一种新调度类型及其在作业车间调度中的应用

研究改进遗传算法解决作业车间调度问题,问题染色体的编码采用基于工序的编码。针对传统的调度类型的局限性,提出全主动调度及其基于工序编码的产生机制。为了克服传统遗传算法求解调度问题易于早熟收敛的缺点,设计基于优先工序交叉(Precedence operation crossover,POX)和改进子代产生模式的遗传算法。用改进的遗传算法求解传统调度问题、交货期调度问题和提前/拖期(Earliness/Tardiness, E/T)调度问题,研究半主动、主动和全主动三种不同的调度解码机制对遗传算法提供解质量的影响。     

一种求解Job-shop调度问题的遗传局部搜索算法

利用遗传局部搜索算法求解了作业车间调度问题,遗传算法中的染色体编码采用基于工序的编码,并用插入式贪婪解码机制将染色体解码至主动调度。为了克服传统遗传算法易于早熟收敛的缺点,设计了一种改进的优先操作交叉IPOX操作和子代产生模式的遗传算法。对于遗传算法每个染色体个体,使用基于N6邻域结构的局部搜索进一步使它们得到改善。利用所提出的混合遗传算法求解基准问题,验证了算法的有效性。     

An improved genetic algorithm for job-shop scheduling problems using Taguchi-based crossover

A Taguchi-based genetic algorithm (TBGA) is proposed as an improved genetic algorithm to solve the job-shop scheduling problems (JSP). The TBGA combines the powerful global exploration capabilities of conventional genetic algorithm (GA) with the Taguchi method that exploits optimal offspring. The latter method is used as a new crossover and is incorporated in the crossover operation of a GA. The reasoning ability of the Taguchi-based crossover can systematically select the better genes to achieve crossover and, consequently, enhance the GA. Furthermore, mutation is designed to have the neighbor search technique of performing the fine-tuning on the positions of jobs for the JSP. Therefore, the proposed TBGA approach possesses the merits of global exploration and robustness. The proposed TBGA approach is effectively applied to solve the famous Fisher-Thompson and Lawrence benchmarks of the JSP. In these studied problems, there are numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed evolutionary approaches. The computational experiments show that the proposed TBGA approach can obtain both better and more robust results than those evolutionary methods reported recently.     

基于POX交叉的遗传算法求解Job-Shop调度问题

通过改进传统的遗传算法求解Job—Shop调度问题。为基于工序的编码提出了一种新的POX交叉算子，并与其他交叉算子进行了比较以显示其高效性。为了保留父代的优良特征和减少遗传算子的破坏性，设计了一种子代交替模式的交叉方式。将提出的改进遗传算法应用于muth and thompson‘s基准问题的实验运行，显示该算法的有效性。     

求解作业车间调度问题的一种改进遗传算法

为克服传统遗传算法解决车间作业调度问题的局限性,综合遗传算法和局部搜索的优点,提出一种改进的遗传算法。为基于工序的编码提出了一种新的POX交叉算子。同时,为克服传统遗传算法在求解车间作业调度问题时的早熟收敛,设计了一种子代交替模式的交叉方式,并运用局部搜索改善交叉和变异后得到的调度解,将提出的改进遗传算法应用于MuthandThompson基准问题的实验运行,显示了该算法的有效性。     

Improved genetic algorithm for the job-shop scheduling problem

In this paper, an improved genetic algorithm, called the hybrid Taguchi-genetic algorithm (HTGA), is proposed to solve the job-shop scheduling problem (JSP). The HTGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimal offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to systematically select the better genes to achieve crossover, and consequently enhance the genetic algorithm. Therefore, the proposed HTGA approach possesses the merits of global exploration and robustness. The proposed HTGA approach is effectively applied to solve the famous Fisher-Thompson benchmarks of 10 jobs to 10 machines and 20 jobs to 5 machines for the JSP. In these studied problems, there are numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed GA-based approaches. The computational experiments show that the proposed HTGA approach can obtain both better and more robust results than other GA-based methods reported recently.     

