采用多个体交叉的遗传算法求解作业车间问题

为改善目前求解Job-Shop问题中的遗传算法的性能,加快搜索最优调度解的速度,首先分析了目前Job-Shop问题自身的求解难点和遗传算法的特点,并借鉴生物学的依据,提出了多个体交叉的遗传算法。该算法在遗传过程中采用多个体遗传算子,充分利用个体的优良性质,对不可行调度解根据多个体修补原则进行修正,可保证遗传后代的合法性和多样性,能够加快最优调度解的搜索时间。仿真结果充分证明了该算法的有效性。     

求解作业车间调度问题的多种群杂交遗传算法

提出了多种群杂交改进遗传算法,在约束条件处理中引入可能解空间概念;设计了机床编号可变的基于工序的编码。父代个体和交叉变异得到的个体在选择操作中具有同等选择机会,保证最优个体保留到下一代,又能保持子代的多样性。在遗传过程中引入修正种群,实现多种群杂交,以保持种群的多样性。应用实例分析和工程实践表明,算法稳定可靠,运行效率大大提高。     

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.     

基于改进遗传算法的车间调度问题求解

针对车间调度问题(Job Shop Problem,JSP)的特点,提出一种改进遗传算法。该方法利用剩余作业时间最多(MostWork Remaining,MWR)的工件优先排列的启发式规则来产生初始种群,并且在进化过程中采用分代交叉算子进行操作来避免算法早熟。通过分析算例结果表明,该改进遗传算法可以在进化初期就得到比较理想的调度方案,而且优化收敛速度快、结果优,更适用于解决车间调度问题。     

基于混合遗传算法的车间调度问题研究

利用遗传算法(GA)和模拟退火算法(SA)求解该问题近优解的有效性和实用性,提出一种实现车间调度的混合遗传算法(GASA),给出了一个新的编码方法,并建立了相应编码的解码规则.对初始温度的确定方法和获得适应度函数的方法进行了探讨.基于LA16调度问题,分别利用该方法和单纯遗传算法及模拟退火算法进行了模拟仿真计算,计算结果表明该混合算法克服了单纯遗传算法和模拟退火算法在车间调度优化方面的不足,具有较高的鲁棒性.     

A novel hybrid genetic algorithm for the open shop scheduling problem

In this paper, a hybrid genetic algorithm is proposed for the open shop scheduling problem with the objective of minimizing the makespan. In the proposed algorithm, a specialized crossover operator is used that preserves the relative order of jobs on machines and a strategy is applied to prevent from searching redundant solutions in the mutation operator. Moreover, an iterative optimization heuristic is employed which uses the concept of randomized active schedules, a dispatching index based on the longest remaining processing time rule and a lower bound to further decrease the search space. Computational results show that the proposed algorithm outperforms other genetic algorithms and is very competitive with well-known metaheuristics available in the literature.     

An artificial immune algorithm for multiple-route job shop scheduling problem

This paper addresses the multiple-route job shop scheduling problem to minimize makespan. The problem is recognized to be extremely difficult because of its combinatorial nature of integer optimization and the large size of the real problem. The goal is, thus, to obtain near-optimal schedules in a computationally efficient manner. Mathematical formulation of the problem is first presented. Then, an approach based on artificial immune algorithm is proposed. In order to evaluate the effectiveness of the proposed approach, 30 problems in small, medium, and large size are designed and solved using the proposed approach. Problems are also solved using Lingo software and the results are compared. The computational results show that the proposed approach generates high-quality schedules in a timely fashion.     

改进混合Jaya优化算法求解柔性作业车间调度问题

针对柔性作业车间调度问题(Flexible Job shop Scheduling Problem,FJSP),以最小化最大完工时间为优化目标,在研究现有Jaya优化算法的基础上,结合邻域搜索算法,提出一种改进混合Jaya优化算法.首先,针对MSOS编码方式设计种群初始化方法;其次,提出一种基于Jaya优化算法思想的离散化更新算子,使算法适用于FJSP;然后,设计了2种新型邻域结构,有效增强了算法的局部寻优能力;最后,通过3组著名的FJSP基准算例进行测试,并与相同目标的其他算法进行对比分析.结果 表明,改进混合Jaya优化算法能有效求解FJSP,且比相同目标的其他算法有更强的求解能力.     

