A new hybrid genetic algorithm for job shop scheduling problem

Job shop scheduling problem is a typical NP-hard problem. To solve the job shop scheduling problem more effectively, some genetic operators were designed in this paper. In order to increase the diversity of the population, a mixed selection operator based on the fitness value and the concentration value was given. To make full use of the characteristics of the problem itself, new crossover operator based on the machine and mutation operator based on the critical path were specifically designed. To find the critical path, a new algorithm to find the critical path from schedule was presented. Furthermore, a local search operator was designed, which can improve the local search ability of GA greatly. Based on all these, a hybrid genetic algorithm was proposed and its convergence was proved. The computer simulations were made on a set of benchmark problems and the results demonstrated the effectiveness of the proposed algorithm.     

求解流水线调度问题的万有引力搜索算法

研究了以最大完工时间为目标的流水线调度问题,使用万有引力算法求解调度问题,提出了一种最大排序规则,利用物体间各个位置分量值存在的大小次序关系,并结合随机键编码的方法产生,将物体的连续位置转变成了一个可行的调度方案;提出了一种边界变异的策略使得越界的物体不再聚集在边界上,而是分布在边界附近的可行空间内,从而增加种群的多样性;结合交换算子和插入算子提出了一种新的局部搜索算法,有效地避免了算法陷入局部最优值,进一步提高了解的质量.最后证明了算法的收敛性,并且计算了算法的时间复杂度和空间复杂度,仿真实验说明了所得算法的有效性.     

Local Search Genetic Algorithms for the Job Shop Scheduling Problem

In previous work, we developed three deadlock removal strategies for the job shop scheduling problem (JSSP) and proposed a hybridized genetic algorithm for it. While the genetic algorithm (GA) gave promising results, its performance depended greatly on the choice of deadlock removal strategies employed. This paper introduces a genetic algorithm based scheduling scheme that is deadlock free. This is achieved through the choice of chromosome representation and genetic operators. We propose an efficient solution representation for the JSSP in which the job task ordering constraints are easily encoded. Furthermore, a problem specific crossover operator that ensures solutions generated through genetic evolution are all feasible is also proposed. Hence, both checking of the constraints and repair mechanism can be avoided, thus resulting in increased efficiency. A mutation-like operator geared towards local search is also proposed which further improves the solution quality. Lastly, a hybrid strategy using the genetic algorithm reinforced with a tabu search is developed. An empirical study is carried out to test the proposed strategies.     

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。     

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 improved particle swarm optimization algorithm for flowshop scheduling problem

The flowshop scheduling problem has been widely studied and many techniques have been applied to it, but few algorithms based on particle swarm optimization (PSO) have been proposed to solve it. In this paper, an improved PSO algorithm (IPSO) based on the “alldifferent” constraint is proposed to solve the flow shop scheduling problem with the objective of minimizing makespan. It combines the particle swarm optimization algorithm with genetic operators together effectively. When a particle is going to stagnate, the mutation operator is used to search its neighborhood. The proposed algorithm is tested on different scale benchmarks and compared with the recently proposed efficient algorithms. The results show that the proposed IPSO algorithm is more effective and better than the other compared algorithms. It can be used to solve large scale flow shop scheduling problem effectively.     

扩散遗传算法及其应用研究

针对遗传算法在局部搜索能力方面的缺陷,提出了一种基于扩散算子的遗产算法(简称扩散遗产算法)。该算法中包含的扩散算子是变异算子,其主要作用是在遗传搜索中进行局部搜索。用扩散遗传算法和实数编码遗传算法分别训练用于解XOR问题的神经网络,对比结果表明,论文提出的算法兼具强的全局搜索能力和局部搜索能力,因此,该算法可以不借助其它局部搜索算法而单独作为神经网络训练算法,从而简化训练算法,提高训练效率。该算法对提高遗传算法搜索效率和求解精度具有重要的意义。     

带工序跳跃的绿色混合流水车间机器与AGV联合调度

针对带工序跳跃的绿色混合流水车间机器和自动引导车(AGV)联合调度问题,提出改进memetic algorithm (MA)以同时最小化最大完工时间和总能耗.首先,设计基于工序、机器和转速的三层编码策略,最大程度保证算法在整个解空间中搜索;然后,设计混合种群初始化方法以提高初始种群解的质量,同时设计交叉和变异算子以及两种基于问题的邻域搜索策略来平衡算法的全局搜索和局部搜索能力;最后,通过大量仿真实验验证MA算法求解该问题的有效性和优越性.     

