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Job-shop scheduling problems based on immune ant colony optimization

SONG Xiao-jiang,LU Jun-yu,SUI Ming-lei,

计算机应用 , 2007,
Abstract: Mathematic model of Job-shop scheduling problem was established and a kind of immune ant colony algorithm was introduced to deal with Job-shop scheduling problem. Through introducing the mechanism of immunity into the operations of genetic algorithm, the vaccines were obtained and updated in those operations. Then, the immune operation was used on the evolution of populations. And the problems of precocity and low searching efficiency can be avoided when immune operation takes effect. The simulations show that the algorithm is feasible and efficient.
An ant colony optimization algorithm for job shop scheduling problem  [PDF]
Edson Flórez,Wilfredo Gómez,Lola Bautista
Computer Science , 2013,
Abstract: The nature has inspired several metaheuristics, outstanding among these is Ant Colony Optimization (ACO), which have proved to be very effective and efficient in problems of high complexity (NP-hard) in combinatorial optimization. This paper describes the implementation of an ACO model algorithm known as Elitist Ant System (EAS), applied to a combinatorial optimization problem called Job Shop Scheduling Problem (JSSP). We propose a method that seeks to reduce delays designating the operation immediately available, but considering the operations that lack little to be available and have a greater amount of pheromone. The performance of the algorithm was evaluated for problems of JSSP reference, comparing the quality of the solutions obtained regarding the best known solution of the most effective methods. The solutions were of good quality and obtained with a remarkable efficiency by having to make a very low number of objective function evaluations.
DYNA , 2009,
Abstract: the purpose of this study is to reduce the total process time (makespan) and to increase the machines working time, in a job shop environment, using a heuristic based on ant colony optimization. this work is developed in two phases: the first stage describes the identification and definition of heuristics for the sequential processes in the job shop. the second stage shows the effectiveness of the system in the traditional programming of production. a good solution, with 99% efficiency is found using this technique.
DYNA , 2009,
Abstract: The purpose of this study is to reduce the total process time (Makespan) and to increase the machines working time, in a job shop environment, using a heuristic based on ant colony optimization. This work is developed in two phases: The first stage describes the identification and definition of heuristics for the sequential processes in the job shop. The second stage shows the effectiveness of the system in the traditional programming of production. A good solution, with 99% efficiency is found using this technique.
Ant colony and particle swarm optimization algorithm-based solution to multi-objective flexible job-shop scheduling problems

ZHANG Wei-cun,ZHENG Pi-e,WU Xiao-dan,

计算机应用 , 2007,
Abstract: A hybrid of ant colony and particle swarm optimization algorithms was proposed to solve the multi-objective flexible job-shop scheduling problem based on the analysis of objectives and their relationship. The hybrid was formulated in a form of hierarchical structure. The ant colony algorithm was performed at the master level to minimize the total load and bottleneck load through selecting job-processing route, while the particle swarm optimization algorithm was carried out at the slave level to minimize the makespan through scheduling the operations with machines without violating the result from the master level. The transfer probabilities of ant between machines were designed by using heuristic information of processing time and machine load. The decoding method of particle vector was well designed in order to sequence operations of every machine based on the size relations of element priority values. The simulation and results from comparison with other algorithms demonstrate the effectiveness of the proposed algorithm.
An Asynchronous Parallel Ant Colony Optimization for Flexible Job-Shop Scheduling Problem

