求解工件车间调度问题的一种新的邻域搜索算法

该文提出了一种新的求解工件车间调度(job shop scheduling)问题的邻域搜索算法．问题的目标是：在满足约束条件的前提下使得调度的makespan尽可能地小．定义了一种新的优先分配规则以生成初始解;定义了一种新的邻域结构;将邻域搜索跟单机调度结合在一起;提出了跳坑策略以跳出局部最优解并且将搜索引向有希望的方向．计算了当前国际文献中的一组共58个benchmark问题实例,算法的优度高于当前国外学者提出的两种著名的先进算法．其中对18个10工件10机器的实例,包括最著名的难解实例ft10,在可接受的时间内都找到了最优解．这些实例是当前文献中报导的所有规模为10工件10机器的实例．     

求解柔性作业车间调度的GATOC混合方法

柔性作业车间调度问题是经典作业车间调度问题的扩展,它允许工序在可选加工机器集中任意一台上加工,加工时间随加工机器不同而不同。针对柔性作业车间调度问题的特点,提出一种基于约束理论的局部搜索方法,对关键路径上的机器的负荷率进行比较,寻找瓶颈机器,以保证各机器之间的负荷平衡。为了克服传统遗传算法早熟和收敛慢的缺点,设计多种变异操作,增加种群多样性。为了更好保留每代中的优良解,设计了基于海明距离的精英解保留策略。运用提出的算法求解基准测试问题,验证了算法的可行性和有效性。     

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.     

A hybrid discrete differential evolution algorithm for the no-idle permutation flow shop scheduling problem with makespan criterion

This paper presents a hybrid discrete differential evolution (HDDE) algorithm for the no-idle permutation flow shop scheduling problem with makespan criterion, which is not so well studied. The no-idle condition requires that each machine must process jobs without any interruption from the start of processing the first job to the completion of processing the last job. A novel speed-up method based on network representation is proposed to evaluate the whole insert neighborhood of a job permutation and employed in HDDE, and moreover, an insert neighborhood local search is modified effectively in HDDE to balance global exploration and local exploitation. Experimental results and a thorough statistical analysis show that HDDE is superior to the existing state-of-the-art algorithms by a significant margin.     

An integrated search heuristic for large-scale flexible job shop scheduling problems

The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem (JSP), where each operation is allowed to be processed by any machine from a given set, rather than one specified machine. In this paper, two algorithm modules, namely hybrid harmony search (HHS) and large neighborhood search (LNS), are developed for the FJSP with makespan criterion. The HHS is an evolutionary-based algorithm with the memetic paradigm, while the LNS is typical of constraint-based approaches. To form a stronger search mechanism, an integrated search heuristic, denoted as HHS/LNS, is proposed for the FJSP based on the two algorithms, which starts with the HHS, and then the solution is further improved by the LNS. Computational simulations and comparisons demonstrate that the proposed HHS/LNS shows competitive performance with state-of-the-art algorithms on large-scale FJSP problems, and some new upper bounds among the unsolved benchmark instances have even been found.     

A GA/TS algorithm for the stage shop scheduling problem

This paper presents a special case of the general shop called stage shop problem. The stage shop is a more realistic generalization of the mixed shop problem. In the stage shop problem, each job has several stages of operations. In order to solve the stage shop problem with makespan objective function, an existing neighborhood of job shop is used. In this neighborhood, few enhanced conditions are proposed to prevent cycle generation. In addition, a new neighborhood for operations that belong to the same job is presented. These neighborhoods are applied to the stage shop problem in a tabu search framework. A genetic algorithm is used to obtain good initial solutions. An existing lower bound of the job shop is adapted to our problem and the computational results have been compared to it. Our algorithm has reached the optimal solutions for more than half of the problem instances.     

Total flow time minimization in no-wait job shop using a hybrid discrete group search optimizer

The no-wait job shop scheduling problem is a well-known NP-hard problem and it is typically decomposed into timetabling subproblem and sequencing subproblem. By adopting favorable features of the group search technique, a hybrid discrete group search optimizer is proposed for finding high quality schedules in the no-wait job shops with the total flow time criterion. In order to find more promising sequences, the producer operator is designed as a destruction and construction (DC) procedure and an insertion-based local search, the scrounger operator is implemented by differential evolution scheme, and the ranger operator is designed by hybridizing best insert moves. An efficient initialization scheme based on Nawaz–Enscore–Ham (NEH) heuristic is designed to construct the initial population with both quality and diversity. A speed-up method is developed to accelerate the evaluation of the insertion neighborhood. Computational results based on well-known benchmark instances show that the proposed algorithm clearly outperforms a hybrid differential evolution algorithm and an iterated greedy algorithm. In addition, the proposed algorithm is comparable to a local search method based on optimal job insertion, especially for large-size instances.     

