An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling

In this paper, an enhanced Pareto-based artificial bee colony (EPABC) algorithm is proposed to solve the multi-objective flexible job-shop scheduling problem with the criteria to minimize the maximum completion time, the total workload of machines, and the workload of the critical machine simultaneously. First, it uses multiple strategies in a combination way to generate the initial solutions as the food sources with certain quality and diversity. Second, exploitation search procedures for both the employed bees and the onlooker bees are designed to generate the new neighbor food sources. Third, crossover operators are designed for the onlooker bee to exchange useful information. Meanwhile, it uses a Pareto archive set to record the nondominated solutions that participate in crossover with a certain probability. To enhance the local intensification, a local search based on critical path is embedded in the onlooker bee phase, and a recombination and select strategy is employed to determine the survival of the individuals. In addition, population is suitably adjusted to maintain diversity in scout bee phase. By using Taguchi method of design of experiment, the influence of several key parameters is investigated. Simulation results based on the benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed EPABC algorithm.     

Multi-objective artificial bee colony for interval job shop scheduling with flexible maintenance

In scheduling problem with uncertainty, flexible preventive maintenance (PM) and multiple objectives are seldom investigated. In this study, interval job shop scheduling problem with non-resumable jobs and flexible maintenance is considered and an effective multi-objective artificial bee colony (MOABC) is proposed, in which an effective decoding procedure is used to build the schedule and handle PM operation. The objective is to minimize interval makespan and a newly defined objective called total interval tardiness. In each cycle, a dominance-based greedy principle is adopted, a dominance-based tournament is utilized to choose solution for onlooker bee, and the non-dominated ranking is applied to update the non-dominated set. A solution with the highest rank is replaced with a non-dominated solution every certain cycle. Computational results show the good performance of MOABC on the considered problem.     

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.     

多目标柔性作业车间调度优化研究

提出了一种集成权重系数变化法和小生境技术的混合遗传算法,建立了包括时间、成本、交货期满意度和设备利用率在内的多目标优化模型。采用基于工序的编码方式和“间隙挤压法”活动化解码方法;遗传算子包括选择、交叉、变异3种类型;选择操作采用轮盘赌选择方式。为了保证解的收敛性和多样性,采用了精英保留策略和小生境技术。交叉操作采用线性次序交叉方式;变异操作采用互换操作变异方法。染色体的适应度是各个目标函数的随机加权和。仿真实验证明,提出的混合遗传算法可以有效解决柔性作业车间多目标调度优化问题。     

多目标离散灰狼优化算法求解作业车间节能调度问题

针对作业车间节能调度问题,建立了一种以优化总能耗和工件最大完工时间为目标的节能调度模型,并提出一种多目标离散灰狼优化算法进行求解.根据问题的特点,首先采用离散整数编码方式,利用调度规则生成初始种群;其次引入一种基于跟踪模式和搜寻模式的双模式并行搜索方法,并在搜索过程中动态调整两种模式下个体的数目,以协调算法全局和局部搜...     

An effective artificial bee colony algorithm for the flexible job-shop scheduling problem

An effective artificial bee colony (ABC) algorithm is proposed in this paper for solving the flexible job-shop scheduling problem with the criterion to minimize the maximum completion time (makespan). The ABC algorithm stresses the balance between global exploration and local exploitation. First, multiple strategies are utilized in a combination to generate the initial solutions with certain quality and diversity as the food sources. Second, crossover and mutation operators are well designed for machine assignment and operation sequence to generate the new neighbor food sources for the employed bees. Third, a local search strategy based on critical path is proposed and embedded in the searching framework to enhance the local intensification capability for the onlooker bees. Meanwhile, an updating mechanism of population by generating the scout bees with the initialing strategy is proposed to enrich the searching behavior and avoid the premature convergence of the algorithm. In addition, a well-designed left-shift decoding scheme is employed to transform a solution to an active schedule. Numerical simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed ABC algorithm.     

扰动机制下的遗传算法求解柔性作业车间调度

针对柔性作业车间调度问题,提出一种扰动机制下的遗传算法,该算法以最大完工时间最小为优化目标.为了克服传统遗传算法早熟的缺点,引入差异度阈值策略对传统遗传算法的结构进行动态调整,同时设计了灾变机制和大变异策略相结合的扰动机制,增强了算法的搜索性能.最后,通过基准案例进行测试并与其他算法的结果进行对比,验证了扰动机制下的遗...     

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.     

双资源多目标车间调度算法研究

针对双资源约束的车间调度问题,考虑机器和操作工人两种生产资源对各种目标的约束影响,提出一种基于遗传算法和禁忌搜索算法的混合调度算法,使用多目标决策理论,使生产周期、工件总延误时间、设备闲置时间、人员闲置时间的综合指标值为最小,得到多目标的最优解或次优解.最后对算法进行试验,试验结果证明该算法可行,具有很好的搜索性能和效率.     

