自适应混合粒子群算法求解置换流水车间调度问题

为更有效地解决以最大完工时间最小化为目标的置换流水车间调度问题,提出了一种自适应混合粒子群算法（SHPSO）。该算法结合Q学习设计了参数自适应更新策略,以平衡算法的探索和开发;同时引入粒子停滞判断方法,使用平局决胜机制和Taillard加速算法改进基于迭代贪婪的局部搜索策略,对全局极值进行局部搜索,帮助粒子跳出局部最优。实验结果表明,SHPSO算法取得的平均相对百分偏差（RPDavg）对比其他四种改进PSO算法至少下降了83.2%,在求解质量上具有明显优势。     

A hybrid particle swarm optimization for job shop scheduling problem

A hybrid particle swarm optimization (PSO) for the job shop problem (JSP) is proposed in this paper. In previous research, PSO particles search solutions in a continuous solution space. Since the solution space of the JSP is discrete, we modified the particle position representation, particle movement, and particle velocity to better suit PSO for the JSP. We modified the particle position based on preference list-based representation, particle movement based on swap operator, and particle velocity based on the tabu list concept in our algorithm. Giffler and Thompson’s heuristic is used to decode a particle position into a schedule. Furthermore, we applied tabu search to improve the solution quality. The computational results show that the modified PSO performs better than the original design, and that the hybrid PSO is better than other traditional metaheuristics.     

求解流水车间调度问题的混合粒子群算法

针对最小化流水车间调度总完工时间问题,提出了一种混合的粒子群优化算法(Hybrid Particle Swarm Algorithm,HPSA),采用启发式算法产生初始种群,将粒子群算法、遗传操作以及局部搜索策略有效地结合在一起。用Taillard’s基准程序随机产生大量实例,实验结果显示:HPSA通过对种群选取方法的改进和搜索范围的扩大提高了解的质量,在性能上均优于目前较有效的启发式算法和混合的禁忌搜索算法,产生最好解的平均百分比偏差和标准偏差均显著下降,最优解所占比例大幅度提高。     

A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop

In this paper we address a hybrid flow shop scheduling problem considering the minimization of the sum of the total earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, we propose an ant colony optimization method to deal with this problem. Our proposed method has several features, including some heuristics that specifically take into account both earliness and tardiness penalties to compute the heuristic information values. The performance of our algorithm is tested by numerical experiments on a large number of randomly generated problems. A comparison with solutions performance obtained by some constructive heuristics is presented. The results show that the proposed approach performs well for this problem.     

求解job shop调度问题的量子粒子群优化算法*

针对粒子群优化算法搜索空间有限、容易出现早熟现象的缺陷,提出将量子粒子群优化算法用于求解作业车间调度问题。求解时,将每个调度按照一定的规则编码为一个矩阵,并以此矩阵作为算法中的粒子;然后根据调度目标确定目标函数,并按照量子粒子群优化算法的进化规则在调度空间内搜索最优解。仿真实例结果证明,该算法具有良好的全局收敛性能和快捷的收敛速度,调度效果优于遗传算法和粒子群优化算法。     

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.     

改进猫群算法求解置换流水车间调度问题

标准猫群算法（CSO）在求解最小化最大完工时间的置换流水车间调度问题（PFSP）时收敛速度较慢，同时，当问题规模变大时容易出现“维数灾难”。为加快寻优速度，同时避免“维数灾难”，提出了一种基于分布估计算法的改进猫群算法（EDA-CSO）。以猫群算法为框架，嵌入分布估计算法，在搜寻模式下，利用概率矩阵挖掘解序列中的优秀基因链组合区块，使用猫群算法中的跟踪模式更新猫的速度和位置，从而更新优秀解序列产生子群体。最后，通过对Carlier和Reeves标准例题集的仿真测试和结果比较，验证了该算法良好的鲁棒性和全局搜索能力。     

A new particle swarm optimization for the open shop scheduling problem

This paper presents a new particle swarm optimization (PSO) for the open shop scheduling problem. Compared with the original PSO, we modified the particle position representation using priorities, and the particle movement using an insert operator. We also implemented a modified parameterized active schedule generation algorithm (mP-ASG) to decode a particle position into a schedule. In mP-ASG, we can reduce or increase the search area between non-delay schedules and active schedules by controlling the maximum delay time allowed. Furthermore, we hybridized our PSO with beam search. The computational results show that our PSO found many new best solutions of the unsolved problems.     

