Solving a multi-objective job shop scheduling problem with sequence-dependent setup times by a Pareto archive PSO combined with genetic operators and VNS

In this paper, we present a combination of particle swarm optimization (PSO) and genetic operators for a multi-objective job shop scheduling problem that minimizes the mean weighted completion time and the sum of the weighted tardiness/earliness costs, simultaneously. At first, we propose a new integer linear programming for the given problem. Then, we redefine and modify PSO by introducing genetic operators, such as crossover and mutation operators, to update particles and improve particles by variable neighborhood search. Furthermore, we consider sequence-dependent setup times. We then design a Pareto archive PSO, where the global best position selection is combined with the crowding measure-based archive updating method. To prove the efficiency of our proposed PSO, a number of test problems are solved. Its reliability based on some comparison metrics is compared with a prominent multi-objective genetic algorithm (MOGA), namely non-dominated sorting genetic algorithm II (NSGA-II). The computational results show that the proposed PSO outperforms the above MOGA, especially for large-sized problems.     

A hybrid discrete particle swarm optimization algorithm for the no-wait flow shop scheduling problem with makespan criterion

This paper proposes a novel hybrid discrete particle swarm optimization (HDPSO) algorithm to solve the no-wait flow shop scheduling problems with the criterion to minimize the maximum completion time (makespan). Firstly, a simple approach is presented in the paper to calculate the makespan of a job permutation. Secondly, a speed-up method is proposed to evaluate the similar insert neighborhood solution. Thirdly, a discrete particle swarm optimization (DPSO) algorithm based on permutation representation and a local search algorithm based on the insert neighborhood are fused to enhance the searching ability and to balance the exploration and exploitation. Then, computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is concluded that the proposed HDPSO algorithm is superior to both the single DPSO algorithm and the existing hybrid particle swarm optimization (HPSO) algorithm from literature in terms of searching quality, robustness and efficiency.     

Pareto archived simulated annealing for job shop scheduling with multiple objectives

In this paper, the job shop scheduling problem is studied with the objectives of minimizing the makespan and the mean flow time of jobs. The simultaneous consideration of these objectives is the multi-objective optimization problem under study. A metaheuristic procedure based on the simulated annealing algorithm called Pareto archived simulated annealing (PASA) is proposed to discover non-dominated solution sets for the job shop scheduling problems. The seed solution is generated randomly. A new perturbation mechanism called segment-random insertion (SRI) scheme is used to generate a set of neighbourhood solutions to the current solution. The PASA searches for the non-dominated set of solutions based on the Pareto dominance or through the implementation of a simple probability function. The performance of the proposed algorithm is evaluated by solving benchmark job shop scheduling problem instances provided by the OR-library. The results obtained are evaluated in terms of the number of non-dominated schedules generated by the algorithm and the proximity of the obtained non-dominated front to the Pareto front.     

多目标柔性作业车间调度决策精选机制研究

针对多目标柔性作业车间调度优化无法找到唯一最优解的问题，提出多目标遗传算法和层次分析法模糊综合评判的分阶段优化策略。提出优化阶段和精选阶段的优化任务，优化阶段选出一组Pareto解集，精选阶段从Pareto解集中选出最优解；在精选阶段运用层次分析法和模糊评判集成的策略精选调度决策。决策算例证明提出的方法是可行的，可很好地帮助决策者选择出一个最满意的解。     

A hybrid two-phase encoding particle swarm optimization for total weighted completion time minimization in proportionate flexible flow shop scheduling

A proportionate flow shop (PFS) is a special case of the m machine flow shop problem. In a PFS, a fixed sequence of machines is arranged in s stages (s?>?1) with only a single machine at each stage, and the processing time for each job is the same on all machines. Notably, PFS problems have garnered considerable attention recently. A proportionate flexible flow shop (PFFS) scheduling problem combines the properties of PFS problems and parallel-identical-machine scheduling problems. However, few studies have investigated the PFFS problem. This study presents a hybrid two-phase encoding particle swarm optimization (TPEPSO) algorithm to the PFFS problem with a total weighted completion time objective. In the first phase, a sequence position value representation is designed based on the smallest position value rule to convert continuous position values into job sequences in the discrete PFFS problem. During the second phase, an absolute position value representation combined with a tabu search (TS) is applied starting from the current position of particles that can markedly improve swarm diversity and avoid premature convergence. The hybrid TPEPSO algorithm combines the cooperative and competitive characteristics of TPEPSO and TS. Furthermore, a candidate list strategy is designed for the TS to examine the neighborhood and concentrate on promising moves during each iteration. Experimental results demonstrate the robustness of the proposed hybrid TPEPSO algorithm in terms of solution quality. Moreover, the proposed hybrid TPEPSO algorithm is considerably faster than existing approaches for the same benchmark problems in literature.     

