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.     

可变机器约束的模糊作业车间调度问题研究

在车间实际加工中,工件的加工时间和交货期是一个模糊数,而且工件的某道工序有多台机器可供选择。针对这类作业的车间调度,提出了以极大化最小客户满意度为指标的可变机器约束的模糊作业车间调度模型,并给出了算法设计。应用遗传算法在适应度函数处理中引入模糊数处理方法,解决作业车间模糊调度问题,实现调度优化。仿真实验结果表明了该调度方法的有效性,为可变机器约束的模糊作业车间调度提供了一种实现途径。     

Group shop scheduling with uncertain data and a general cost objective

In this paper, a stochastic group shop scheduling problem with a due date-related objective is studied. The group shop scheduling problem provides a general formulation including two other shop scheduling problems, the job shop and the open shop. Both job release dates and processing times are assumed to be random variables with known distributions. Moreover, earliness and tardiness of jobs are penalized at different rates. The objective is to minimize the expected maximum completion cost among all jobs. A lower bound on the objective function is proposed, and then, a hybrid approach following a simulation optimization procedure is developed to deal with the problem. An ant colony optimization algorithm is employed to construct good feasible solutions, while a discrete-event simulation model is used to estimate the performance of each constructed solution that, taking into account its lower bound, may improve the best solution found so far. The proposed approach is then evaluated through computational experiments.     

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。     

面向IC生产模式的Job Shop调度问题的研究

针对即时定制生产模式的车间调度的特点,提出基于粒子群算法(PSO)的车间调度问题的解决方案.利用粒子群算法本身的优越性解决复杂的车间作业排序问题,克服了传统调度算法存在寻优效率低或全局寻优能力差的弱点.对粒子群的编码及寻优操作进行研究,确定了更适合车间调度问题的编码和操作方式,并将算法进行编程,应用到系统的车间调度部分.仿真结果表明,通过设置适当的参数,可以快速地得到理想的排序结果,能够适用于IC生产模式的车间调度问题.     

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。     

Dynamic production planning model: a dynamic programming approach

Production planning is one of the most important issues in manufacturing. The nature of this problem is complex and therefore researchers have studied it under several and different assumptions. In this paper, applied production planning problem is studied in a general manner and it is assumed that there exists an optimal control problem that its production planning strategy is a digital controller and must be optimized. Since this is a random problem because of stochastic values of sales in future, it is modeled as a stochastic dynamic programming and then it is transformed to a linear programming model using successive approximations. Then, it is proved that these two models are equivalent. The main objective of the proposed model is achieving optimal decisions using forecasting sales which can be applied in master production schedule, manufacturing resource planning, capacity requirements planning, and job shop/shop floor scheduling.     

Minimizing makespan in a single-machine scheduling problem with a learning effect and fuzzy processing times

In scheduling problems with learning effects, most research assumes that processing times are deterministic. This paper studies a single-machine scheduling problem with a position-based learning effect and fuzzy processing times where the objective is to minimize the makespan. The position-based learning effect of a job is assumed to be a function of its position. The processing times are considered to be triangular fuzzy numbers. Two different polynomial-time algorithms are developed for the problem. The first solution methodology is based on the fuzzy chance-constrained programming, whereas the second is based on a method to rank fuzzy numbers. Computational experiments are then conducted in order to evaluate the performance of the algorithms.     

Scheduling fuzzy job shop with preventive maintenance through swarm-based neighborhood search

Fuzzy job shop scheduling problem with preventive maintenance (FJSSP-PM) is seldom investigated. In this paper, FJSSP-PM with n resumable jobs processed on m machines are considered and an efficient swarm-based neighborhood search (SNS) is proposed, in which an ordered operation-based representation and the decoding procedure incorporating PM are given. Swap operation and binary tournament selection are applied to update the swarm of SNS for the best solutions of the problem. It is also proved that most of possible actual completion times lie in the cut of fuzzy completion time for each job. SNS is finally compared with some methods from literature and computational results demonstrate that SNS has promising advantage on fuzzy scheduling with PM.     

Pareto archive particle swarm optimization for multi-objective fuzzy job shop scheduling problems

This paper addresses multi-objective job shop scheduling problems with fuzzy processing time and due-date in such a way to provide the decision-maker with a group of Pareto optimal solutions. A new priority rule-based representation method is proposed and the problems are converted into continuous optimization ones to handle the problems by using particle swarm optimization. The conversion is implemented by constructing the corresponding relationship between real vector and the chromosome obtained with the new representation method. Pareto archive particle swarm optimization is proposed, in which the global best position selection is combined with the crowding measure-based archive maintenance, and the inclusion of mutation into the proposed algorithm is considered. The proposed algorithm is applied to eight benchmark problems for the following objectives: the minimum agreement index, the maximum fuzzy completion time and the mean fuzzy completion time. Computational results demonstrate that the proposal algorithm has a promising advantage in fuzzy job shop scheduling.     

