基于人工蜂群算法的柔性工艺与车间调度集成优化

为实现柔性工艺与车间调度集成优化,在考虑工件特征的加工工艺、次序及加工机器的柔性基础上,以最小化最大完工时间为优化目标,提出一种基于交叉变异的人工蜂群算法。该算法针对柔性工艺与车间调度集成问题的离散性特征,对工艺路线进行序列编码,工件调度采用基于工序的编码方式。通过工艺种群与调度种群的交叉变异操作,分别使采蜜蜂及观察蜂进行局部寻优,侦查蜂进行全局寻优,以此提高算法性能。在此基础上用两部分测试实例分别验证了集成研究的必要性及改进算法的有效性。     

基于免疫和模拟退火原理的柔性JobShop调度研究*

为了研究柔性Job-Shop调度的不同解法,采用免疫和模拟退化原理求解柔性Job-Shop调度问题。研究了柔性处理问题,提出两种调度策略;分析了算法混合的思想,提出了免疫模拟退火算法。分别采用不同调度策略,使用不同调度算法对多种国际标准算例进行了仿真,仿真结果表明,该模型、策略和算法能够解决柔性Job-Shop调度问题。     

求解低碳调度问题的改进型候鸟优化算法

针对柔性作业车间,建立一种以能耗最小化为目标的数学模型,解决低碳策略下的该车间内的作业调度问题。对于上述模型,提出一种改进型候鸟优化（Improved Migrating Birds Optimization,IMBO）算法进行求解。结合全局搜索、局部搜索和随机规则三种方式初始化种群,确保算法的求解质量和收敛速度。采用两种有效的邻域结构构造个体的邻域解,并在此基础上设计一种局部搜索方法增强算法的局部寻优能力。此外,引入一种跳跃机制避免算法陷入早熟收敛状态。通过大量计算结果验证了模型和算法的可行性和有效性。     

柔性作业车间调度分析及其启发式算法

针对多目标柔性作业车间调度问题,基于甘特图和搭积木经验进行了分析,提出了一种组合优先规则和基于此优先规则的启发式算法。组合优先规则面向完工时间、关键机床负荷和总负荷三个指标,改变规则中各数据项的比例可调整三个指标所占的比例。算法采用随机方式调整三个指标的比例,并微调最优解对应的比例,能随机产生多个高质量调度解。对比测试表明,算法求解质量更高,运行速度快,稳定,可直接用于在其他调度算法中产生初始解,或者用于动态调度。     

An integrated job shop scheduling and assembly sequence planning approach for discrete manufacturing

This paper proposes an integrated job shop scheduling and assembly sequence planning (IJSSASP) approach for discrete manufacturing, enabling the part processing sequence and assembly sequence to be optimized simultaneously. The optimization objectives are to minimize the total production completion time and the total inventory time of parts during production. The interaction effects between the job shop schedule and the assembly sequence plan in discrete manufacturing are analyzed, and the mathematical models including the objective functions and the constraints are established for IJSSASP. Based on the above, a non-dominated sorting genetic algorithm-II (NSGA-Ⅱ) with a hybrid chromosome coding mechanism is applied to solve the IJSSASP problem. Through the case studies and comparison tests for different scale problems, the proposed IJSSASP approach is verified to be able to improve the production efficiency and save the manufacturing cost of the discrete manufacturing enterprise more effectively.     

基于遗传算法的双目标作业调度优化

提出了一种双向调度遗传算法，用于解决多工艺路线的以缩小生产周期和工件准时交货为目标的作业调度问题．双向调度就是采用倒排法调度关键工件，使其准时交货；采用顺排法调度其余工件，利用剩余加工资源，使其尽旱完工．双向调度遗传算法就是用遗传算法搜索双向调度的最优解．设计了一种新的染色体编码方法，该方法直接把工艺路线和工序映射成字符串．并对该编码的交叉算子和变异算子进行了研究．仿真结果表明，该算法是可行的，在生产中有一定的优越性。     

A hybrid immune simulated annealing algorithm for the job shop scheduling problem

A hybrid simulated annealing algorithm based on a novel immune mechanism is proposed for the job shop scheduling problem with the objective of minimizing total weighted tardiness. The proposed immune procedure is built on the following fundamental idea: the bottleneck jobs existing in each scheduling instance generally constitute the key factors in the attempt to improve the quality of final schedules, and thus, the sequencing of these jobs needs more intensive optimization. To quantitatively describe the bottleneck job distribution, we design a fuzzy inference system for evaluating the bottleneck level (i.e. the criticality) of each job. By combining the immune procedure with a simulated annealing algorithm, we design a hybrid optimization algorithm which is subsequently tested on a number of job shop instances. Computational results for different-sized instances show that the proposed hybrid algorithm performs effectively and converges fast to satisfactory solutions.     

