A Genetic Algorithm Approach to Solving Stochastic Job-shop Scheduling Problems

This paper proposes a method for solving stochastic job-shop scheduling problems based on a genetic algorithm. The genetic algorithm was expanded for stochastic programming. In this expansion, the fitness function is regarded as representing fluctuations that may occur under stochastic circumstances specified by the distribution functions of stochastic variables. In this study, the Roulette strategy is adopted for selecting the optimum solution in terms of the expected value. Within this algorithm, it is expected that the individual that appears most frequently must give the optimum solution. The effectiveness of this approach is confimed by applying it to stochastic job-shop scheduling problems. I compare the approximately optimum solutions found by this approach with the truly or approximately optimum solutions obtained by other conventional methods, and discuss the performance and effectiveness of this approach.     

Group shops scheduling with makespan criterion subject to random release dates and processing times

This paper deals with a stochastic group shop scheduling problem. The group shop scheduling problem is a general formulation that includes the other shop scheduling problems such as the flow shop, the job shop and the open shop scheduling problems. Both the release date of each job and the processing time of each job on each machine are random variables with known distributions. The objective is to find a job schedule which minimizes the expected makespan. First, the problem is formulated in a form of stochastic programming and then a lower bound on the expected makespan is proposed which may be used as a measure for evaluating the performance of a solution without simulating. To solve the stochastic problem efficiently, a simulation optimization approach is developed that is a hybrid of an ant colony optimization algorithm and a heuristic algorithm to generate good solutions and a discrete event simulation model to evaluate the expected makespan. The proposed approach is tested on instances where the random variables are normally, exponentially or uniformly distributed and gives promising results.     

求解车间作业调度问题的一种改进遗传算法

针对标准遗传算法收敛速度慢和易陷入局部最优的问题,在总结已有经验的基础上对标准遗传算法提出改进:采用基于工序的编码、解码方式,每一次遗传操作后对种群采用循环选择并保留最优个体,对交叉操作和变异概率的计算提出了一系列改进方法,避免遗传算法产生无用解或陷入局部优化,以提高效率。通过实验验证,改进后的算法具有可行性,并且可以得到十分满意的结果。     

基于遗传算法的作业车间调度优化求解方法

针对 job shop调度问题 ,提出了一种遗传算法编码方法和解码方法。该方法根据问题的特点 ,采用一种按工序用不同编号进行的染色体编码方案 ,并采用矩阵解码方法。此编码与调度方案一一对应 ,并且该编码方案有多种交叉操作算子可用 ,无须专门设计算子。算例计算结果表明 ,该算法是有效的 ,适用于解决 job shop调度问题 ,通过比较 ,该遗传算法优化 job shop调度操作简单并且收敛速度快。     

基于混合遗传算法的车间调度问题的研究

提出了在柔性生产环境下基于遗传算法与模拟退火算法混合的动态调度算法,充分发挥遗传算法良好的全局搜索能力和模拟退火算法有效避免陷入局部极小的特性,有很好的收敛精度,并且能够在扰动发生后提供新的调度计划;通过交叉,变异等遗传操作、得到目标的最佳或次优解,最后对算法进行了仿真研究,仿真结果表明该算法是可行的,与传统的调度算法相比,其优越性是明显的。     

求解模糊Job Shop调度的遗传算法与蚁群算法融合研究

提出一种算法融合策略,解决单一算法求解模糊Job Shop调度问题存在的不足,提高这类问题的求解质量.算法融合策略中,采用遗传算法和蚁群算法进行并行搜索;根据模糊Job Shop调度问题解的特征,提出基于关键工序的邻域选择方法,并将基于这种邻域选择方法的禁忌搜索算法作为局部搜索算法,加强了遗传算法和蚁群算法的局部搜索能力.采用算法融合策略的混合优化算法对以13个难的benchmarks问题经模糊化得到实例进行求解,在较短的时间内,得到的平均满意度较并行遗传算法(PGA)提高5.24%、较TSAB算法提高8.40% .采用算法融合策略构造的混合算法具有较强的搜索能力,说明提出的混合搜索策略是有效的.     

