集团制造多主体外协订单任务制造资源配置

集团制造企业的外协订单任务制造资源配置过程具有多主体、多任务、多资源、多工序以及协同性的特点,增加了订单制造资源配置过程的复杂性和不确定性。针对这一问题,提出两阶段多主体外协订单任务制造资源配置方法;设计了基于成本—工期—收益的订单任务排序算法和基于PageRank的订单任务制造资源排序算法,并建立了多主体外协订单任务制造资源配置模型;运用遗传算法求解多目标下多主体外协订单任务制造资源配置模型的Pareto最优解。以某建材装备制造集团的外协订单任务制造资源配置为例,验证了所提理论方法的合理性和有效性。     

多重资源约束下的多目标U型装配线平衡

为处理因设备故障、订单变化等引起的任务量波动或生产中断问题,在关键工序设置多个并行可选设备、在生产子线设置助理,以保证装配线的生产率。针对该类问题,构建随机工时下基于资源分配的成本、效率双目标U型装配线平衡模型,并采用Benders分解法,将问题分解为设备和助理分配主问题、工序分配子问题,以降低模型求解的复杂度。提出基于Benders分解的快速非支配遗传算法,通过三层编码及解码来适应多决策变量;采用非回溯的Pareto层级构造和拥挤距离,实现种群评价与选择;提出基于概率的层次化遗传操作,以扩充邻域结构、增强寻优能力、避免局部优化。通过非支配解比率、Pareto前沿解收敛性和个体间距度量指标分析所提算法、多目标遗传算法和非支配排序遗传算法,证明算法获得了逼近Pareto最优前沿的非支配解集,且具有良好的收敛性和分布性。     

考虑客户收货顺序的“货到人”分拣系统的订单排序和客户分批优化

可以并行分拣多个客户订单的"货到人"分拣系统中,每个客户包含多个订单,客户要求按订单排序依次收货。为提高该系统的分拣效率,以最小化料箱出入库数量为目标,从订单排序和客户分批两方面进行优化。分别建立两个0-1整数规划模型解决多客户同时拣选时的订单排序优化和客户分批优化问题;针对客户分批问题,又提出种子算法和遗传算法来解决。设计试验检验了不同客户数量、客户订单数量、总品项数量时订单排序模型和客户分批算法的优化效果。试验结果表明,0-1整数规划模型优化订单排序,可提高效率约15%,具有有效性;客户分批优化方面,0-1整数规划模型、遗传算法和种子算法都可以不同程度地提高系统效率,分别适合不同问题规模和时间要求的场景。     

基于改进遗传算法的多目标装配线平衡优化研究

针对离散制造企业装配线再平衡问题,文章提出基于改进遗传算法的多目标装配线平衡优化方法.以最小化生产节拍、最大化产线平衡率和最小化平滑指数为优化目标建立装配线再平衡优化模型,并采用改进的遗传算法对平衡模型进行求解,算法基于任务排序的种群初始化方法,采用两点交叉方法,提高了算法寻优能力.文章最后以青贮机装配线实际案例验证了...     

订单优先权的置换流水线订单排序问题

不同订单对企业来说重要度不同,而传统的置换流水车间调度研究主要解决订单平等下的总完工时间最小问题。应用模糊层次分析法找到订单优先权排序,将每种订单排序与订单优先权排序进行一致程度对比,在此基础上构建了一个考虑订单优先权的置换流水车间生产调度模型,该模型能够求解综合考虑订单重要度和总完工时间的最优订单生产排序。最后运用遗传算法对该模型进行求解,并对结果进行分析,得出优先权系数w对生产调度的指导意义。     

订货型企业基于约束理论的订单排产优化研究

基于约束理论，对订货型企业的客户订单优势因素与瓶颈资源确定准则进行了分析，并引入了“虚拟订单”的概念；通过对客户订单进行分解，建立了“虚拟订单”的数据结构和优势准则；基于该准则给出了订单投产排序优化的启发式算法，并成功地进行了实践。     