考虑多目标的柔性工艺与调度集成优化算法

针对工艺规划与车间调度集成优化问题,在考虑零件的加工工序柔性、工序次序柔性及加工机器柔性的基础上,以最大完工时间、总加工成本和总拖期时间为优化目标,对多目标柔性工艺与车间调度集成问题建模,提出一种基于改进人工蜂群算法的多目标柔性工艺与车间调度集成优化策略,并提出邻域变异操作以及全局交叉操作,对种群进行更新。引入Pareto方法,通过对适应度评价、贪婪准则、Pareto最优解集构造和保存以及解得多样性维护等方面进行改进,设计了一种基于Pareto方法的多目标人工蜂群算法。最后,通过采用基本人工蜂群算法及改进人工蜂群算法对六个工件、五台机床的柔性工艺与车间调度集成问题进行优化,验证了改进算法的有效性。     

A multi-objective approach to the application of real-world production scheduling

An initiative was introduced in one of the production facilities of Germany’s E.G.O. Group in order to enhance its SAP information system with a custom-made application for production-scheduling optimization. The goal of the optimization is to find a production schedule that satisfies different, contradictory production and business constraints. We show the challenges faced in the application of the multi-objective optimization approach, which is gaining influence in the management of production scheduling. We implement a memetic version of the Indicator-Based Evolutionary Algorithm with customized reproduction operators and local search procedures to find a set of feasible, non-dominated solutions. Such a memetic algorithm was applied to two real order lists from the production company. Additionally, we also lay out an efficient presentation of the multi-objective results for an expert’s support in decision making. This provides the management with the possibility to gain additional insights into how the production schedule dynamically reacts to changes in the decision criteria. We show that the multi-objective approach is able to find high-quality solutions, which enables flexibility when it comes to quickly adapting to specific business conditions.     

批量生产柔性作业车间多目标精细化调度方法

提出了一种批量生产柔性作业车间多目标精细化调度方法。针对批量生产柔性作业车间多目标调度问题特点,建立了一类以完工时间最短和制造成本最低为优化目标的等量分批柔性作业车间调度多目标优化模型。提出了5种批量生产柔性作业车间精细化调度技术;设计了一种改进的NSGA II算法对模型进行求解。算法中引入面向对象技术处理复杂的实体逻辑关系,使用矩阵编码技术进行编码,采用分段交叉和分段变异的遗传算子实现遗传进化,应用上述5种精细化调度技术于解码过程以提高设备利用率。通过案例分析验证了该方法的有效性。     

融入概率学习的混合差分进化算法求解绿色分布式可重入作业车间调度

本文针对绿色分布式可重入作业车间调度问题(GDRJSSP), 提出一种融入概率学习的混合差分进化算法 (HDE PL), 以实现最大完工时间和总能耗最小. 根据GDRJSSP的问题特点, 设计编码和解码规则, 并采用差分进化 算法执行全局搜索来发现优质解区域. 为能更明确地引导全局搜索方向, 设计基于贝叶斯网络结构的多维概率模型 合理学习和积累优质解(即当前种群中的较优解)的模式信息. 结合问题解的结构特征, 提出基于关键路径的4种邻 域结构来构造局部搜索, 并设计基于非关键路径的节能策略来提升算法获取低能耗非劣解的能力. 仿真实验和算 法对比验证了HDE PL可有效求解GDRJSSP.     

柔性作业调度的串并行模型对比与求解

作业处理中的柔性使得作业调度更为灵活,作业中操作的执行顺序满足拓扑排序是作业调度的前提。是否允许没有优先关系的操作在不同的机器上同时执行是区分串行和并行调度的条件。文中以共生进化算法求解一个复杂的作业调度模型为例,给出了算法实现串行调度和并行调度的具体区别,并给出了串行和并行调度的结果。结果表明,并行相对于串行对算法效率的提高与柔性大小相关,与作业的规模成反比。     

A cutting plane approach for integrated planning and scheduling

In this paper we propose a branch-and-cut algorithm for solving an integrated production planning and scheduling problem in a parallel machine environment. The planning problem consists of assigning each job to a week over the planning horizon, whereas in the scheduling problem those jobs assigned to a given week have to be scheduled in a parallel machine environment such that all jobs are finished within the week. We solve this problem in two ways: (1) as a monolithic mathematical program and (2) using a hierarchical decomposition approach in which only the planning decisions are modeled explicitly, and the existence of a feasible schedule for each week is verified by using cutting planes. The two approaches are compared with extensive computational testing.     

