed to obtain a set of Pareto solutions quickly through combining the techniques of computing crowding distance and constructing external Pareto solution archive.In the MoPSO,a hybrid encoding composed of real and integer is developed to represent the configuration and scheduling of RFL.The designed encoding /decoding method along with the particle updating mechanism ensures that any particle corresponds to a feasible solution.The effectiveness of the MoPSO is verified by the comparison results between MoPSO and the NSGA-Ⅱ algorithm in three cases.The computation results also indicate that the proposed MoPSO is superior to the NSGA-Ⅱ algorithm with respect to quality of identified non-dominated solutions and computation efficiency.