考虑扰动事件的机械加工工艺参数与车间动态调度综合节能优化

随着能源消耗和环境问题的不断加剧,机械加工车间的高效节能生产越来越受到制造业的关注。传统动态调度优化时每道工序的工艺参数固定,未考虑工艺参数与车间调度之间的关联关系,限制了调度优化的潜力。为了更好地实现柔性作业车间节能增效,并快速有效地应对车间生产过程中出现的突发扰动事件,提出一种考虑扰动事件的加工工艺参数与车间动态调度综合优化方法。首先详细分析订单插入与机床故障下柔性作业车间的能耗特性,以总能耗与最大完工时间为目标,建立工艺参数与动态调度综合优化模型,然后设计一种面向扰动事件的动态决策机制,并提出改进的自适应形状估计进化算法(AGE-MOEA)进行优化求解,最后通过案例分析与算法对比,验证了所提出方法的有效性。     

Hybridizing tabu search with ant colony optimization for solving job shop scheduling problems

The manufacturing industry continues to be a prime contributor and it requires an efficient schedule. Scheduling is the allocation of resources to activities over time and it is considered to be a major task done to improve shop-floor productivity. Job shop problem comes under this category and is combinatorial in nature. Research on optimization of the job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the global optimization technique called tabu search that is combined with the ant colony optimization technique to solve the job shop scheduling problems. The neighborhoods are selected based on the strategies in the ant colony optimization with dynamic tabu length strategies in the tabu search. The inspiring source of ant colony optimization is pheromone trail that has more influence in selecting the appropriate neighbors to improve the solution. The performance of the algorithm is tested using well-known benchmark problems and is also compared with other algorithms in the literature.     

A dispatching rule-based hybrid genetic algorithm focusing on non-delay schedules for the job shop scheduling problem

Job shop scheduling is an important decision process in contemporary manufacturing systems. In this paper, we aim at the job shop scheduling problem in which the total weighted tardiness must be minimized. This objective function is relevant for the make-to-order production mode with an emphasis on customer satisfaction. In order to save the computational time, we focus on the set of non-delay schedules and use a genetic algorithm to optimize the set of dispatching rules used for schedule construction. Another advantage of this strategy is that it can be readily applied in a dynamic scheduling environment which must be investigated with simulation. Considering that the rules selected for scheduling previous operations have a direct impact on the optimal rules for scheduling subsequent operations, Bayesian networks are utilized to model the distribution of high-quality solutions in the population and to produce the new generation of individuals. In addition, some selected individuals are further improved by a special local search module based on systematic perturbations to the operation processing times. The superiority of the proposed approach is especially remarkable when the size of the scheduling problem is large.     

基于遗传-粒子群混合算法的柔性作业车间多资源调度问题

在传统柔性作业车间调度问题(FJSP)中加入运输和装配环节,提出一种柔性作业车间多资源调度问题(MRFJSP),以完工时间最短为目标建立了包含加工、运输和装配的柔性作业车间调度模型。为了提高传统遗传算法(GA)在车间调度问题中的寻优能力,将粒子群算法(PSO)的寻优过程进行改进并与遗传算法进行结合,提出一种带保优策略的遗传-粒子群混合算法,利用单层编码对模型进行求解。通过算例验证了模型的可行性,并将提出的混合算法与遗传算法和粒子群算法进行比较,证明了混合算法的优越性。     

基于过滤定向搜索的Job-Shop调度算法及评价

对以Makespan最小为目标的Job Shop调度问题进行了研究。首先对Job Shop调度问题进行了描述,在此基础上建立了一种求解Job Shop调度问题的启发式优化算法———基于过滤定向搜索的算法,同时结合实例对算法的优化过程作了具体描述。最后通过不同规模的Benchmark实例对该算法进行了仿真评价,结果表明基于过滤定向搜索的算法搜索效率高,解的性能好,是一种有效的优化算法。     

多目标柔性作业车间调度决策精选机制研究

针对多目标柔性作业车间调度优化无法找到唯一最优解的问题，提出多目标遗传算法和层次分析法模糊综合评判的分阶段优化策略。提出优化阶段和精选阶段的优化任务，优化阶段选出一组Pareto解集，精选阶段从Pareto解集中选出最优解；在精选阶段运用层次分析法和模糊评判集成的策略精选调度决策。决策算例证明提出的方法是可行的，可很好地帮助决策者选择出一个最满意的解。     