基于离散教与学算法的分布式预制流水车间调度研究

分布式工厂生产形式对提高预制构件生产效率、保证订单按时交付、降低企业拖期交货惩罚费用具有重要的意义;因此针对分布式预制构件流水车间调度问题,以最小化订单总拖期惩罚为目标建立了数学优化模型,并基于双层整数编码方式提出了一种离散教与学算法(DTLBO);在算法初始化阶段,采用启发式规则和随机生成融合策略改善初始解的质量,进而增加算法的寻优效率;在教学阶段,结合问题模型特点,设计了顶层替换、底层替换两种邻域构造,促进教师解对学生解的引导优化;在学习阶段,通过变异算子和交叉算子让学生解之间相互学习更新,进一步提升算法的局部开发和全局探索能力;试验结果表明,与遗传算法和变邻域搜索算法对比,提出的DTLBO算法具有更好的求解性能和鲁棒性;最后与实际生产过程常用的经验启发式调度方法相比,提出算法在目标值上表现出不低于10％的平均改进率,有望显著增加预制构件制造企业净利润并提高客户满意度,能够为企业管理者提供更佳、更合理的生产调度方案.     

改进的离散型萤火虫优化算法求解柔性作业车间调度问题

针对传统的群智能优化算法在求解柔性作业车间调度问题(FJSP)时,存在寻优能力不足且易陷入局部最优等缺点,本文以最小化最大完工时间为目标,将萤火虫算法(FA)用于求解柔性作业车间调度问题,提出一种改进的离散型萤火虫算法(DFA)。首先,通过两段式编码建立FA连续优化问题与FJSP离散优化问题之间的联系;其次,设计一种群初始化方法,以确保初始解的质量以及多样性;然后,提出改进离散型萤火虫优化算法并引入局部搜索算法,加强算法的全局搜索能力和局部搜索能力;最后,对标准算例进行仿真,验证DFA算法求解FJSP的有效性。通过与遗传算法和粒子群优化算法进行仿真对比,表明了DFA求解FJSP的优越性。     

易变质产品的生产计划与作业排序集成优化研究

讨论了一类针对易变质产品生产批量计划与作业排序的集成优化问题,以最小化库存成本、变质成本、缺货成本、加班成本之和作为目标函数并建立了混合整数规划模型,采用协同进化遗传算法进行求解,即通过迁移算子把协同进化算法和遗传算法有机联系起来,加强算法的寻优能力和收敛性能,最后通过仿真实验,分析自身进化结果,同时与遗传算法对比结果,验证了算法的性能。     

A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems

This paper presents a novel discrete differential evolution (DDE) algorithm for solving the no-wait flow shop scheduling problems with makespan and maximum tardiness criteria. First, the individuals in the DDE algorithm are represented as discrete job permutations, and new mutation and crossover operators are developed based on this representation. Second, an elaborate one-to-one selection operator is designed by taking into account the domination status of a trial individual with its counterpart target individual as well as an archive set of the non-dominated solutions found so far. Third, a simple but effective local search algorithm is developed to incorporate into the DDE algorithm to stress the balance between global exploration and local exploitation. In addition, to improve the efficiency of the scheduling algorithm, several speed-up methods are devised to evaluate a job permutation and its whole insert neighborhood as well as to decide the domination status of a solution with the archive set. Computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is shown that the proposed DDE algorithm is superior to a recently published hybrid differential evolution (HDE) algorithm [Qian B, Wang L, Huang DX, Wang WL, Wang X. An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers. Computers & Operations Research 2009;36(1):209–33] and the well-known multi-objective genetic local search algorithm (IMMOGLS2) [Ishibuchi H, Yoshida I, Murata T. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation 2003;7(2):204–23] in terms of searching quality, diversity level, robustness and efficiency. Moreover, the effectiveness of incorporating the local search into the DDE algorithm is also investigated.     

基于改进教学算法的车间作业调度问题

为求解车间作业调度问题,提出一种基于个体差异化自学习的改进教学算法.针对教学算法局部搜索能力不高的缺陷, 提出学生不仅应向能力好的学习者学习,亦应进行有差异的自我学习.通过学习者的完工时间评估学生的学习能力,提出学习次数概念,并设计自学习算子,完善学生阶段的更新,提高算法的局部搜索能力.最后,对OR-Library中的标准仿真实例进行实验,结果表明改进教学算法是有效的,其在收敛精度和鲁棒性能上均有较好的提高.     