- , 2016, DOI: 10.11784/tdxbz201603058
Abstract: 调度问题广泛存在于资源共享型系统中, 大多数的调度问题都属于混合整数规划问题. 大规模混合整数规划问题是计算科学领域中的NP-hard经典问题之一, 一般认为无法用精确计算求解. 生产调度是调度的一个重要分支, 是实现智能制造关键环节之一. 针对多品种变批量柔性作业车间调度问题, 以最小制造期为优化目标, 设计了一种基于Petri网的异步并行蚁群算法, 其中:提出了一种基于Petri网的步可达图构造方法, 用于蚁群算法解空间的构造; 探讨了传统蚁群算法搜索机制, 并给出了一种基于异步仿真时钟的蚁群并行搜索方法; 仿真结果表明, 多线程控制方法可以有效地避免算法的早熟收敛问题. 将所提出的算法应用于某安防件智能制造系统的柔性作业车间调度中, 降低了系统的总制造时间, 获得较好工程效果的同时验证了算法的有效性.
Scheduling problem widely exists in the systems of resource-sharing,mainly in the form of mixed integer programming. Large-scale mixed-integer programming problem is one of the classic NP-hard problems in the field of computational science,which cannot be solved through precise computation in general. Job-shop scheduling is a major sub-field of scheduling and a key aspect of intelligent manufacturing. Aiming at the flexible job-shop scheduling problems of multi-products and variety batch,a Petri nets-based asynchronous parallel ant colony optimization is proposed with the optimization target of minimizing the time consuming of manufacturing cycle. A Petri nets-based method of creating step-reachability graph is put forward,which is used for construction of search space of ant colony optimization. On the basis of discussing the search mechanism of traditional ant colony algorithm,a search method of asynchronous parallel for ant colony is presented based on asynchronous simulation clock. A multi-threaded control method for update of pheromone is used. Simulation results show that the multi-threaded control method can overcome the premature convergence effectively. The proposed approach is illustrated by a case of flexible job shop scheduling for an intelligent manufacturing system of defense and security facilities,through which solutions of high quality can be found quickly. In sum,the proposed optimization has obtained a good effect in engineering applications while the validity of optimization has been proved
A Novel Metaheuristics To Solve Mixed Shop Scheduling Problems  [PDF]
V. Ravibabu
Computer Science , 2013,
Abstract: This paper represents the metaheuristics proposed for solving a class of Shop Scheduling problem. The Bacterial Foraging Optimization algorithm is featured with Ant Colony Optimization algorithm and proposed as a natural inspired computing approach to solve the Mixed Shop Scheduling problem. The Mixed Shop is the combination of Job Shop, Flow Shop and Open Shop scheduling problems. The sample instances for all mentioned Shop problems are used as test data and Mixed Shop survive its computational complexity to minimize the makespan. The computational results show that the proposed algorithm is gentler to solve and performs better than the existing algorithms.
A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems  [PDF]
S. Narendhar,T. Amudha
Computer Science , 2012,
Abstract: Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the solutions obtained by Bacterial Foraging Optimization algorithm for well-known test problems of different sizes. From the implementation of this research work, it could be observed that the proposed Hybrid Bacterial Foraging Optimization was effective than Bacterial Foraging Optimization algorithm in solving Job Shop Scheduling Problems. Hybrid Bacterial Foraging Optimization is used to implement real world Job Shop Scheduling Problems.
Two??Stage Hybrid Pareto Ant Colony Algorithm for Multi??Objective Flexible Job Shop Scheduling

- , 2016, DOI: 10.7652/xjtuxb201607022
Abstract: 针对多目标柔性作业车间调度问题(FJSP)分解得到的作业分派、排序子问题仍是多目标优化问题的情况,提出了一种求解该问题的分层Pareto优化框架,并采用该框架构建了两阶段混合Pareto蚁群算法的求解算法,其中两个Pareto蚁群系统分别求解多目标作业分派、排序问题。结合GT算法、排产规则评估和过滤第一阶段的分派方案,将具有较好评估全局解的分派方案作为分派阶段的精英档案,并输入给排序蚁群系统获取其非支配调度解,进而获取问题全局非支配解。子问题算法混合了各目标相关的邻域搜索策略,与Pareto蚁群算法结合,以期提高解的质量。通过求解带有平均工件加权延迟时间指标的多个FJSP基准算例,验证了算法的有效性。计算结果表明,该分层Pareto优化框架对原问题进行分层分解,有利于降低原问题的复杂性,相比多数文献,算法能够获得各基准算例Pareto非支配解,从而为分解求解复杂多目标调度优化问题提供了一种途径。
Multi??objective flexible job shop scheduling can be divided into two sub??problems, namely job assignment and sorting, which are often multi??objective optimization problems. Aiming at this situation, this paper presents a layered Pareto optimization frame for multi??objective flexible job shop problem and proposes a two??stage hybrid Pareto ant colony algorithm for multi??objective operation assignment (OA) and operation sequencing (OS) sub??problems. Embedding multiple scheduling rules in GT algorithm is used to evaluate and filter the assignment solutions. The global optimal non??dominated front of the original problem is obtained by scheduling optimization as the elite archive of assignments. Each Pareto ant colony algorithm is combined with the neighborhood search strategies related to different objectives. The co??evolutionary can obtain high??quality solutions to multi??objective FJSP. Finally, by solving four benchmark instances considering minimizing the mean weighted tardiness time, the effectiveness of the method is testified. The simulation results show that the layered Pareto optimization frame helps to reduce complexity of the problem, and compared with other literatures, the proposed algorithm can obtain the Pareto non??dominant solutions of each instance, providing a new way for solving complex multi??objective scheduling problems
Genetic ant colony algorithm for Job-shop scheduling problem

WU Yu-ming,XU Cong-fu,

计算机应用研究 , 2010,
Abstract: 描述了Job-shop调度问题,研究遗传算法和蚁群算法在解决Job-shop问题中的优点和不足,融合遗传算法和蚁群算法设计了遗传蚁群算法以求解Job-shop调度问题,并对算法进行了仿真实验,通过与遗传算法、蚁群算法及已有的遗传算法和蚁群算法的融合算法结果的对比,验证了该算法的有效性。
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