考虑工序相关性的动态Job shop调度问题启发式算法

提出一类考虑工序相关性的、工件批量到达的动态Job shop 调度问题,在对工序相关性进行了定义和数学描述的基础上,进一步建立了动态Job shop 调度问题的优化模型。设计了一种组合式调度规则RAN(FCFS,ODD),并提出了基于规则的启发式算法以及该类动态Job shop 调度问题的算例生成方法。为验证算法和比较评估调度规则的性能,对算例采用文献提出的7种调度规则和RAN(FCFS,ODD)进行了仿真调度,对调度结果的分析表明了算法的有效性和RAN(FCFS,ODD)调度规则求解所提出的动态Job Shop 调度问题的优越性能。     

工艺计划与车间作业计划集成系统的研究

分析了现有工艺计划与车间作业计划的系统集成模型,建立了企业生产工艺计划与作业计划的层次结构,提出了面向车间作业计划的动态、分布式工艺计划与车间作业计划集成模型。将工艺计划与基于周期和事件驱动的动态作业计划相结合,把改进的离散化粒子群算法引入车间优化作业计划运算,使集成模型中生产作业计划与控制功能得以实现。实例证明了集成系统的可行性和有效性。     

Reentrant two-stage multiprocessor flow shop scheduling with due windows

Reentrant flow shop scheduling allows a job to revisit a particular machine several times. The topic has received considerable interest in recent years; with related studies demonstrating that particle swarm algorithm (PSO) is an effective and efficient means of solving scheduling problems. By selecting a wafer testing process with the due window problem as a case study, this study develops a farness particle swarm optimization algorithm (FPSO) to solve reentrant two-stage multiprocessor flow shop scheduling problems in order to minimize earliness and tardiness. Computational results indicate that either small- or large-scale problems are involved in which FPSO outperforms PSO and ant colony optimization with respect to effectiveness and robustness. Importantly, this study demonstrates that FPSO can solve such a complex scheduling problem efficiently.     

基于混合遗传鲸鱼优化算法的柔性作业车间自动导引车融合调度方法

针对柔性作业车间调度问题,考虑自动导引车(AGV)在车间制造过程中只参与装卸和搬运工作,提出一种实现AGV路径规划与柔性作业车间调度集成优化的融合调度模型。采用基于工序排序与机器选择两个子问题的二维向量编码方案,并在解码过程中提出基于最先服务原则的AGV安排策略。对鲸鱼优化算法进行离散化改进,针对性地设计了多种种群初始化策略,引入遗传算法的交叉、变异操作以提升鲸鱼优化算法的全局搜索能力,并嵌入局部搜索算法以达到全局搜索和局部搜索的平衡,构建了一种混合遗传鲸鱼优化算法（HGWOA）来求解该融合调度模型。通过经典测试算例验证了算法性能,并使用正交试验优化了算法参数。研究结果表明,HGWOA算法用于求解柔性作业车间AGV融合调度问题可以获得较好的效果。     

分段式车间作业调度算法

车间作业调度问题是制造系统运筹技术、管理技术与优化技术发展的核心。本文对离散作业型 (Job Shop)车间中的作业调度问题做了探讨 ,并根据离散作业调度的阶段性提出了基于作业状态空间的逐段式车间作业调度算法。通过对一个实际车间作业调度仿真比较 ,此算法运算速度比最短加工时间 (SPT)和最少工作量剩余 (L WR)算法快 ,其调度结果具有可执行性     

变扰动强度下柔性装配作业车间调度性能分析

柔性装配作业车间是柔性作业车间的一类现实化扩展,其调度问题既要考虑复杂的加工路径柔性,还要考虑零件间的装配关联约束,以及由其带来的关联零件生产进度协同难题。首先给出了柔性装配作业车间调度问题的数学模型;然后考虑现实生产中普遍存在的随机扰动,采用了完全反应式与预测-反应式两类动态调度策略,并提出了相应的优先度规则算法和周期性滚动遗传算法。前者能快速协同各关联任务,但其决策分散,缺乏全局优化力度;后者进行全局周期决策,但扰动将导致性能的下降。最后构建了一般化的仿真模型,并设计了大量的比较实验,分析了不同综合扰动强度对两种调度策略的影响,为实际生产调度策略选择提供了有效的依据。     

基于混合粒子群算法具有交货期瓶颈的作业车间调度问题

为了解决一类具有交货期瓶颈的作业车间调度问题,给出了基于订单优势的交货期满意度和交货期瓶颈资源确定方法,以工件拖期加权和最小为优化目标,建立了基于交货期满意度和瓶颈资源约束的作业车间调度模型;为了求解该调度模型,设计了一种基于模拟退火的混合粒子群算法,该算法采用随机工序表达方式进行编码,并在模拟退火算法中引入变温度参数来提高算法效率。通过随机仿真,分别采用PSO-SA、SA和PSO对所建立的调度模型进行求解,结果显示PSO-SA算法的广泛性好、求解效率高且算法的稳定性好,验证了模型和算法的有效性。     