粒子群优化算法求解车间调度问题

针对车间调度问题的特点构造了此问题的粒子表达方法,给出了具体的算法应用过程,并将结果与神经网络方法、遗传算法、改进的加工效率函数的调度算法做了对比.结果表明粒子群算法在柔性工作车间调度问题的应用上是十分有效的.     

基于高维多目标候鸟优化算法的柔性作业车间调度

针对柔性作业车间调度问题,提出一种改进的多目标候鸟优化算法来求解考虑完工时间、总拖期、机器总负荷以及总能耗的高维多目标问题。多目标候鸟优化算法在候鸟优化算法的基础上引入基于Pareto支配和参考点的选择算子来给予鸟群选择压力,并用基于属性层次模型和灰色关联分析法的组合权重法从最优解集中选择一个最合适的方案。算例和实例验证了算法的有效性和实用性。     

[车间调度]并行多机开放车间调度问题的模型与算法

发光二极管制造过程中,晶粒分类拣选工序的调度问题是典型的并行多机开放车间调度问题,属于NP-hard问题。研究了该调度问题以最小化总加权完工时间为目标的求解模型与算法。根据问题特性构建了可获得最优解的混合整数规划模型,并设计了同时考虑质量与求解效率的启发式算法和改进粒子群优化算法。仿真结果显示,启发式算法和改进粒子群优化算法都能在合理的时间内迅速有效地获得较佳的调度解。     

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.     

多目标批量生产柔性作业车间优化调度

研究批量生产中以生产周期、最大提前/最大拖后时间、生产成本以及设备利用率指标(机床总负荷和机床最大负荷)为调度目标的柔性作业车间优化调度问题。提出批量生产优化调度策略,建立多目标优化调度模型,结合多种群粒子群搜索与遗传算法的优点提出具有倾向性粒子群搜索的多种群混合算法,以提高搜索效率和搜索质量。仿真结果表明,该模型及算法较目前国内外现有方法更为有效和合理。最后,从现实生产实际出发给出多目标批量生产柔性调度算例,结果可行,可对生产实践起到一定的指导作用。     

基于改进粒子群算法的车间作业排序的优化设计

兼顾车间作业排序中的制造周期和机器利用率,建立了以最小化最大完工时间为主目标、以最大化机器利用率为从目标的优化模型。设计了引入自适应技术的惯性权重,使基本粒子群算法的学习因子可动态变化地改进粒子群算法,并用该改进后的算法对车间作业排序进行了优化设计。实例研究表明：改进后的粒子群算法在收敛速度和收敛可靠性上均优于未改进的粒子群算法,在求解车间作业排序问题的应用中具有更高的求解质量。     

An effective hybrid particle swarm optimization for no-wait flow shop scheduling

The no-wait flow shop scheduling that requires jobs to be processed without interruption between consecutive machines is a typical NP-hard combinatorial optimization problem, and represents an important area in production scheduling. This paper proposes an effective hybrid algorithm based on particle swarm optimization (PSO) for no-wait flow shop scheduling with the criterion to minimize the maximum completion time (makespan). In the algorithm, a novel encoding scheme based on random key representation is developed, and an efficient population initialization, an effective local search based on the Nawaz-Enscore-Ham (NEH) heuristic, as well as a local search based on simulated annealing (SA) with an adaptive meta-Lamarckian learning strategy are proposed and incorporated into PSO. Simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed hybrid algorithm.     

考虑扰动事件的机械加工工艺参数与车间动态调度综合节能优化

随着能源消耗和环境问题的不断加剧,机械加工车间的高效节能生产越来越受到制造业的关注。传统动态调度优化时每道工序的工艺参数固定,未考虑工艺参数与车间调度之间的关联关系,限制了调度优化的潜力。为了更好地实现柔性作业车间节能增效,并快速有效地应对车间生产过程中出现的突发扰动事件,提出一种考虑扰动事件的加工工艺参数与车间动态调度综合优化方法。首先详细分析订单插入与机床故障下柔性作业车间的能耗特性,以总能耗与最大完工时间为目标,建立工艺参数与动态调度综合优化模型,然后设计一种面向扰动事件的动态决策机制,并提出改进的自适应形状估计进化算法(AGE-MOEA)进行优化求解,最后通过案例分析与算法对比,验证了所提出方法的有效性。     