求解分布式混合流水线调度问题的改进双层嵌套式遗传算法

针对分布式混合流水线生产的生产调度问题,模拟实际排产中的排产到线和排产到时的排产策略,提出了基于改进双层嵌套式遗传算法的两层优化模型。外层依据流水线分配平衡和准时交货等基本原则总体上解决生产订单在流水线之间的分配问题,内层以最小生产时间为主要目的求解流水线的生产订单生产次序问题。考虑到双层嵌套式遗传算法的时间复杂性,基于模糊逻辑理论设计了一种模糊控制器来动态调整遗传算子,并采用主动检测停止方法,提高算法效率。使用某空调工厂的实际生产数据验证了算法的可行性、计算结果的准确性及排产策略的有效性,为高级计划与排程(Advanced Planning and Scheduling,APS)中大规模复杂供应链调度问题提供了可借鉴的方法。     

基于投产数量的实数编码遗传算法在双目标主生产计划模型中的应用

应用约束理论和JIT思想建立一种双目标的主生产计划模型,利用一种基于投产数量的实数编码遗传算法对模型进行优化,方便有效地解决多品种小批量订货型制造企业订单投产方案的问题。此双目标模型及其优化方法适合多种同类型生产企业使用。     

水平型制造协作联盟订单分配多目标优化模型研究

针对水平型制造协作联盟的订单分配问题,引入了生产负荷参数,建立了最小化综合成本与生产负荷均衡的多目标优化模型。应用改进的非支配排序遗传算法对多目标优化模型进行求解,获得了Pareto最优解集。仿真计算结果表明,所提出的模型和算法能够获得满意的解。     

产品族架构设计与供应商选择的主从关联优化

针对产品族架构设计与供应商选择的关联优化问题，提出了基于主从对策的交互评价机制。根据主从决策机制建立主从关联双层规划优化模型，以产品族架构设计为主，供应商选择为从。模型上层以效用-成本比为目标，决策产品族架构设计方案。模型下层以成本为目标，决策供应商的选择。构建双层嵌套遗传算法求解模型，并添加具有产品族特色的编码处理策略。最后以客车底盘的案例对模型和遗传算法进行了验证。     

Integrating theory of constraints and particle swarm optimization in order planning and scheduling for machine tool production

Order planning and scheduling has become a significant challenge in machine tool enterprises, who want to meet various demands of different customers and make full use of existing resources in enterprises simultaneously. Based on the Theory of Constraints, a three-stage order planning and scheduling solution is proposed to optimize the whole system performance with bottleneck resources' capability as the constraints. After the identification of bottleneck resources, multicriteria priority sequencing is made with order per-contribution rate, order delivery urgency, and customer importance as the evaluation criteria, and the evaluation result deduced from the ideal point function can decide the production mode of all orders and products. Then, a PSO-based multiobjective optimization model is set up with minimizing bottleneck machines' makespan and minimizing total products' tardiness as the two objectives. Finally, the proposed solution is applied in one machine tool enterprise by integrating into Baosight MES (Manufacturing Execution System) system. In addition, some comparisons are carried out to evaluate the proposed PSO optimization method. The comparison with actual report shows that PSO can satisfy enterprise's needs better than before; the comparisons with genetic algorithm and ant colony optimization algorithms indicate that PSO is more effective than the others because of its faster convergence rate.     

A genetic-algorithm-based optimization model for scheduling flexible assembly lines

In this paper, a scheduling problem in the flexible assembly line (FAL) is investigated. The mathematical model for this problem is presented with the objectives of minimizing the weighted sum of tardiness and earliness penalties and balancing the production flow of the FAL, which considers flexible operation assignments. A bi-level genetic algorithm is developed to solve the scheduling problem. In this algorithm, a new chromosome representation is presented to tackle the operation assignment by assigning one operation to multiple machines as well as assigning multiple operations to one machine. Furthermore, a heuristic initialization process and modified genetic operators are proposed. The proposed optimization algorithm is validated using two sets of real production data. Experimental results demonstrate that the proposed optimization model can solve the scheduling problem effectively.     

遗传算法优化的汽车混流装配线投产排序决策

采用遗传算法,以车辆完成装配的总等待时间最少为目标,对汽车混流装配线进行投产排序优化,通过AutoMod仿真软件和遗传算法优化,得出了优化方案,其目标值减少69.5％,大大提高了企业生产效率,从而证明了遗传优化算法的有效性.     