混合蜂群算法求解柔性作业车间调度问题

为解决柔性作业车间调度问题,提出一种基于蜂群模型的混合群智能优化算法.在算法初始化阶段提出了蜂群优化算法结合随机方法的种群初始化方法,提高了初始种群质量;为提高算法搜索精度,在观察蜂阶段采用模拟退火算法更新观察蜂群,并以退温系数调节邻域规模,随算法进程细化搜索范围;针对柔性作业车间调度问题特点,建立了可控规模的邻域更新方法.采用柔性作业车间标准算例,通过仿真编程和与其他算法的比较,验证了算法的有效性和优越性.     

多目标柔性车间调度的Pareto混合禁忌搜索算法

针对最小化最大完成时间、总机床负荷及最大机床负荷的多目标柔性作业车间调度问题,提出了一种带有Pareto档案集的混合禁忌搜索算法.该算法为每次迭代产生的邻域解集进行Pareto非支配排序,选择第一前沿的解用于Pareto档案集更新,并给出了一种Pareto档案集快速更新算法.为减小邻域搜索空间,结合问题特征,设计了基于公共关键块结构的插入邻域和交换邻域.通过3个经典算例的实验仿真,以及与其他算法的比较,验证了该算法的可行性和有效性.     

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.     

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.     

改进细菌觅食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的NP难特性,提出一种改进的细菌觅食优化算法。采用集成法策略同时求解柔性作业车间调度问题的机器分配和工序调度子问题。将细菌个体表示为工序串,建立问题和算法的映射关系;分别针对普通细菌个体和当前最优个体设计了多重趋化操作,以增强算法的局部搜索能力;复制操作设置繁殖阈和死亡阈,以提高对历史经验的继承程度;迁移/驱散操作中,结合改进的LPT启发式规则,提出带倾向性的迁移/驱散操作方式。采用正交试验对算法的重要参数进行了优化配置,通过搜索算子优化效果对比实验证明了正交试验的结论;进行了收敛性能对比实验,证明算法具有优秀的全局开发能力和局部探索能力;典型算例实验结果表明,该算法能够有效求解柔性作业车间调度问题。     

Solving job shop scheduling problems using artificial immune system

The n-job, m-machine job shop scheduling (JSS) problem is one of the general production scheduling problems. Many existing heuristics give solutions for small size problems with near optimal solutions. This paper deals with the criterion of makespan minimization for the job shop scheduling of different size problems. The proposed computational method of artificial immune system algorithm (AIS) is used for finding optimal makespan values of different size problems. The artificial immune system algorithm is tested with 130 benchmark problems [10 (ORB1-ORB5 & ARZ5-ARZ9), 40 (LA01-LA40) and 80 (TA01-TA80)]. The results show that the AIS algorithm is an efficient and effective algorithm which gives better results than the Tabu search shifting bottleneck procedure (TSSB) as well as the best solution of shifting bottleneck procedure ( SB-GLS1 ) of Balas and Vazacopoulos.     

多目标模糊作业车间调度问题研究

研究了具有模糊加工时间和模糊交货期的多目标作业车间调度问题,首先给出了基于模糊优先规则的编码新方式,染色体的每一位表示在GT算法迭代过程中,对应机器上发生的某次冲突,根据该基因位对应的优先规则消除。然后设计了基于个体密集距离的多目标进化算法,该算法利用密集距离进行外部档案维护和适应度赋值。最后将多目标进化算法应用于模糊作业车间调度问题,以最大化最小一致指标和最小化模糊最大完成时间,并和其他算法比较。计算结果验证了多目标进化算法在模糊调度方面良好的搜索性能。     

Interval job shop scheduling problems

This paper focuses on the design and development of an expert system for on-line detection of various control chart patterns so as to enable the quality control practitioners to initiate prompt corrective actions for an out-of-control manufacturing process. Using this expert system developed in Visual BASIC 6, all the nine most commonly observed control chart patterns, e.g., normal, stratification, systematic, increasing trend, decreasing trend, upward shift, downward shift, cyclic, and mixture can be recognized well, employing an optimal set of seven shape features. Based on an observation window of 32 data points, it can plot the control chart, compute the control limits, identify the control chart pattern, calculate the process capability index, determine the maximum run length, and identify the starting point of the maximum run length. After pattern recognition, it can also inform the users about various root assignable causes associated with a particular pattern along with the necessary pre-emptive actions. It opens up wide opportunities for quality improvement and real-time applications in diverse manufacturing processes. This developed expert system is built for a vertical drilling process and its recognition performance is tested using simulated process data.