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。     

An efficient memetic algorithm for solving the job shop scheduling problem

The job shop scheduling problem (JSP) is well known as one of the most complicated combinatorial optimization problems, and it is a NP-hard problem. Memetic algorithm (MA) which combines the global search and local search is a hybrid evolutionary algorithm. In this paper, an efficient MA with a novel local search is proposed to solve the JSP. Within the local search, a systematic change of the neighborhood is carried out to avoid trapping into local optimal. And two neighborhood structures are designed by exchanging and inserting based on the critical path. The objective of minimizing makespan is considered while satisfying a number of hard constraints. The computational results obtained in experiments demonstrate that the efficiency of the proposed MA is significantly superior to the other reported approaches in the literature.     

基于深度强化学习求解作业车间机器与 AGV联合调度问题

针对作业车间中自动引导运输车(automated guided vehicle, AGV)与机器联合调度问题,以完工时间最小化为目标,提出一种基于卷积神经网络和深度强化学习的集成算法框架.首先,对含AGV的作业车间调度析取图进行分析,将问题转化为一个序列决策问题,并将其表述为马尔可夫决策过程.接着,针对问题的求解特点,设计一种基于析取图的空间状态与5个直接状态特征;在动作空间的设置上,设计包含工序选择和AGV指派的二维动作空间;根据作业车间中加工时间与有效运输时间为定值这一特点,构造奖励函数来引导智能体进行学习.最后,设计针对二维动作空间的2D-PPO算法进行训练和学习,以快速响应AGV与机器的联合调度决策.通过实例验证,基于2D-PPO算法的调度算法具有较好的学习性能和可扩展性效果.     

A modified ABC algorithm for the stage shop scheduling problem

Stage shop problem is an extension of the mixed shop as well as job shop and open shop. The problem is also a special case of the general shop. In a stage shop, each job has a number of stages; each of which includes one or more operations. As a subset of operations of a job, the operations of a stage can be done without any precedence consideration of each other, whereas the stages themselves should be processed according to a preset sequence. Due to the NP-hardness of the problem, a modified artificial bee colony (ABC) algorithm is suggested. In order to improve the exploitation feature of ABC, an effective neighborhood of the stage shop problem and PSO are used in employed and onlooker bee phases, respectively. In addition, the idea of tabu search is substituted for the greedy selection property of the artificial bee colony algorithm. The proposed algorithm is compared with the traditional ABC and the state-of-the-art CMA-ES. The computational results show that the modified ABC outperforms CMA-ES and completely dominates the traditional ABC. In addition, the proposed algorithm found high quality solutions within short times. For instance, two new optimal solutions and many new upper bounds are discovered for the unsolved benchmarks.     

多AGV和机器集成的多目标柔性作业车间调度研究

为解决智能制造环境中具有多时间和多AGV约束的柔性作业车间调度问题,构建了以最小化最大完工时间、最小化总延期、最小化设备总负荷为目标的机器/AGV双约束多目标调度模型,模型中综合考虑加工时间、工件到达时间、交货期等多时间因素,进行了多AGV和机器集成调度。为求解该模型,设计了新的AGV调度规则和改进的NSGA-算法,算法中提出了基于工序的扩展染色体编码方式和基于AGV分配的贪婪式解码策略,同时设计了不同参数控制的多种群二元锦标赛选择和分段交叉变异策略以及基于Pareto级的去重精英保留策略,以促进个体协同优化搜索。通过实例实验,分析了不同AGV数量任务分配方案下的模型有效性,对4个案例的仿真测试和同类算法比较解也验证了改进NSGA-算法求解该模型的有效性。     

求解Job Shop调度问题的混合蚁群算法研究

为了解决单一算法求解Job Shop调度问题存在的不足，该文提出了一种混合算法，将蚁群算法用于全局搜索。针对蚁群算法易于陷入局部最优的情况，提出了一种基于关键工序的邻域搜索方法，将使用此邻域搜索方法的TS算法作为局部搜索策略。利用TS算法较强的局部搜索能力，提高了蚁群算法的优化能力，达到改善Job Shop调度问题解的质量。实验结果表明，混合算法在较短的时间内，找到了FT10、LA24、LA36等典型benchmarks问题的最优解，得到的makespan的平均值较并行遗传算法(PGA)和TSAB算法均有所提高。     

Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives

Flexible job-shop scheduling problem (FJSP) is a practically useful extension of the classical job shop scheduling problem. This paper proposes an effective discrete harmony search (DHS) algorithm to solve FJSP. The objectives are the weighted combination of two minimization criteria namely, the maximum of the completion time (Makespan) and the mean of earliness and tardiness. Firstly, we develop a new method for the initial machine assignment task. Some existing heuristics are also employed for initializing the harmony memory with discrete machine permutation for machine assignment and job permutation for operation sequencing. Secondly, we develop a new rule for the improvisation to produce a new harmony for FJSP incorporating machine assignment and operation sequencing. Thirdly, several local search methods are embedded to enhance the algorithm’s local exploitation ability. Finally, extensive computational experiments are carried out using well-known benchmark instances. Computational results and comparisons show the efficiency and effectiveness of the proposed DHS algorithm for solving the FJSP with weighted combination of two objectives.     

