解决零空闲流水线调度问题的离散粒子群算法

研究了以最大完工时间为目标的零空闲流水线调度问题.提出一种复杂度为O(nm)的最大完工时间算法和一种快速插入邻域搜索算法;提出了解决该问题的离散粒子群调度算法,并结合简化邻域搜索算法给出了提高调度算法性能的措施.仿真实验表明了所得算法的有效性.     

改进离散粒子群算法求解柔性流水车间调度问题

针对以最小化完工时间为目标的柔性流水车间调度问题(FFSP),提出了一种改进离散粒子群(DPSO)算法.所提算法重新定义粒子速度和位置的相关算子,并引入编码矩阵和解码矩阵来表示工件、机器以及调度之间的关系.为了提高柔性流水车间调度问题求解的改进离散粒子群算法的初始群体质量,通过分析初始机器选择与调度总完工时间的关系,首次提出一种基于NEH算法的最短用时分解策略算法.仿真实验结果表明,该算法在求解柔性流水车间调度问题上有很好的性能,是一种有效的调度算法.     

基于改进离散粒子群禁忌算法的并行测试任务调度

针对并行测试任务调度问题特点,提出了一种解决并行测试任务调度的离散粒子群禁忌算法,设计了符合并行测试任务调度特点的粒子初始化编码方式、粒子位置转移公式以及禁忌搜索邻域。通过与禁忌搜索相结合,避免了离散粒子群算法早熟问题。通过实验以及算法对比,表明提出的算法是快速有效的,能够很好地解决并行测试任务调度问题,求得测试总时间最短的任务调度序列。     

基于粒子群优化和禁忌搜索的混合调度算法

提出了用于解决作业车间调度问题的离散版粒子群优化算法。该算法采用基于先后表编码方案和新的位移更新模型,使具有连续本质的粒子群优化算法直接适用于车间调度问题。同时,利用粒子群优化算法的全局搜索能力和禁忌搜索算法的自适应优点,将粒子群优化算法和禁忌搜索结合起来,设计了广义粒子群优化算法和粒子群—禁忌搜索交替算法两种混合调度算法。实验结果表明,两种混合调度算法能够有效地、高质量地解决作业车间调度问题。     

基于新邻域结构的Memetic 算法求解流水车间调度问题

流水车间调度是一类典型的生产调度问题,属于NP-难问题.针对传统的最优化方法难以求解大规模问题,提出了一个Memetic算法,在算法的局部搜索中使用一种新型的基于NEH的邻域结构,并且其邻域规模随着搜索的进行能够动态变化,可以大大提高算法的搜索能力.通过对标准Benchmark问题的测试,所得结果表明提出的基于新邻域结构的Memetic算法具有较好的性能,并且优于已有文献中的粒子群算法.     

基于改进粒子群算法求解柔性作业车间批量调度问题

基于工序排序和机器分配的粒子编码方式,提出一种新的粒子位置更新方式,该方式使得粒子群算法更新可以直接在离散域执行.通过对工件工序进行多次机器分配来扩大搜索范围,引入改进的模拟退火算法,用以增强粒子群算法的邻域搜索能力,实现全局搜索与局部搜索能力的有效平衡.最后通过数值算例以及某电声企业纸盆车间批量调度的应用实例验证了所提出算法的有效性和可行性.     

离散车间多目标调度算法探究

随着科学技术的快速发展和客户需求的不断提高,传统大批量生产的模式逐渐被淘汰,离散制造这种小批量的生产模式将逐渐成为制造业的主流形式之一。中国制造业近些年发展迅速,在世界上打造了“中国制造”的“品牌”,离散制造这一主流模式也将成为我国制造业的发展目标之一。本文首先综述了离散型车间调度的发展背景,阐述了离散型车间多目标调度问题的模型,介绍了粒子群算法和混合蛙跳算法两种多目标调度算法。最后,对离散车间多目标调度算法的研究方向提出几点建议。     

求解流水车间批量流集成调度的离散入侵杂草优化算法

提出一种离散入侵杂草优化算法,用来解决最大完工时间目标的流水车间批量流集成调度问题.该调度问题包含两个紧密耦合的子问题:批次分割问题和考虑启动时间的批次调度问题.设计了两段字符串编码,用来表示两个子问题.与基本入侵杂草优化算法不同,所提算法基于适应度和年龄确定杂草种子数量,基于正切函数和连续邻域操作产生种子.8种邻域算子的混合应用与局部搜索增强了算法的求解能力.仿真实验表明了所提算法的有效性.     