No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm

A novel hybrid discrete particle swarm optimization (HDPSO) algorithm is proposed in this paper to solve the no-idle permutation flow shop scheduling problems with the criterion to minimize the maximum completion time (makespan). Firstly, two simple approaches are presented to calculate the makespan of a job permutation. Secondly, a speed-up method is proposed to evaluate the whole insert neighborhood of a job permutation with (n?1)² neighbors in time O(mn ²), where n and m denote the number of jobs and machines, respectively. Thirdly, a discrete particle swarm optimization (DPSO) algorithm based on permutation representation and a local search algorithm based on the insert neighborhood are fused to enhance the searching ability and to balance the exploration and exploitation. Then, computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is concluded that the proposed HDPSO algorithm is not only superior to two recently published heuristics, the improved greedy (IG) heuristic and Kalczynski–Kamburowski (KK) heuristic, in terms of searching quality, but also superior to the single DPSO algorithm and the PSO algorithm with variable neighborhood search (PSOvns) in terms of searching quality, robustness and efficiency.     

基于混合粒子群优化算法的置换流水车间调度问题研究

针对最大完工时间最小的置换流水车间调度问题,提出一种粒子群优化算法与变邻域搜索算法结合的混合粒子群优化（hybrid particle swarm optimization,HPSO）算法。在该混合算法中,采用NEH启发式算法进行种群初始化,以提高初始解质量。运用基于随机键的升序排列规则（ranked-or-der-value,ROV）,将连续PSO算法应用于离散置换流水车间调度问题中,提出了一种基于关键路径的变邻域搜索算法,以进一步提高算法的局部搜索能力,使算法在集中搜索和分散搜索之间达到合理的平衡。最后,运用提出的混合算法求解Taillard和Watson基准测试集,并将测试结果与一些代表算法进行比较,验证了该调度算法的有效性。     

混合离散蝙蝠算法求解多目标柔性作业车间调度

针对以最大完工时间、生产成本和生产质量为目标的柔性作业车间调度问题,在研究和分析蝙蝠算法的基础上,提出一种混合离散蝙蝠算法。为了提高求解多目标柔性作业车间调度问题的混合离散蝙蝠算法的初始种群质量,在通过分析初始选择的机器与每道工序调度完工时间两者关系的基础上,提出一种优先指派规则策略产生初始种群,提高了算法的全局搜索能力。同时采用位置变异策略来使得算法在较短的时间内尽可能多地搜索到最优位置,有效地避免了算法早熟收敛。在计算问题的目标值上面,首次提出时钟算法。针对具体实例进行测试,试验数据表明,该算法在求解柔性作业车间调度问题上有很好的性能,是一种有效的调度算法,从而为解决这类问题提供了新的途径和方法。     

解决JOB SHOP问题的粒子群优化算法

设计了2种解决Job shop问题的粒子群算法,即实数编码的粒子群调度算法和工序编码的粒子群调度算法。工序编码的粒子群调度算法更符合Job shop问题的特点,优化性能相对高。但粒子群调度算法容易陷入局部最优。为了提高优化性能,将粒子群算法和模拟退火算法结合,得到了粒子群-模拟退火混合调度算法。仿真结果表明了算法的有效性。     

基于混合粒子群算法具有交货期瓶颈的作业车间调度问题

为了解决一类具有交货期瓶颈的作业车间调度问题,给出了基于订单优势的交货期满意度和交货期瓶颈资源确定方法,以工件拖期加权和最小为优化目标,建立了基于交货期满意度和瓶颈资源约束的作业车间调度模型;为了求解该调度模型,设计了一种基于模拟退火的混合粒子群算法,该算法采用随机工序表达方式进行编码,并在模拟退火算法中引入变温度参数来提高算法效率。通过随机仿真,分别采用PSO-SA、SA和PSO对所建立的调度模型进行求解,结果显示PSO-SA算法的广泛性好、求解效率高且算法的稳定性好,验证了模型和算法的有效性。     

粒子群算法解决置换流水车间调度问题方法综述

置换流水车间调度问题是典型的NP问题,近年来随着粒子群算法的出现和发展,用来解决车间生产调度问题的粒子群思想和方法也层出不穷。为了促进粒子群算法的进一步发展,更好地解决流水车间调度问题以及为设计更好的算法提供参考,对粒子群算法解决生产调度问题的各个步骤所采用的方法进行总结,分析了各种方法的适用范围,为设计更好的算法奠定了良好的基础;最后探讨了粒子群算法求解置换流水车间调度问题有待进一步研究的若干方向和内容。     

解决无等待流水线调度问题的新算法

提出了解决无等待流水线调度问题的3种新算法,即离散粒子群优化算法、离散差异进化算法和阈值接收算法。离散粒子群优化算法和离散差异进化算法采用了基于工件序列的编码方式和新的个体生成方法,从而使具有连续性质的粒子群优化算法和差异进化算法能直接用于求解调度问题。仿真试验表明了上述算法的有效性。