多目标柔性作业调度的优化研究

针对以生产周期、生产成本、设备利用率为目标的柔性作业调度问题,基于混合遗传算法提出了一种新的优化求解方法。首先建立了该类问题的调度模型,基于工序编码的染色体决定了工序调度的优先级;利用无量纲的标准化处理方法统一目标量纲;然后,利用层次分析法将多目标问题转化为单目标问题,同时为了保证算法的收敛性,在基本遗传算法框架的基础上集成了禁忌搜索算法,从而延缓或避免了早熟收敛的发生。最后通过实验仿真,证明提出的方法可以有效解决该类多目标柔性作业调度问题。     

A research survey: review of flexible job shop scheduling techniques

In the last 25 years, extensive research has been carried out addressing the flexible job shop scheduling (JSS) problem. A variety of techniques ranging from exact methods to hybrid techniques have been used in this research. The paper aims at presenting the development of flexible JSS and a consolidated survey of various techniques that have been employed since 1990 for problem resolution. The paper comprises evaluation of publications and research methods used in various research papers. Finally, conclusions are drawn based on performed survey results.     

基于新型帝国竞争算法的高维多目标柔性作业车间调度

针对高维多目标柔性作业车间调度问题(MaOFJSP),提出了一种新型帝国竞争算法(ICA)以同时最小化最大完成时间、最大拖期、最大机器负荷和总能耗,该算法采用新方法构建初始帝国使得大多数殖民国家分配数量相近的殖民地,引入殖民国家的同化,并应用新的革命策略和帝国竞争方法以获得高质量解.最后通过大量实验测试ICA新策略对其性能的影响并将ICA与其他算法对比,实验结果表明新型ICA在求解MaOFJSP方面具有较强的优势.     

考虑工件移动时间的柔性作业车间调度问题研究

分析生产车间的实际生产状况,建立了考虑工件移动时间的柔性作业车间调度问题模型,该模型考虑了以往柔性作业车间调度问题模型所没有考虑的工件在加工机器间的移动时间,使柔性作业车间调度问题更贴近实际生产,让调度理论更具现实性。通过对已有的改进遗传算法的遗传操作进行重构,设计出有效求解考虑工件移动时间的柔性作业车间调度问题的改进遗传算法。最后对实际案例进行求解,得到调度甘特图和析取图,通过对甘特图和析取图的分析验证了所建考虑工件移动时间的柔性作业车间调度问题模型的可行性和有效性。     

An evolutionary simulation-based optimization approach for dispatching scheduling

An effective scheduling decision is one of the key factors towards improving the efficiency of a system’s performance, particularly in the instance of multiple products thus dispatching rules have been widely used for real-time scheduling because they can provide a very quick and pretty good solution. However, deciding how to select appropriate rules is very difficult. In this paper, we develop evolutionary simulation-based heuristics to construct near-optimal solutions for dispatching rule allocation. Our heuristic is easy to use and gives a manager a useful tool for testing a configuration that can minimize certain performance measures. The optimization heuristics are used to determine priority strategies to maximize the performance of a complex manufacturing system with a large number of different products, along with an overtime that changes with a mix of different process types, including assembly and disassembly operations and with different types of internal and external disturbances. Modeling is carried out using discrete-event simulation. Case study analysis is of a commercial offset printing production system.     

精英进化策略求解柔性作业车间调度问题

柔性作业车间调度问题允许一道工序可以在多个可选机器上进行加工,减少了机器约束,增加了求解难度,是典型的NP难问题。结合其特点,设计了一种精英进化策略遗传算法求解柔性作业车间调度问题。提出了解阀值的指标,使得外部精英库中不仅保留算法每次迭代过程中的最优解,而且保留最优值相等而调度方案不同的解,为调度人员提供更多选择。通过制造企业中的实际案例和其它文献中的案例对提出的精英进化策略遗传算法进行了测试,结果证明提出方法的有效性。     

Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling

Traditionally, process planning and scheduling were performed sequentially, where scheduling was implemented after process plans had been generated. Considering their complementarity, it is necessary to integrate these two functions more tightly to improve the performance of a manufacturing system greatly. In this paper, a mathematical model of integrated process planning and scheduling has been formulated. And, an evolutionary algorithm-based approach has been developed to facilitate the integration and optimization of these two functions. To improve the optimized performance of the approach, efficient genetic representation and operator schemes have been developed. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show that the integrated process planning and scheduling is necessary and the proposed approach has achieved significant improvement.