基于多目标决策的车间作业调度算法研究

为解决具有多目标约束的Job-shop问题,提出了一种利用模糊综合评判规则和优先分配启发式算法相结合的调度算法.首先,用层次分析法给出各评判目标的评价权重;再由模糊综合评判规则确定目标权重下零件各工序所用的加工机床;最后,利用优先分配启发式调度算法确定在同一台机床上加工的各零件的先后顺序.实验结果证明了算法的有效性.     

基于订单的多任务厂多目标决策的生产作业算法研究

为解决订单在多个工厂间的生产作业问题,提出了一种利用模糊综合评判规则和优先分配启发式算法相结合的作业算法;首先通过层次分析法给出各评判目标的评价权重,再由模糊综合评判规则确定目标权重下各工厂的生产任务分配,最后利用优先分配启发式算法,确定订单在工厂生产的先后顺序,实验结果证明了算法的有效性。     

具有工件约束的模具制造优化调度算法研究

为解决具有工件约束的模具制造优化调度问题,提出了一种利用蚁群算法和优先分配启发式调度算法相结合的调度算法。该算法能够方便地描述问题的约束条件的特点。首先,由蚁群算法确定模具零件各工序所用的加工机床,用节点模式下的有向图描述问题的解空间,用蚂蚁种子信息素踪迹更新策略对信息素进行更新,以获得问题的解;然后,利用优先分配启发式调度算法确定在同一台机床上加工的各零件的先后顺序。实验结果验证了算法的有效性。     

基于蚁群算法的模具制造动态调度研究

为解决模具制造动态调度问题,建立了动态调度系统。该系统利用蚁群算法和优先分配启发式算法相结合的调度算法,解决具有工件约束的模具零件的调度问题。该算法首先由蚁群算法确定模具零件各工序所用加工机床,然后利用优先分配启发式算法确定在同一台机床上加工的各零件的先后顺序。考虑动态调度的实时性,提出了局部更新和全局更新相结合的、基于滑动窗口机制的动态调度方法。对于发生频率高但对调度计划执行影响不大的扰动事件采用局部更新策略,反之则采用全局更新策略,在保证获得近优解的同时提高了动态调度的效率。     

一种新型的作业车间调度算法的研究与实现

将工件的剩余加工时间分为相对剩余加工时间和绝对剩余加工时间 ,提出了优先分配启发式算法的一种新的优先分配规则 ,即相对剩余加工时间最大的概念 ,把调度分成多个阶段的部分调度 ,通过比较部分调度集合中的可调度工序的相对剩余加工时间 ,求解出每个部分调度的最优解 ,从而使整个调度达到全局最优或近似最优。最后 ,开发出调度软件 ,验证了算法在工程中的可行性、有效性。     

一种基于模糊综合评判的设计方法

提出了一种新的公差分配方法。首先利用模糊综合评判的方法确定特定制造环境下装配体中各零件公差的加工难易程度ζ,然后结合零件公差对装配功能要求的敏感度因子ξ,建立了公差分配的数学模型,最后利用遗传算法求解最优的公差值。     

多目标模糊作业车间调度问题研究

研究了具有模糊加工时间和模糊交货期的多目标作业车间调度问题,首先给出了基于模糊优先规则的编码新方式,染色体的每一位表示在GT算法迭代过程中,对应机器上发生的某次冲突,根据该基因位对应的优先规则消除。然后设计了基于个体密集距离的多目标进化算法,该算法利用密集距离进行外部档案维护和适应度赋值。最后将多目标进化算法应用于模糊作业车间调度问题,以最大化最小一致指标和最小化模糊最大完成时间,并和其他算法比较。计算结果验证了多目标进化算法在模糊调度方面良好的搜索性能。     

求解作业车间调度问题的快速启发式算法

首先将作业车间调度问题转换为一个搭积木模型,受这个直观模型的启发,提出了一个启发式的搭积木规则,该规则综合考虑了已经搭好的积木的顶高和将要搭积木的剩余高度。基于这个规则,提出了一个求解作业车间调度问题的快速启发式算法,对国际上通用的benchmark例的模拟实验结果表明,提出的算法优于经典的优先分配启发式算法。     