The economic lot scheduling problem in limited-buffer flexible flow shops: Mathematical models and a discrete fruit fly algorithm

This paper considers the lot scheduling problem in the flexible flow shop with limited intermediate buffers to minimize total cost which includes the inventory holding and setup costs. The single available mathematical model by Akrami et al. (2006) for this problem suffers from not only being non-linear but also high size-complexity. In this paper, two new mixed integer linear programming models are developed for the problem. Moreover, a fruit fly optimization algorithm is developed to effectively solve the large problems. For model’s evaluation, this paper experimentally compares the proposed models with the available model. Moreover, the proposed algorithm is also evaluated by comparing with two well-known algorithms (tabu search and genetic algorithm) in the literature and adaption of three recent algorithms for the flexible flow shop problem. All the results and analyses show the high performance of the proposed mathematical models as well as fruit fly optimization algorithm.     

一种求解作业车间调度的混合粒子群算法*

针对车间作业调度问题,提出了一种混合了知识进化算法和粒子群优化的算法。算法主要是结合知识进化算法的进化选择机制和粒子群优化的局部快速收敛性特性,首先让粒子替代知识进化算法中的进化个体,在群体空间中按粒子群优化规则寻找局部最优,然后根据知识进化算法的全局选择机制寻找全局最优,最后,将车间作业调度问题的特点融入到所提出的混合算法中求解问题。采用基准数据进行测试的仿真实验,并比对标准遗传算法,结果表明所提算法的有效性。     

改进人工蜂群算法求解分布式柔性作业车间调度问题

针对分布式柔性作业车间调度问题的特点,提出一种改进人工蜂群算法.首先,建立以最小化最大完工时间为优化目标的分布式柔性作业车间调度优化模型;然后,改进基本人工蜂群算法以使其适用于求解分布式柔性作业车间调度问题,具体的改进包括设计一种包含三维向量的编码方案,结合问题特点针对性地设计多种策略用于种群初始化,在雇佣蜂改良搜索操作中设计多种有效的进化操作算子,并在跟随蜂搜索操作中引入基于关键路径的局部搜索算子以提升算法的局部搜索能力;最后,利用扩展柔性作业车间通用测试集得到的测试数据设计实验验证算法性能,使用正交试验法优化算法参数设置.仿真实验结果表明,改进后的人工蜂群算法能有效求解分布式柔性作业车间调度问题.     

求解多目标流水车间调度Pareto最优解的遗传强化算法

针对多目标流水车间调度Pareto最优问题, 本文建立了以最大完工时间和最大拖延时间为优化目标的多目标流水车间调度问题模型, 并设计了一种基于Q-learning的遗传强化学习算法求解该问题的Pareto最优解. 该算法引入状态变量和动作变量, 通过Q-learning算法获得初始种群, 以提高初始解质量. 在算法进化过程中, 利用Q表指导变异操作, 扩大局部搜索范围. 采用Pareto快速非支配排序以及拥挤度计算提高解的质量以及多样性, 逐步获得Pareto最优解. 通过与遗传算法、NSGA-II算法和Q-learning算法进行对比实验, 验证了改进后的遗传强化算法在求解多目标流水车间调度问题Pareto最优解的有效性.     

面向多目标流水车间调度的多种群多目标遗传算法

针对制造型企业普遍存在的流水车间调度问题,建立了以最小化最迟完成时间和总延迟时间为目标的多目标调度模型,并提出一种基于分解方法的多种群多目标遗传算法进行求解.该算法将多目标流水车间调度问题分解为多个单目标子问题,并分阶段地将这些子问题引入到算法迭代过程进行求解.算法在每次迭代时,依据种群的分布情况选择各子问题的最好解及与其相似的个体分别为当前求解的子问题构造子种群,通过多种群的进化完成对多个子问题最优解的并行搜索.通过对标准测试算例进行仿真实验,结果表明所提出的算法在求解该问题上能够获得较好的非支配解集.     

求解柔性流水车间调度问题的高效分布估算算法

针对最小化最大完工时间的柔性流水车间调度,利用事件建模思想,线性化0-1混合整数规划模型,使得小规模调度问题通过Cplex可以准确求解,同时设计了高效分布估算算法来求解大规模调度问题.该算法采用的是一种新颖的随机规则解码方式,工件排序按选定的规则安排而机器按概率随机分配.针对分布估算算法中的概率模型不能随种群中个体各位置上工件的更新而自动调整的缺点,提出了自适应调整概率模型,该概率模型能提高分布估算算法的收敛质量和速度.同时为提高算法局部搜索能力和防止算法陷入局部最优,设计了局部搜索和重启机制.最后,采用实验设计方法校验了高效分布估算算法参数的最佳组合.算例和实例测试结果都表明本文提出的高效分布估算算法在求解质量和稳定性上均优于遗传算法、引力搜索算法和经典分布估算算法.