直觉模糊离散粒子群算法

在研究和分析离散粒子群算法(DBPSO)的基础上,提出一种基于直觉模糊熵的改进离散粒子群算法(IFDPSO).该算法以直觉模糊熵作为粒子群状态测度和速度变异的基本参数,同时加入了位置变异策略以保证算法在有限时间内尽可能多地遍历到次优位置及其邻域,增强了算法的全局寻优能力.实验数据表明,在求解较大规模整数规划问题(如0-1背包问题)时,IFDPSO比DPSO和蚁群算法(ACO)更为有效,从而为解决这类问题提供了新的途径和方法.     

基于改进DPSO的网格资源调度算法

基于市场经济模型的网格资源调度问题是一个典型的离散问题及NP-Hard问题,考虑到离散粒子群优化算法在解决离散问题上的有效性,本文在现有算法的研究基础上,提出一种基于改进的离散粒子群优化算法的网格资源分配和任务调度算法,并采用GridSim模拟器对相关算法进行仿真模拟实验和比较。实验结果表明,本文提出的调度算法在作业完成时间、综合性能以及资源的负载平衡方面均具有较大的优势。     

A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem

In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the no-wait flowshop scheduling problem with both makespan and total flowtime criteria. The main contribution of this study is due to the fact that particles are represented as discrete job permutations and a new position update method is developed based on the discrete domain. In addition, the DPSO algorithm is hybridized with the variable neighborhood descent (VND) algorithm to further improve the solution quality. Several speed-up methods are proposed for both the swap and insert neighborhood structures. The DPSO algorithm is applied to both 110 benchmark instances of Taillard [Benchmarks for basic scheduling problems. European Journal of Operational Research 1993;64:278–85] by treating them as the no-wait flowshop problem instances with the total flowtime criterion, and to 31 benchmark instances provided by Carlier [Ordonnancements a contraintes disjonctives. RAIRO Recherche operationelle 1978;12:333–51], Heller [Some numerical experiments for an M×J$\mathrm{M}\times \mathrm{J}$ flow shop and its decision-theoretical aspects. Operations Research 1960;8:178–84], and Revees [A genetic algorithm for flowshop sequencing. Computers and Operations Research 1995;22:5–13] for the makespan criterion. For the makespan criterion, the solution quality is evaluated according to the reference makespans generated by Rajendran [A no-wait flowshop scheduling heuristic to minimize makespan. Journal of the Operational Research Society 1994;45:472–8] whereas for the total flowtime criterion, it is evaluated with the optimal solutions, lower bounds and best known solutions provided by Fink and Voß [Solving the continuous flow-shop scheduling problem by metaheuristics. European Journal of Operational Research 2003;151:400–14]. The computational results show that the DPSO algorithm generated either competitive or better results than those reported in the literature. Ultimately, 74 out of 80 best known solutions provided by Fink and Voß [Solving the continuous flow-shop scheduling problem by metaheuristics. European Journal of Operational Research 2003;151:400–14] were improved by the VND version of the DPSO algorithm.     

A discrete particle swarm optimization algorithm with self-adaptive diversity control for the permutation flowshop problem with blocking

This paper proposes a discrete particle swarm optimization (DPSO) algorithm for the m-machine permutation flowshop scheduling problem with blocking to minimize the makespan, which has a strong industrial background, e.g., many production processes of chemicals and pharmaceuticals in chemical industry can be reduced to this problem. To prevent the DPSO from premature convergence, a self-adaptive diversity control strategy is adopted to diversify the population when necessary by adding a random perturbation to the velocity of each particle according to a probability controlled by the diversity of the current population. In addition, a stochastic variable neighborhood search is used as the local search to improve the search intensification. Computational results using benchmark problems show that the proposed DPSO algorithm outperforms previous algorithms proposed in the literature and that it can obtain 111 new best known upper bounds for the 120 benchmark problems.     

Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling

Factory management plays an important role in improving the productivity and quality of service in the production process. In particular, the distributed permutation flow shop scheduling problem with multiple factories is considered a priority factor in the factory automation. This study proposes a novel model of the developed distributed scheduling by supplementing the reentrant characteristic into the model of distributed reentrant permutation flow shop (DRPFS) scheduling. This problem is described as a given set of jobs with a number of reentrant layers is processed in the factories, which compromises a set of machines, with the same properties. The aim of the study is to determine the number of factory needs to be used, jobs assignment to certain factory and sequence of job assigned to the factory in order to simultaneously satisfy three objectives of minimizing makespan, total cost and average tardiness. To do this, a novel multi-objective adaptive large neighborhood search (MOALNS) algorithm is developed for finding the near optimal solutions based on the Pareto front. Various destroy and repair operators are presented to balance between intensification and diversification of searching process. The numerical examples of computational experiments are carried out to validate the proposed model. The analytical results on the performance of proposed algorithm are checked and compared with the existing methods to validate the effectiveness and robustness of the proposed potential algorithm in handling the DRPFS problem.     