基于改进免疫遗传算法的混合车间调度研究

建立了以最大总完成时间最小为目标的混合车间调度模型。该模型包括作业车间和并行流水装配车间两部分调度问题。为降低问题求解难度,采用分解的策略对调度问题分阶段求解,并引入多Agent协商机制和模拟退火算法与免疫遗传算法相结合,提出了基于分解策略的免疫遗传算法,并通过在某汽车减振器企业的实施验证了模型和算法的有效性。     

Application of multidivisional bi-level programming to coordinate pricing and inventory decisions in a multiproduct competitive supply chain

This study concerns the coordination of pricing and inventory decisions in a multiproduct two-stage supply chain that consists of one manufacturer and multiple retailers within a competitive environment. The retailers order some substitutable products from a common manufacturer. It is assumed that channel members have different market power. The purpose of this paper is to coordinate pricing and inventory decisions such that utility of all involved levels (manufacturer and retailers) is met. Hence, a nonlinear multidivisional bi-level programming model is developed. This model considers both retailers and manufacturer when deciding about the pricing and production volume (for manufacturer) or amount of purchase (for retailers). A hybrid of genetic algorithm (GA) and local search method is proposed to solve the nonlinear bi-level model. This model is reduced to a nonlinear programming by replacing the Karush–Kuhn–Tucker (KKT) conditions of followers to the lower level of the model. Then, the obtained single-level model is relaxed to a linear model to achieve an upper bound (UB). Finally, a numerical example is presented to analyze which parameters have more effect on the price, lot size and, consequently, on the profit. Results show that increasing the market scale parameter of the manufacturer increases the profit of the manufacturer, but the market scale parameter of retailers has no effect on the manufacturer’s profit, although it increases the retailers’ profit.     

A multi-objective optimization model of the partner selection problem in a virtual enterprise and its solution with genetic algorithms

Virtual enterprise is a basic organization form to achieve agile manufacturing in a manufacturing enterprise. One of the key factors of virtual enterprise’s success is that the dominant enterprise can make the correct decision in the selection of a cooperative partner. In this paper, a multi-objective optimization model is proposed from an activity network project. Based on the concept of inefficient candidate, the solution space of the problem is first reduced. Then, an R-GA with embedded project scheduling is developed for solving the problem, where fuzzy factors-based rules are proposed in order to modify the partner selection according to different situations in the evaluation process of the genetic algorithm by using the characteristics of the considered problem and the knowledge of project scheduling. The results indicate that the proposed model and algorithm can obtain satisfactory solutions.     

基于遗传算法的多资源平衡工序排序优化决策

工序排序是生产管理中经常遇到的问题,多资源平衡工序优化是提高生产效率、降低生产成本的重要手段,至今尚未见十分有效的解法。本文建立了典型的网络计划多资源平衡工序优化的数学模型,以每道工序开工时间作为设计变量,极小化某种关键性资源需求的最大量或波动的幅度,并运用所设计的改进遗传算法对该模型进行了求解,获得了多组最优工序计划。这就使得生产调度安排灵活机动,便于智能调度。     

基于JIT的加工和装配计划集成方法及其应用

针对订单生产型企业JIT准时供货的要求,建立了加工和装配计划集成模型,该模型的目标函数是为了保证产品交货期的要求;为保证计划的可行性,约束函数包括加工能力约束、装配能力约束、加工和装配顺序约束;模型为两层混合规划模型,运用了遗传算法和启发式规则,提出了混合启发式算法。最后,针对某按订单制造型企业进行了实例应用,对产品制造过程进行了分解,采用先由装配计划得出各工件的交货期,然后根据工件的交货期确定工件的加工和装配计划。     

带准备时间的柔性流水车间多序列有限缓冲区排产优化问题

针对带准备时间的柔性流水车间多序列有限缓冲区排产优化问题,提出一种改进的紧致遗传算法（Improved compactgenetic algorithm,ICGA）与局部指派规则结合的方法来解决该问题。全局优化过程采用改进的紧致遗传算法,为了克服紧致遗传算法（Compact genetic algorithm,CGA）易早熟收敛的问题,提出一种基于高斯映射的概率模型更新方式,在保持紧致遗传算法快速收敛特性的前提下,扩展了种群中个体的多样性,增强了算法进化活力。为减少生产阻塞和降低准备时间对排产过程的影响,设计了多种局部启发式规则来指导工件进出多序列有限缓冲区的分配和选择过程。采用某客车制造企业中的实例数据进行测试,测试结果表明,改进的紧致遗传算法与局部指派规则配合使用,能够有效解决带准备时间的柔性流水车间多序列有限缓冲区排产优化问题。