工艺路线可变的双资源双目标车间调度优化

将遗传算法与启发式调度规则相结合 ,研究了工艺路线可变的双资源双目标的作业车间调度优化问题。在探讨过程中 ,不仅考虑到了每个工件有几条可行的工艺路线 ,而且考虑到了工件的调度受到机床、工人等资源的制约 ,以及在加工过程中发生的储存费用、机床的加工费用和工人的劳动费用对工件调度的影响 ,设计了以生产周期和生产成本综合优化为目标的适应度函数。启发式调度规则使该算法具有较高的局部搜索效率 ,遗传算法保证了解的全局最优性。最后给出了算例 ,并对计算结果进行了分析和讨论     

基于机床加工匹配特性的混合流水车间绿色生产调度

针对混合流水车间绿色生产过程中的设备选择和调度目标匹配问题,提出基于机床加工特性的多目标调度模型和改进遗传算法。该算法建立了混合流水车间调度的时间、能耗与成本优化模型,采用模糊隶属方法描述了机床加工特性,在遗传算法求解过程中通过机床加工特性隶属度与调度目标的权重系数匹配关系,建立了自适应的交叉、变异和优势保留策略,在每一代迭代中提高在调度目标方向上的选择压力,加速收敛。通过实例分析对比了不同算法的优化结果,从而验证了模型及算法的有效性,并提出了高效、节能、经济和综合4种调度生产模式,为混合流水车间绿色生产提供了指导。     

An efficient heuristic for scheduling batches of parts in a flexible flow system

Flexible manufacturing systems (FMSs) are a class of automated systems that can be used to improve productivity in batch manufacturing. Four stages of decision making have been defined for an FMS—the design, planning, scheduling, and control stages. This research focuses on the planning stage, and specifically in the area of scheduling batches of parts through the system.The literature to date on the FMS planning stage has mostly focused on the machine grouping, tool loading, and parttype selection problems. Our research carries the literature a step further by addressing the problem of scheduling batches of parts. Due to the use of serial-access material-handling systems in many FMSs, the batch-scheduling problem is modeled for a flexible flow system (FFS). This model explicitly accounts for setup times between batches that are dependent on their processing sequence.A heuristic procedure is developed for this batch-scheduling problem—the Maximum Savings (MS) heuristic. The MS heuristic is based upon the savings in time associated with a particular sequence and selecting the one with the maximum savings. It uses a two-phase method, with the savings being calculated in phase I, while a branch-and-bound procedure is employed to seek the best heuristic solution in phase II. Extensive computational results are provided for a wide variety of problems. The results show that the MS heuristic provides good-quality solutions.     

An interactive algorithm for multi-objective flow shop scheduling with fuzzy processing time through resolution method and TOPSIS

This paper develops a method for solving a multi-objective flow shop scheduling in a fuzzy environment where processing times are fuzzy numbers. The objective functions are designed to simultaneously minimize the makespan (completion time), the mean flow time, and the machine idle time. For each objective function, a fuzzy subset in the decision space whose membership function represents the balance between feasibility degree of constraints and satisfaction degree of the goal is defined. Then, technique for order preference by similarity to an ideal solution (TOPSIS) method finds the nondominated solution in a multiple objective state. The TOPSIS method and the interactive resolution method are integrated in the proposed method to solve the multi-objective flow shop scheduling problem. One of the new contributions of this research is combining these two methods in solving this problem. The proposed algorithm provides a way to find a crisp solution for the fuzzy flow shop scheduling in a multi-objective state. Also, the proposed method yields a reasonable solution that represents the balance between the feasibility of a decision vector and the optimality for an objective function by the interactive participation of the decision maker in all steps of decision process. Application of the proposed method to flow shop scheduling is shown with two numerical examples. The results show that the algorithm could be applied for determining the most preferable sequence by finding a nondominated solution for different degrees of satisfaction of constraints, and with regard to objective value, where processing time is fuzzy.     

