摘要
针对零等待约束下多产品间歇过程的总流程时间和完工时间最小化问题,提出一种多目标离散组搜索算法求解.在采用启发式规则产生初始解的基础上,通过发现者、追随者和巡逻者的操作设计,算法不断更新Pareto前沿,同时,混合了基于插入邻域的多目标局部搜索方法.大量计算实验表明,所提出的算法获得的非支配解集在IGD和Set Coverage指标上优于非支配排序遗传算法和模拟退火算法,可为多目标决策者提供更好的决策依据,利于间歇生产过程的优化运行.
The multi-objective scheduling problem for the multi-product batch process with no-wait constraint is considered with makespan and total flow time minimizations, and a multi-objective discrete group search optimizer is proposed.The algorithm employs a well-known heuristic to generate initial solutions, and updates a non-dominated solution set by operators of producers, scroungers and rangers.Besides, an insertion-based local search procedure is hybridized.A bunch of computational experiments reveal that the proposed algorithm yields a non-dominated solution set with better IGD and Set Coverage values than the non-dominated sorting genetic algorithm and the bi-objective multi-start simulated annealing algorithm.It is also shown that, the algorithm can provide better basis for multi-objective decision-makers, which benefits the operation optimization of batch process production.
出处
《控制与决策》
EI
CSCD
北大核心
2017年第3期474-480,共7页
Control and Decision
基金
国家自然科学基金项目(61403180
51405075
61573144)
关键词
遗传算法
生产
优化
间歇过程
多目标
genetic algorithm
production
optimization
batch process
multi-objective