Scatter search with path relinking for the job shop with time lags and setup times

This paper addresses the job shop scheduling problem with time lags and sequence-dependent setup times. This is an extension of the job shop scheduling problem with many applications in real production environments. We propose a scatter search algorithm which uses path relinking and tabu search in its core. We consider both feasible and unfeasible schedules in the execution, and we propose effective neighborhood structures with the objectives of reducing the makespan and regain feasibility. We also define new procedures for estimating the quality of the neighbors. We conducted an experimental study to compare the proposed algorithm with the state-of-the-art, in benchmarks both with and without setups. In this study, our algorithm has obtained very competitive results in a reduced run time.     

基于AGV的管道加热器柔性作业车间调度方法研究

针对基于AGV约束的管道加热器柔性作业车间调度问题,以最小化最大完工时间和最小化车间总负载为目标,提出改进麻雀搜索算法求解调度方案;建立合理的编解码方式表示调度方案;为解决多目标优化问题,引入Patero排序;考虑麻雀搜索算法求解离散优化问题时无效解较多、易陷入局部最优等缺陷,提出引入交叉变异算子、设置精英种群、设计自适应种群比例因子等改进措施;根据标准算例数据及实际车间生产数据对算法可行性进行验证,结果表明改进算法可有效求解合理的调度方案,相比于车间原生产方案,生产效率提高19.6%,且有效降低了车间总负载。     

AGV作业调度模型及改进的DE算法研究

AGV作业调度问题的求解结果对AS/RS的运行效率具有重要影响。通过必要的简化,建立了AGV作业调度问题的静态优化模型。可知静态AGV作业调度问题实质是一种带约束的多重TSP问题,属于典型的NP完全问题,目前还不存在可在多项式时间内求解的确定算法。提出了一种改进的差分演化算法用于求解该问题。为了适应AGV作业调度问题的特点,新算法设计了新的两段编码方法,对多个DE算子进行了改造。还提出了基于生存时间的种群多样性增强机制,用于增强算法的搜索能力,避免陷入局部最优。仿真实验显示,该算法可以有效提高AGV作业调度的效率,验证了相关改进机制的有效性。     

改进的基于分解的多目标进化算法求解双目标模糊柔性作业车间调度问题

针对同时考虑最大模糊完工时间和总模糊机器负载的双目标模糊柔性作业车间调度问题(BFFJSP),本文提出了一种改进的基于分解的多目标进化算法(IMOEA/D),同时最优化最大模糊完工时间和总模糊机器负载,其主要特点是:1)采用3种初始化种群的策略; 2)提出了非支配解优先策略; 3)设计了结合5种局部搜索策略的变邻域搜索; 4)提出了计数器策略预防陷入局部解.运用大量实例进行了算法策略分析和对比实验,仿真结果表明, IMOEA/D在求解BFFJSP上具有更优性能.     

Multi-center variable-scale search algorithm for combinatorial optimization problems with the multimodal property

Combinatorial optimization problems (COPs) are discrete problems arising from aerospace, bioinformatics, manufacturing, and other fields. One of the classic COPs is the scheduling problem. Moreover, these problems are usually multimodal optimization problems with a quantity of global and local optima. As a result, many search algorithms can easily become trapped into local optima. In this article, we propose a multi-center variable-scale search algorithm for solving both single-objective and multi-objective COPs. The algorithm consists of two distinct points. First, the multi-center strategy chooses several individuals with better performance as the only parents of the next generation, which means that there are a number of separate searching areas around the searching center. Second, the next generation of the population is produced by a variable-scale strategy with an exponential equation based on the searching center. The equation is designed to control the neighborhood scale, and adaptively realize the large-scale and small-scale searches at different search stages to balance the maintenance of diversity and convergence speed. In addition, an approach of adjusting centers is proposed concerning the number and distribution of centers for solving multi-objective COPs. Finally, the proposed algorithm is applied to three COPs, including the well-known flexible job shop scheduling problem, the unrelated parallel machine scheduling problem, and the test task scheduling problem. Both the single-objective optimization algorithm and the multi-objective optimization algorithm demonstrate competitive performance compared with existing methods.     

求解柔性作业车间调度问题的新型改进Jaya算法

为有效解决复杂的柔性作业车间调度问题,以最小化最大完成时间为目标,提出了一种结合了变邻域搜索算法的新型改进Jaya算法来求解。为不断挖掘和优化探索最优解,提高算法求解的结果质量,通过Jaya算法的原理重新提出一种解的更新机制,此外在Jaya算法原理的基础上嵌入一种变邻域搜索策略,并在传统邻域结构的基础上重新设计了两种新型邻域结构,扩大了邻域搜索范围,增强了Jaya算法的局部搜索能力,避免算法因失去解的多样性从而陷入局部最优。运用基准算例对该算法的求解性能进行了验证,并与其他算法的仿真结果进行对比,结果表明该改进算法的求解效率更高。