A Modified Genetic Algorithm for Job Shop Scheduling

As a class of typical production scheduling problems, job shop scheduling is one of the strongly NP-complete combinatorial optimisation problems, for which an enhanced genetic algorithm is proposed in this paper. An effective crossover operation for operation-based representation is used to guarantee the feasibility of the solutions, which are decoded into active schedules during the search process. The classical mutation operator is replaced by the metropolis sample process of simulated annealing with a probabilistic jumping property, to enhance the neighbourhood search and to avoid premature convergence with controllable deteriorating probability, as well as avoiding the difficulty of choosing the mutation rate. Multiple state generators are applied in a hybrid way to enhance the exploring potential and to enrich the diversity of neighbour-hoods. Simulation results demonstrate the effectiveness of the proposed algorithm, whose optimisation performance is markedly superior to that of a simple genetic algorithm and simulated annealing and is comparable to the best result reported in the literature.     

求解车间调度问题的改进型自适应遗传算法

针对作业车间调度问题,以最小化完工时间为目标,借鉴内分泌激素调节机制,提出了一种新颖的改进型自适应遗传算法.通过引入自适应交叉概率和变异概率因子,克服了传统的遗传算法在解决生产调度问题时存在的搜索精度低和收敛性难以控制等问题,并在Microsoft Visual C++6.0中实现了该算法.通过一个10工件、10机器作...     

求解作业车间调度的变邻域细菌觅食优化算法

针对最小化最大完工时间的作业车间调度问题,提出一种基于变邻域趋化操作的细菌觅食优化算法。邻域搜索是一类改进型局部搜索算法,在每一步迭代过程中通过搜索当前解的邻域得到一个改进的解,利用邻域搜索可大大提高局部最优解的精确度。本算法采用基于操作的编码,使得细菌觅食优化算法适用于作业车间调度求解;将3种不同的邻域结构引入趋化操作中,以便扩大可行解的搜索空间,细菌个体按照自适应学习策略根据邻域的各自贡献率选择搜索方式,减少陷入局部极小的机会;同时使用自适应步长更新各邻域内趋化操作的位置,根据适应度值动态调整搜索精度,避免早熟收敛。典型算例试验表明,该算法具有一定的鲁棒性,并有效地提高了搜索精度和收敛性。     

柔性作业车间调度问题的两级遗传算法

研究不同性能指标柔性作业车间调度问题的优化.针对柔性作业车间调度问题的特点,设计基于工序编码和基于机器分配编码的两种交叉和变异算子,并提出一种双层子代产生模式的改进遗传算法应用于该调度问题,以使子代更好地继承父代的优良特征.使用实例测试改进的遗传算法,并与其他遗传算法的测试结果进行比较,所提出算法的有效性得到证实.     

基于粒子群优化和模拟退火的混合调度算法

提出了一种离散粒子群调度算法，采用基于工序的编码方式及相应的位置和速度更新方法，使具有连续本质的粒子群算法直接适用于调度问题。针对粒子群算法容易陷入局部最优的缺陷，将其与模拟退火算法结合，得到了粒子群-模拟退火算法、改进的粒子群算法、粒子群-模拟退火交替算法以及粒子群-模拟退火协同算法等4种混合调度算法。仿真结果表明，混合算法均具有较高的求解质量。     

求解作业车间调度问题的广义粒子群优化算法

为克服传统粒子群优化算法在解决组合优化问题上的局限性，分析了其优化机理，并在此基础上提出了广义粒子群优化模型。按照此模型提出了一种求解作业车间调度问题的广义粒子群优化算法。在本算法中，利用遗传算法中的交叉操作作为粒子间的信息交换策略，利用遗传算法中的变异操作作为粒子的随机搜索策略，而粒子的局部搜索策略则采用禁忌搜索来实现。为了控制粒子的局部搜索以及向全局最优解的收敛，迭代过程中交叉概率以及禁忌搜索的最大步长都是动态变化的。实验结果表明，本算法可有效地求解作业车间调度问题，验证了广义粒子群优化模型的合理性。     

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。     

改进遗传算法求解柔性作业车间调度问题

分析柔性作业车间调度问题的特点,提出一种求解该问题的改进遗传算法。在考虑各个机器负荷平衡,所有机器上的总负荷和最大完工时间等性能指标更加合理情况下,设计一种全局搜索、局部搜索和随机产生相结合的初始化方法,提高种群初始解的质量,加快遗传算法的收敛速度。结合问题特点设计合理的染色体编码方式、交叉算子和变异算子,防止遗传操作过程中非法解的产生,避免染色体的修复,提高求解效率。使用文献中相同的实例测试利用初始化方法的改进遗传算法,并将计算结果与文献中其他遗传算法的测试结果进行比较,验证所提出的初始化方法的可行性和有效性。     