共生进化算法母体选择策略性能研究及改进

轮盘赌在传统遗传算法中能加快进化速度和提高解质量,以共生进化算法求解一个复杂的柔性作业调度为例,跟踪共生种群进化过程。研究轮盘赌在以求得最优组合为目标的共生进化算法中对种群进化速度、种群多样性以及解质量的影响。为提高种群进化的解质量,引入了Worst策略。仿真实验表明,轮盘赌在共生进化算法中的应用不能促进解质量的提高,Worst策略能有效调节种群的进化速度并能提升解质量。     

遗传算法解柔性作业车间调度问题应用综述

柔性作业车间调度问题是典型的NP难问题,对实际生产应用具有指导作用。近年来,随着遗传算法的发展,利用遗传算法来解决柔性作业车间调度问题的思想和方法层出不穷。为了促进遗传算法求解柔性作业车间调度问题的进一步发展,阐述了柔性作业车间调度问题的研究理论,对已有改进方法进行了分类,通过对现存问题的分析,探讨了未来的发展方向。     

柔性作业车间调度问题的多目标优化算法

柔性作业车间调度问题具有解集多样化与解空间复杂的特点,传统多目标优化算法求解时容易陷入局部最优且丢失解的多样性。在建立以最大完工时间、最大能耗、机器总负荷为优化目标的柔性作业车间调度模型的情况下,提出一种改进的非支配排序遗传算法（Improved Non-dominated Sorting Genetic Algorithm II, INSGA-II）求解该模型。INSGA-II算法先将随机式初始化与启发式初始化方法混合,提高种群多样性;然后对工序部分与机器部分采用针对性的交叉、变异策略,提高算法全局搜索能力;最后设计自适应的交叉、变异算子以兼顾算法的全局收敛与局部寻优能力。在mk01～mk07标准数据集上的实验结果显示INSGA-II算法有着更优的算法收敛性与解集多样性。     

An extended Akers graphical method with a biased random‐key genetic algorithm for job‐shop scheduling

This paper presents a local search, based on a new neighborhood for the job‐shop scheduling problem, and its application within a biased random‐key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based on an extension of the 1956 graphical method of Akers, is applied to improve the solution. The new heuristic is tested on a set of 205 standard instances taken from the job‐shop scheduling literature and compared with results obtained by other approaches. The new algorithm improved the best‐known solution values for 57 instances.     

Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop

Much of the research on operations scheduling problems has ignored dynamic events in real-world environments where there are complex constraints and a variety of unexpected disruptions. Besides, while most scheduling problems which have been discussed in the literature assume that machines are incessantly available, in most real life industries a machine can be unavailable for many reasons, such as unanticipated breakdowns (stochastic unavailability), or due to a scheduled preventive maintenance where the periods of unavailability are determined in advance (deterministic unavailability). This paper describes how we can integrate simulation into genetic algorithm to the dynamic scheduling of a flexible job shop with machines that suffer stochastic breakdowns. The objectives are the minimization of two criteria, expected makespan and expected mean tardiness. An overview of the flexible job shops and scheduling under the stochastic unavailability of machines are presented. Subsequently, the details of integrating simulation into genetic algorithm are described and implemented. Consequently, problems of various sizes are used to test the performance of the proposed algorithm. The results obtained reveal that the relative performance of the algorithm for both abovementioned objectives can be affected by changing the levels of the breakdown parameters.     

A new hybrid parallel genetic algorithm for the job‐shop scheduling problem

The job‐shop scheduling problem (JSSP) is considered one of the most difficult NP‐hard problems. Numerous studies in the past have shown that as exact methods for the problem solution are intractable, even for small problem sizes, efficient heuristic algorithms must achieve a good balance between the well‐known themes of exploitation and exploration of the vast search space. In this paper, we propose a new hybrid parallel genetic algorithm with specialized crossover and mutation operators utilizing path‐relinking concepts from combinatorial optimization approaches and tabu search in particular. The new scheme relies also on the recently introduced concepts of solution backbones for the JSSP in order to intensify the search in promising regions. We compare the resulting algorithm with a number of state‐of‐the‐art approaches for the JSSP on a number of well‐known test‐beds; the results indicate that our proposed genetic algorithm compares fairly well with some of the best‐performing genetic algorithms for the problem.     

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。     

A holonic approach to flexible flow shop scheduling under stochastic processing times

Flexible flow shop scheduling problems are NP-hard and tend to become more complex when stochastic uncertainties are taken into consideration. This paper presents a novel decomposition-based holonic approach (DBHA) for minimising the makespan of a flexible flow shop (FFS) with stochastic processing times. The proposed DBHA employs autonomous and cooperative holons to construct solutions. When jobs are released to an FFS, the machines of the FFS are firstly grouped by a neighbouring K-means clustering algorithm into an appropriate number of cluster holons, based on their stochastic nature. A scheduling policy, determined by the back propagation networks (BPNs), is then assigned to each cluster holon for schedule generation. For cluster holons of a low stochastic nature, the Genetic Algorithm Control (GAC) is adopted to generate local schedules in a centralised manner; on the other hand, for cluster holons of a high stochastic nature, the Shortest Processing Time Based Contract Net Protocol (SPT-CNP) is applied to conduct negotiations for scheduling in a decentralised manner. The combination of these two scheduling policies enables the DBHA to achieve globally good solutions, with considerable adaptability in dynamic environments. Computation results indicate that the DBHA outperforms either GAC or SPT-CNP alone for FFS scheduling with stochastic processing times.     