多目标批量生产柔性作业车间优化调度

研究批量生产中以生产周期、最大提前/最大拖后时间、生产成本以及设备利用率指标(机床总负荷和机床最大负荷)为调度目标的柔性作业车间优化调度问题。提出批量生产优化调度策略,建立多目标优化调度模型,结合多种群粒子群搜索与遗传算法的优点提出具有倾向性粒子群搜索的多种群混合算法,以提高搜索效率和搜索质量。仿真结果表明,该模型及算法较目前国内外现有方法更为有效和合理。最后,从现实生产实际出发给出多目标批量生产柔性调度算例,结果可行,可对生产实践起到一定的指导作用。     

A rolling horizon job shop rescheduling strategy in the dynamic environment

In this paper, the job shop scheduling problem in a dynamic environment is studied. Jobs arrive continuously, machines breakdown, machines are repaired and due dates of jobs may change during processing. Inspired by the rolling horizon optimisation method from predictive control technology, a periodic and event-driven rolling horizon scheduling strategy is presented and adapted to continuous processing in a changing environment. The scheduling algorithm is a hybrid of genetic algorithms and dispatching rules for solving the job shop scheduling problem with sequence-dependent set-up time and due date constraints. Simulation results show that the proposed strategy is more suitable for a dynamic job shop environment than the static scheduling strategy.     

Application of chance-constrained programming for stochastic group shop scheduling problem

In this paper, we study a group shop scheduling (GSS) problem subject to uncertain release dates and processing times. The GSS problem is a general formulation including the other shop scheduling problems such as the flow shop, the job shop, and the open shop scheduling problems. The objective is to find a job schedule which minimizes the total weighted completion time. We solve this problem based on the chance-constrained programming. First, the problem is formulated in a form of stochastic programming and then prepared in a form of deterministic mixed binary integer linear programming such that it can be solved by a linear programming solver. To solve the problem efficiently, we develop an efficient hybrid method. Exploiting a heuristic algorithm in order to satisfy the constraints, an ant colony optimization algorithm is applied to construct high-quality solutions to the problem. The proposed approach is tested on instances where the random variables are normally, uniformly, or exponentially distributed.     

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     

Scheduling jobs under constant period-by-period resource availability to maximize project profit at a due date

In this paper, we propose a lump-sum payment model for the resource-constrained project scheduling problem, which is a generalization of the job shop scheduling problem. The model assumes that the contractor will receive the profit of each job at a predetermined project due date, while taking into account the time value of money. The contractor will then schedule the jobs with the objective of maximizing his total future net profit value at the due date. This proposed problem is nondeterministic polynomial-time (NP)-hard and mathematically formulated in this paper. Several variable neighborhood search (VNS) algorithms are developed by using insertion move and two-swap to generate various neighborhood structures, and making use of the well-known backward–forward scheduling, a proposed future profit priority rule, or a short-term VNS as the local refinement scheme (D-VNS). Forty-eight 20-job instances were generated using ProGen and optimally solved with ILOG CPLEX. The performances of these algorithms are evaluated based on the optimal schedules of the 48 test instances. Our experimental results indicate that the proposed VNS algorithms frequently obtain optimal solutions in a short computational time. For larger size problems, our experimental results also indicate that the D-VNS with forward direction movement outperforms the other VNS algorithms, as well as a genetic algorithm and a tabu search algorithm.     

模具加工过程的面向对象分析及其动态车间作业计划建模

模具的生产属于单件订货型生产,其车间级的生产管理一直是模具企业管理的难点。车间作业计划是解决这一难点的有效措施之一。通过采用面向对象的方法,对模具企业生产过程中与车间作业计划有关的对象进行了详细地分析和描述,以此为基础,进一步分析一模具企业车间作业计划中的优化目标和约束,建立了相应的动态车间作业计划模型,为模具企业编制切实可行的车间作业计划奠定了基础。     

Minimizing makespan in a group shop with fuzzy release dates and processing times

This paper deals with a fuzzy group shop scheduling problem. The group shop scheduling problem is a general formulation that includes the flow shop, the job shop, and the open shop scheduling problems. Job release dates and processing times are considered to be triangular fuzzy numbers. The objective is to find a job schedule that minimizes the maximum completion time or makespan. First, the problem is formulated in a form of fuzzy programming and then prepared in a form of deterministic mixed binary integer linear programming by applying the chance-constrained programming. To solve the problem, an efficient genetic algorithm hybridized with an improvement procedure is developed. Both Lamarckian and Baldwinian versions are then implemented and evaluated through computational experiments.