求解作业车间调度问题的快速启发式算法

首先将作业车间调度问题转换为一个搭积木模型,受这个直观模型的启发,提出了一个启发式的搭积木规则,该规则综合考虑了已经搭好的积木的顶高和将要搭积木的剩余高度。基于这个规则,提出了一个求解作业车间调度问题的快速启发式算法,对国际上通用的benchmark例的模拟实验结果表明,提出的算法优于经典的优先分配启发式算法。     

改进混合遗传算法的作业车间调度研究

针对作业车间调度问题,结合遗传算法和模拟退火算法的优点,提出一种改进混合遗传模拟退火算法.首先,加入自适应调整的遗传操作以及精英替换策略,并对模拟退火算子进行改进,增加记忆功能以防止遗失当前最优解;然后,对于当前状态,采用多次搜索策略代替单次比较方式,以接受区域内的最优状态;其次,加入升温策略,从而激活各个状态的接受概...     

新型混合改进遗传算法求解零等待流水车间调度问题

具有零等待约束条件的流水车间调度问题是一类典型的NP难问题,针对该问题提出一种新型混合改进遗传算法进行优化求解.首先,采用改进NEH算法强化初始种群质量,提高种群的多样性.结合关联规则理论挖掘种群中的优势块,借助优势块进行人工染色体组合,以降低问题复杂度.交叉操作采用单段交叉、双段交叉和三段交叉3种交叉机制,改善算法全...     

A hybrid scatter search for the partial job shop scheduling problem

This paper presents a special case of the general shop called partial job shop problem. The partial job shop is a more realistic generalization of the mixed shop problem. The problem is formulated as a mixed integer programming model. A scatter search algorithm combined with tabu search and path relinking is used to tackle this problem with makespan criterion. The computational experiments are performed on some problem instances. The results are compared with a lower bound and the effectiveness of the algorithm is shown.     

A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem

In this paper, a hybrid algorithm combining particle swarm optimization (PSO) and tabu search (TS) is proposed to solve the job shop scheduling problem with fuzzy processing time. The object is to minimize the maximum fuzzy completion time, i.e., the fuzzy makespan. In the proposed algorithm, PSO performs the global search, i.e., the exploration phase, while TS conducts the local search, i.e., the exploitation process. The global best particle is used to direct other particles to optimal search space. Therefore, in the proposed algorithm, TS-based local search approach is applied to the global best particle to conduct find-grained exploitation. In order to share information among particles, one-point crossover operator is embedded in the hybrid algorithm. The proposed algorithm is tested on sets of the well-known benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed algorithm is shown against the best performing algorithms from the literature.     

Parallel line job shop scheduling using genetic algorithm

Parallel line job shop scheduling involves the optimal allocation and scheduling of jobs in multiple processing lines. Each job is allocated to a particular line and is processed to completion in that line. Also, all jobs allocated to a line are processed in a particular order. The objective of this paper is to find the optimal allocation of jobs to lines and also the optimal order of jobs processed in each line based on individual processing times and set up times. The optimal schedule gives the minimum makespan for the completion of all jobs. The optimization technique used is genetic algorithm.     

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。     

A heuristic algorithm for solving flexible job shop scheduling problem

This paper deals with the flexible job shop scheduling problem with the objective of minimizing the makespan. An efficient heuristic based on a constructive procedure is developed to obtain high-quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic can obtain effective solutions in very short and nearly zero time and is comparable with even metaheuristic algorithms and promising for practical problems.     

基于遗传退火算法的绿色作业车间调度

绿色制造和智能制造是工业发展的两大趋势,针对目前作业车间能耗大、设备利用率低,以及产品拖期严重等问题,以智能制造业环境中的作业车间为研究对象,建立了以车间总能耗和总拖期惩罚为优化目标的多目标调度模型,并通过设置权重系数来调节优化目标决策偏好;基于遗传算法收敛速度快、全局搜索能力强,以及模拟退火算法突跳性强的特点,设计一...     

A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problem with limited resource constraints

In this paper, a hybrid discrete firefly algorithm is presented to solve the multi-objective flexible job shop scheduling problem with limited resource constraints. The main constraint of this scheduling problem is that each operation of a job must follow a process sequence and each operation must be processed on an assigned machine. These constraints are used to balance between the resource limitation and machine flexibility. Three minimisation objectives—the maximum completion time, the workload of the critical machine and the total workload of all machines—are considered simultaneously. In this study, discrete firefly algorithm is adopted to solve the problem, in which the machine assignment and operation sequence are processed by constructing a suitable conversion of the continuous functions as attractiveness, distance and movement, into new discrete functions. Meanwhile, local search method with neighbourhood structures is hybridised to enhance the exploitation capability. Benchmark problems are used to evaluate and study the performance of the proposed algorithm. The computational result shows that the proposed algorithm produced better results than other authors’ algorithms.