基于长短期记忆近端策略优化强化学习的等效并行机在线调度方法

针对等效并行机在线调度问题,以加权完工时间和为目标,提出了一种基于长短期记忆近端策略优化（LSTM-PPO）强化学习的在线调度方法。通过设计融合LSTM的智能体记录车间的历史状态变化和调度策略,进而根据状态信息进行在线调度。设计了车间状态矩阵对问题约束和优化目标进行描述,在调度决策中引入额外的设备等待指令来扩大解空间,并设计奖励函数将优化目标分解为分步奖励值实现调度决策评价。最后基于PPO算法进行模型更新和参数全局优化。实验结果表明所提方法优于现有的几种启发式规则,并将所提算法应用于实际车间的生产调度,有效减小了加权完工时间和。     

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.     

基于层次候选集蚁群算法求解多级混流生产线动态排程问题

针对汽车企业平整化生产、降低成本的需求,建立了一种以油漆车间最少更换油漆次数、总装车间零件均衡使用以及最小化空间约束背离程度为目标的多级混流生产线动态排程问题的数学模型。然后采用了基于周期和事件驱动的动态调度机制,在考虑了多级混流生产线结构约束的情况下,提出了一种递进优化与模糊决策相结合的优化算法模型并设计了基于层次候选集蚁群优化与模糊层次分析法的多目标优化算法。最后,仿真结果说明了本文提出的策略与算法的有效性和实用性。     

A new approach to solve time–cost trade-off problem with fuzzy decision variables

Time–cost trade-off problem is one of the main aspects of project scheduling. Due to variations in the real world, usually, risks in estimation of project parameters are considerably high. Therefore, use of uncertain models, which is capable of formulating vagueness in the real world, to solve time–cost trade-off problems, gives a scheduling with more stability against environmental variations. On the other hand, crisp decision making in uncertain environment causes loss of some parts of information. This paper presents a new optimal model for time–cost trade-off problem in a fuzzy environment. In order to solve this problem, a new solution method for possibility goal programming problems is developed. The significant feature of this model is the determination of optimal duration for each activity in the form of triangular fuzzy numbers. To validate the algorithm developed here, a case study will be presented.     

Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time

This paper addresses a new mathematical model for cellular manufacturing problem integrated with group scheduling in an uncertain space. This model optimizes cell formation and scheduling decisions, concurrently. It is assumed that processing time of parts on machines is stochastic and described by discrete scenarios enhances application of real assumptions in analytical process. This model aims to minimize total expected cost consisting maximum tardiness cost among all parts, cost of subcontracting for exceptional elements and the cost of resource underutilization. Scheduling problem in a cellular manufacturing environment is treated as group scheduling problem, which assumes that all parts in a part family are processed in the same cell and no inter-cellular transfer is needed. Finally, the nonlinear model will be transformed to a linear form in order to solve it for optimality. To solve such a stochastic model, an efficient hybrid method based on new combination of genetic algorithm (GA), simulated annealing (SA) algorithm, and an optimization rule will be proposed where SA and optimization rule are subordinate parts of GA under a self-learning rule criterion. Also, performance and robustness of the algorithm will be verified through some test problems against branch and bound and a heuristic procedure.     

基于遗传算法的作业车间调度优化

车间调度问题由于具有重要的理论和实用价值吸引了很多研究者的兴趣 ,但以前的大多数研究集中在经典的作业车间调度问题 ,忽略了很多重要的因素 ,离应用尚有不少的差距。本文结合实际的生产过程 ,考虑到工件的加工受到机床、工人和机器人等资源的制约 ,并且可以有多种可行的工艺路线。提出了一种与启发式调度规则相结合的混合遗传算法 ,调度规则使该算法具有较高的局部搜索效率 ,遗传算法保证了解的全局最优性 ,算例表明该算法在求解性能和效率两方面均具有显著的优势