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作 者:陈伟嘉 刘建军[1] 钟宏扬 曾创锋 Chen Weijia;Liu Jianjun;Zhong Hongyang;Zeng Chuangfeng(School of Electro-mechanical Engineering,Guangdong University of Technology,Guangzhou 510006,China;Foshan Polytechnic,Foshan,Guangdong 528137,China)
机构地区:[1]广东工业大学机电工程学院,广州510006 [2]佛山职业技术学院,广东佛山528137
出 处:《机电工程技术》2023年第11期71-75,164,共6页Mechanical & Electrical Engineering Technology
摘 要:针对多品种小批量生产调度等具有多层决策变量、多维度约束特点的资源受限并行机调度问题,结合问题特性与Cplex中的CP Optimizer优化引擎对于复杂约束的强大表达能力,建立了基于数量约束关联多层决策变量的CP模型与基于映射关系关联多层决策变量的CP模型。两者的主要区别是,前者使用的决策变量较少但两组决策变量之间具有一定的独立性,可能会牺牲一定的求解效率但能够更灵活地搜索解空间;后者使用的决策变量较多但本质上两类变量通过映射关系一一对应,可以更高效地利用约束规划中的约束传播技术。通过对家电企业发泡车间的实际订单数据进行求解,实验结果表明两种模型在不同特征的输入数据下,各有优劣,并分析了输入数据特征与模型选择的规律,验证了两种模型求解此类问题的可行性与有效性。Aiming at the resource-constrained parallel machine scheduling problem with multi-level decision variables and multi-dimensional constraints,such as multi-variety and small-batch production scheduling,a CP model based on multi-level decision variables with quantity constraints and a CP model based on multi-level decision variables with mapping relationship is established by combining the characteristics of the problem and the powerful expression ability of CP to complex constraints in Cplex.The former essentially uses fewer decision variables but has a certain degree of independence between the two variables,which may sacrifice certain solving efficiency but be able to search solution space more flexibly.The latter uses more decision variables but the two types of variables correspond one by one through mapping relationship,which can make more efficient use of constraint propagation technology in constraint planning.Through solving the actual order data from foaming workshop of household electrical appliances enterprise,the experimental results show that the two models have advantages and disadvantages under the input data of different characteristics,and verify the feasibility and validity of the two models.
分 类 号:TH186[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]
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