车间作业调度问题(job-shop)的一种改进遗传算法

搜索空间适应性的遗传算法(GSA)具有这样的能力,即使在不通过修改遗传算法的某些参数(倒如交叉率和变异率)的情况下,就可适应解空间的结构、并调节全局搜索和局部搜索的相互平衡.但是这种遗传算法(GSA)需有时个体特征继承率控制能力的交叉操作.文章阐述了一种改进的搜索空间适应性的遗传算法(mGSA)用于解决车间作业调度问题(JSP);这种方法不同于GSA不需要带特征继承率调节能力的交叉操作.最后通过两个benchmark问题的数字实验,展示了这种方法的的有效性;并通过与现存的遗传算法相比较,展示了这种方法有更好的结果.     

An intelligent concurrent design task planner for manufacturing systems

Product design is an integral component of manufacturing systems. This paper presents a prototype intelligent concurrent design task planner, which combines the strength of genetic algorithms and an iterative design analyser for the scheduling of a complex design process of a manufacturing system. It accounts significantly for shortening the time-to-market of a product, and hence, improves the agility of a manufacturing system. The proposed prototype attempts to schedule the design process inherently containing iteration, which is caused by the interdependencies among tasks and leads to prolonged lead-time and increased cost on the whole time-span of introducing a product. Genetic algorithm (GA), as one of the effective optimisation techniques, is embodied in the prototype to search for the optimal schedule for a design process for the goal of satisfying the managerial objective under resource constraints. The iterative design analyser, which is basically an analytical tool for design iteration, is utilised to estimate the time and engineering cost spent on the design process for each candidate schedule. Considering the unpredictable length of a schedule for iterative design process, a novel chromosome representation scheme and unique crossover and mutation operators have been introduced. A case study conducted on a burn-in system of a manufacturing company has illustrated the effectiveness of the proposed prototype.     

An improved genetic algorithm for robust permutation flowshop scheduling

In order to deal with uncertainties, a robust schedule for M-machine permutation flowshop is proposed. The presented robust schedule is aimed to maximize the probability of ensuring that makespan will not exceed the expected completion time. An improved genetic algorithm (GA) with a new generation scheme is developed, which can preserve good characteristics of parents in the new generation. Experiments are performed to get robust schedules for well-known Car and Rec permutation flowshop problems, taken from OR library. The schedules obtained from the improved GA are compared with the schedules formed by well-known heuristic in literature. Computational results show that the permutation flowshop schedules obtained from improved GA are robust to produce an affirmative percentage increase in the probability of getting makespan less than expected completion time.     

Job shop scheduling based on earliness and tardiness penalties with due dates and deadlines: an enhanced genetic algorithm

This paper studies a job shop scheduling problem with due dates and deadlines in the presence of tardiness and earliness penalties. Due dates are desired completion dates of jobs given by the customer, while deadlines are determined by the manufacturer based on customer due dates. Due dates can be violated at the cost of tardiness, whereas deadlines must be met and cannot be violated. The aforementioned scheduling problem, which is NP-hard, can be formulated with the objective function of minimizing the sum of weighted earliness and weighted tardiness of jobs subject to due dates and deadlines. In order to solve this problem, an enhanced genetic algorithm (EGA) is introduced in this paper. EGA utilizes an operation-based scheme to represent schedules as chromosomes. After the initial population of chromosomes is randomly generated, each chromosome is processed through a three-stage decoder, which first reduces tardiness based on due dates, second ensures deadlines are not violated, and finally reduces earliness based on due dates. After the population size is reached, EGA continues with selection, crossover, and mutation. The proposed algorithm is tested on 180 job shop scheduling problems of varying sizes and its performance is discussed.     

柔性设计任务协同调度算法

指出柔性多任务协同调度是一个NP难题,并分析了协同任务调度在协同设计系统中的重要性,提出一种基于遗传算法和模拟退火算法的混合算法,利用该算法实现设计任务的选择。设计二维结构的矩阵编码,并基于这种编码方式,提出行算子与列算子,融入约束条件,采用列交叉算子与列变异算子;为了加快群体的收敛性,采用精英保留策略;此外引入灾变算子,以保证群体的多样性;在个体生成过程中,考虑能力等相关因素对设计效果的影响,在解码过程中实现任务的时间调度与优化,并设计解码算法。通过实例仿真分析,所提出的混合遗传算法收敛速度快,寻优能力强。