Modified genetic algorithms for solving fuzzy flow shop scheduling problems and their implementation with CUDA

In this paper we propose an improved algorithm to search optimal solutions to the flow shop scheduling problems with fuzzy processing times and fuzzy due dates. A longest common substring method is proposed to combine with the random key method. Numerical simulation shows that longest common substring method combined with rearranging mating method improves the search efficiency of genetic algorithm in this problem. For application in large-sized problems, we also enhance this modified algorithm by CUDA based parallel computation. Numerical experiments show that the performances of the CUDA program on GPU compare favorably to the traditional programs on CPU. Based on the modified algorithm invoking with CUDA scheme, we can search satisfied solutions to the fuzzy flow shop scheduling problems with high performance.     

Job Shop 调度的序列拉格朗日松驰法

拉格朗日松驰法为求解复杂调度问题次最优解的一种重要方法，陆宝森等人把这种方法推广到Ｊｏｂ Ｓｈｏｐ调度问题，但他们的方法存在解振荡问题。本文提出一种序列拉格朗日松驰法，它能避免解振荡。     

Generating robust and flexible job shop schedules using genetic algorithms

The problem of finding robust or flexible solutions for scheduling problems is of utmost importance for real-world applications as they operate in dynamic environments. In such environments, it is often necessary to reschedule an existing plan due to failures (e.g., machine breakdowns, sickness of employees, deliveries getting delayed, etc.). Thus, a robust or flexible solution may be more valuable than an optimal solution that does not allow easy modifications. This paper considers the issue of robust and flexible solutions for job shop scheduling problems. A robustness measure is defined and its properties are investigated. Through experiments, it is shown that using a genetic algorithm it is possible to find robust and flexible schedules with a low makespan. These schedules are demonstrated to perform significantly better in rescheduling after a breakdown than ordinary schedules. The rescheduling performance of the schedules generated by minimizing the robustness measure is compared with the performance of another robust scheduling method taken from literature, and found to outperform this method in many cases.     

基于基因表达式的克隆选择算法求解JSP问题

针对作业车间调度问题的特征,提出一种基于基因表达式的克隆选择算法。在这个方法中,采用基因表达式编程算法中的编码方式来表示调度方案,同时为了提出的方法具有更强的全局搜索能力,运用克隆选择算法作为搜索引擎。最后,验证提出的方法的有效性,对7组Benchmark实例进行测试。实验结果表明,基于基因表达式的克隆选择算法在求解作业车间调度问题中是非常有效的。     

双资源生产车间调度问题的研究

将遗传算法(GA)和模拟退火算法(SA)相结合研究了双资源生产车间的调度优化问题,该混合算法将机床设备和工人合理地分配给加工任务,使评价性能指标获得最优。通过与国内外学者的算法进行比较,本算法获得的生产周期最短,机床利用率和工人利用率都较高,并且在某些情况下,平均流动时间也较短。因此可以证明本算法具有一定的优越性。     

A neighborhood for complex job shop scheduling problems with regular objectives

Due to the limited applicability in practice of the classical job shop scheduling problem, many researchers have addressed more complex versions of this problem by including additional process features, such as time lags, setup times, and buffer limitations, and have pursued objectives that are more practically relevant than the makespan, such as total flow time and total weighted tardiness. However, most proposed solution approaches are tailored to the specific scheduling problem studied and are not applicable to more general settings. This article proposes a neighborhood that can be applied for a large class of job shop scheduling problems with regular objectives. Feasible neighbor solutions are generated by extracting a job from a given solution and reinserting it into a neighbor position. This neighbor generation in a sense extends the simple swapping of critical arcs, a mechanism that is widely used in the classical job shop but that is not applicable in more complex job shop problems. The neighborhood is embedded in a tabu search, and its performance is evaluated with an extensive experimental study using three standard job shop scheduling problems: the (classical) job shop, the job shop with sequence-dependent setup times, and the blocking job shop, combined with the following five regular objectives: makespan, total flow time, total squared flow time, total tardiness, and total weighted tardiness. The obtained results support the validity of the approach.