An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers

In this paper, an effective hybrid discrete differential evolution (HDDE) algorithm is proposed to minimize the maximum completion time (makespan) for a flow shop scheduling problem with intermediate buffers located between two consecutive machines. Different from traditional differential evolution algorithms, the proposed HDDE algorithm adopted job permutation to represent individuals and applies job-permutation-based mutation and crossover operations to generate new candidate solutions. Moreover, a one-to-one selection scheme with probabilistic jumping is used to determine whether the candidates will become members of the target population in next generation. In addition, an efficient local search algorithm based on both insert and swap neighborhood structures is presented and embedded in the HDDE algorithm to enhance the algorithm’s local searching ability. Computational simulations and comparisons based on the well-known benchmark instances are provided. It shows that the proposed HDDE algorithm is not only capable to generate better results than the existing hybrid genetic algorithm and hybrid particle swarm optimization algorithm, but outperforms two recently proposed discrete differential evolution (DDE) algorithms as well. Especially, the HDDE algorithm is able to achieve excellent results for large-scale problems with up to 500 jobs and 20 machines.     

A hybrid discrete differential evolution algorithm for the no-idle permutation flow shop scheduling problem with makespan criterion

This paper presents a hybrid discrete differential evolution (HDDE) algorithm for the no-idle permutation flow shop scheduling problem with makespan criterion, which is not so well studied. The no-idle condition requires that each machine must process jobs without any interruption from the start of processing the first job to the completion of processing the last job. A novel speed-up method based on network representation is proposed to evaluate the whole insert neighborhood of a job permutation and employed in HDDE, and moreover, an insert neighborhood local search is modified effectively in HDDE to balance global exploration and local exploitation. Experimental results and a thorough statistical analysis show that HDDE is superior to the existing state-of-the-art algorithms by a significant margin.     

A tabu search algorithm based on new block properties and speed-up method for permutation flow-shop with finite intermediate storage

This paper deals with a permutation flow-shop scheduling problem with finite intermediate storage (PFSFIS) between successive machines so as to minimize makespan. In such a problem the intermediate storage capacity constraints are considered besides the machine-related constraints usually discussed in the general permutation flow-shop. This feature adds extra difficulties to the scheduling problem. In this paper, we present some new block properties and a speed-up method using a forward-backward hybrid algorithm to compute makespan. Applied in a tabu search algorithm, the new block properties greatly reduce the neighborhood size and thus shorten the search time. Also, the speed-up method eliminates redundant computation for the objective function and reduces a majority of the running time. Computational experiments (up to 200 jobs and 20 machines) are given to demonstrate the effectiveness of new block neighborhood characteristics and the speed-up method. Compared with the results yielded by the best-known algorithm, the objective function is improved by 0.14% if the new block neighborhood characteristics are used; furthermore, the running time is reduced by 53.7% on the average if the speed-up method is used. Under the condition that both of the algorithms have the same running time the objective function is improved by 0.24% if both of the two above improvement methods are applied in the original tabu search.     

蛙跳优化算法求解多目标无等待流水线调度

提出了基于Pareto边界和档案集的改进蛙跳算法,解决以最大完工时间、最大拖后时间和总流经时间为目标值的无等待流水线调度问题．首先,采用NEH（Nawaz—Enscore—Ham）启发式与随机解相结合的初始化方法,保证了初始群体的质量和分布性;其次,采用两点交叉方法生成新解,使蛙跳算法能够直接用于解决调度问题;再次,利用非支配解集动态更新群体,改善了群体的质量和多样性;最后,将基于插入邻域的快速局部搜索算法嵌入到蛙跳算法中,增强了算法的开发能力和效率．仿真试验表明了所得蛙跳算法的有效性和高效性．     

A variable neighborhood descent heuristic for the problem of makespan minimisation on unrelated parallel machines with setup times

The NP-hard problem of scheduling jobs on unrelated parallel machines, in the presence of machine-dependent and sequence-dependent setup times, with the objective of minimizing the makespan, is considered. A variable neighborhood descent search algorithm, hybridized with mathematical programming elements, is presented and its performance is evaluated on a large set of benchmark problem instances. The extensive computational experiments show that the proposed algorithm outperforms previously proposed methods in terms of solution quality as well as computation time.     

An effective neighborhood search algorithm for scheduling a flow shop of batch processing machines

This paper considers scheduling problem of flow shop with many batch processing machines and objective of maximum lateness. An effective neighborhood search algorithm (NSA) is proposed for the problem, in which a job permutation and a batch permutation are used to indicate the solution of two sub-problems, respectively. Each job permutation consists of several family-permutations for the representation of jobs from the same family. Two swaps are applied to two permutations to produce new solutions. NSA is applied to a number of instances and compared with some methods, and computational results validate the good performance of NSA.