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。     

GA based heuristic for the open job shop scheduling problem

Open job shop scheduling is a kind of job shop scheduling in which operations can be performed in any order. In this paper an attempt is made to develop a heuristic for the open job shop scheduling problem using genetic algorithm to minimize makespan. Genetic algorithm operators are suitably modified to maintain feasibility. The results are statistically compared and found to be significantly better than the earlier reported results.     

具有柔性加工路径的作业车间批量调度优化研究

古典作业车间调度问题已经被研究了几十年并证明为 NP- hard问题。柔性作业车间调度是古典作业车间调度问题的扩展 ,它允许工序可以由一个机床集合中的多台机床完成加工 ,调度的目的是将工序分配给各机床 ,并对各机床上的工序进行排序以使完成所有工序的时间最小化。本文采用遗传算法进行柔性作业车间调度研究 ,针对柔性作业车间问题提出了一种新颖直观的基因编码方法以适用于批量调度 ,并分析了几种批量调度方案 ,最后给出了这些调度的仿真结果 ,证明单件最佳调度不适合扩展成批量最佳调度     

基于效率函数的双资源约束作业车间调度算法

目前对制造业领域作业车间调度问题研究中,仅考虑单一设备资源的传统作业车间调度问题已不能反应出实际作业车间的作业环境。根据实际作业车间多资源环境,建立了分别最小化生产完工期和超期时数为目标函数的设备和模具双资源约束作业车间调度模型,并采用基于效率函数的逆序调度算法对问题进行求解。最后,通过实例对算法进行验证并给出对比结果。     

Lot streaming in a two-machine mixed shop

Most classical scheduling models overlook the fact that products are often produced in job lots and assume that job lots are indivisible single entities, although an entire job lot consists of many identical items. However, splitting an entire lot (process batch) into sublots (transfer batches) to be moved to downstream machines allows the overlapping of different operations on the same product while work needs to be completed on the upstream machine. This approach is known as lot streaming in scheduling theory. In this study, the lot streaming problem of multiple jobs in a two-machine mixed shop where there are two different job types as flow shop and open shop is addressed so as to minimize the makespan. The optimal solution method is developed for the mixed shop scheduling problem in which lot streaming can improve the makespan.     

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.     

A two-stage genetic algorithm for large size job shop scheduling problems

The majority of large size job shop scheduling problems are non-polynomial-hard (NP-hard). In the past few decades, genetic algorithms (GAs) have demonstrated considerable success in providing efficient solutions to many NP-hard optimization problems. But there is no literature available considering the optimal parameters when designing GAs. Unsuitable parameters may generate an inadequate solution for a specific scheduling problem. In this paper, we proposed a two-stage GA which attempts to firstly find the fittest control parameters, namely, number of population, probability of crossover, and probability of mutation, for a given job shop problem with a fraction of time using the optimal computing budget allocation method, and then the fittest parameters are used in the GA for a further searching operation to find the optimal solution. For large size problems, the two-stage GA can obtain optimal solutions effectively and efficiently. The method was validated based on some hard benchmark problems of job shop scheduling.     

Integrated and events-oriented job shop scheduling

When events such as rush orders or machine breakdown occurs during the production process in a job shop, the delivery date of order may be delayed. To solve this problem, an events-oriented job shop scheduling process is built, and a scheduling model integrated with an enterprise information system (EIS), including Enterprise Resource Planning (ERP), Product Data Management/Computer Aided Process Planning (PDM/CAPP), etc., is proposed and analyzed in detail. A genetic algorithm and a hybrid allocation-based method are used in scheduling. Lastly, an events-oriented job shop scheduling simulation system is presented .     

Integrated and events-oriented job shop scheduling

When events such as rush orders or machine breakdown occurs during the production process in a job shop, the delivery date of order may be delayed. To solve this problem, an events-oriented job shop scheduling process is built, and a scheduling model integrated with an enterprise information system (EIS), including Enterprise Resource Planning (ERP), Product Data Management/Computer Aided Process Planning (PDM/CAPP), etc., is proposed and analyzed in detail. A genetic algorithm and a hybrid allocation-based method are used in scheduling. Lastly, an events-oriented job shop scheduling simulation system is presented.     

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。