离散微粒群算法求解批量流水线调度问题

提出了解决批量流水线调度问题的离散微粒群优化算法。该算法采用了基于工序的编码方式,设计了新的粒子生成公式,从而使微粒群算法可以直接应用于调度问题。同时,针对微粒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,得到了改进的微粒群优化算法。仿真实验表明了上述算法的有效性。     

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

This study presents a simulation optimization approach for a hybrid flow shop scheduling problem in a real-world semiconductor back-end assembly facility. The complexity of the problem is determined based on demand and supply characteristics. Demand varies with orders characterized by different quantities, product types, and release times. Supply varies with the number of flexible manufacturing routes but is constrained in a multi-line/multi-stage production system that contains certain types and numbers of identical and unrelated parallel machines. An order is typically split into separate jobs for parallel processing and subsequently merged for completion to reduce flow time. Split jobs that apply the same qualified machine type per order are compiled for quality and traceability. The objective is to achieve the feasible minimal flow time by determining the optimal assignment of the production line and machine type at each stage for each order. A simulation optimization approach is adopted due to the complex and stochastic nature of the problem. The approach includes a simulation model for performance evaluation, an optimization strategy with application of a genetic algorithm, and an acceleration technique via an optimal computing budget allocation. Furthermore, scenario analyses of the different levels of demand, product mix, and lot sizing are performed to reveal the advantage of simulation. This study demonstrates the value of the simulation optimization approach for practical applications and provides directions for future research on the stochastic hybrid flow shop scheduling problem.     

An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP. However, they are very difficult to solve multi-objective FJSP very well. In this paper, a particle swarm optimization (PSO) algorithm and a tabu search (TS) algorithm are combined to solve the multi-objective FJSP with several conflicting and incommensurable objectives. PSO which integrates local search and global search scheme possesses high search efficiency. And, TS is a meta-heuristic which is designed for finding a near optimal solution of combinatorial optimization problems. Through reasonably hybridizing the two optimization algorithms, an effective hybrid approach for the multi-objective FJSP has been proposed. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale.     

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.     

Elucidating multiprocessors flow shop scheduling with dependent setup times using a twin particle swarm optimization

Particle swarm optimization (PSO) is a novel metaheuristic, which has been applied in a wide variety of production scheduling problems. Two basic characteristics of this algorithm are its efficiency and effectiveness in providing high-quality solutions. In order to improve the traditional PSO, this study proposes the incorporation of a local search heuristic into the basic PSO algorithm. The new, hybrid, metaheuristic is called “twin particle swarm optimization (TPSO)”. The proposed metaheuristic scheme is applied to a flow shop with multiprocessors scheduling problem, which can be considered a real world case regarding the production line. This study, as far as the multiprocessors flow shop production system is concerned, utilizes sequence dependent setup times as constraints. Finally, simulated data confirm the effectiveness and robustness of the proposed algorithm. The data test results indicate that TPSO has potential to replace PSO and become a significant heuristic algorithm for similar problems.     

利用免疫粒子群算法解决排课问题

为解决排课当中的资源合理分配问题,寻求一种合理的解决方案,提出一种带免疫量子行为的粒子群智能优化算法.将粒子群中的粒子当作抗体,给粒子的生成加入免疫记忆机制,迭代开始后,使用抗体浓度指导粒子种群向更优方向移动.改进后的方法能避免粒子陷入局部最优和早熟收敛,用以解决这种多约束、多目标的组合排课问题.最后进行实验仿真,仿真结果表明了该新算法在解决实际问题中的有效性与优越性.     

A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems

In the real-world applications, most optimization problems are subject to different types of constraints. These problems are known as constrained optimization problems (COPs). Solving COPs is a very important area in the optimization field. In this paper, a hybrid multi-swarm particle swarm optimization (HMPSO) is proposed to deal with COPs. This method adopts a parallel search operator in which the current swarm is partitioned into several subswarms and particle swarm optimization (PSO) is severed as the search engine for each sub-swarm. Moreover, in order to explore more promising regions of the search space, differential evolution (DE) is incorporated to improve the personal best of each particle. First, the method is tested on 13 benchmark test functions and compared with three stateof-the-art approaches. The simulation results indicate that the proposed HMPSO is highly competitive in solving the 13 benchmark test functions. Afterward, the effectiveness of some mechanisms proposed in this paper and the effect of the parameter setting were validated by various experiments. Finally, HMPSO is further applied to solve 24 benchmark test functions collected in the 2006 IEEE Congress on Evolutionary Computation (CEC2006) and the experimental results indicate that HMPSO is able to deal with 22 test functions.     

粒子群算法求解具有机器灵活性的FFSP

对柔性流水车间调度问题(FFSP)进行了分析阐述,在此基础上对某饲料厂的饲料生产过程建立了具有机器灵活性的柔性流水车间调度模型,该模型中存在多台制粒机,既能加工大颗粒饲料,又能加工小颗粒饲料,但是必须在开始加工之前确定各台机器的用途,增加了柔性流水车间调度的难度。利用新型的粒子群算法以最小化最大完工时间为目标对该模型求解,为了克服粒子群算法易陷入局部极值的缺点,提出基于位置相似度的邻域结构,并对邻域内的较优粒子采用基于最大完工时间排序的学习方式进行局部搜索。实验结果表明,该方法有利于克服粒子群算法的早熟缺陷,有效地解决了饲料生产调度问题,有一定的应用价值。     

一种新的混合粒子群算法求解置换流水车间调度问题

针对粒子群算法易早熟的缺点,提出了一种结合迭代贪婪(IG)算法的混合粒子群算法。算法通过连续几代粒子个体极值和全局极值的变化判断粒子的状态,在发现粒子出现停滞或者粒子群出现早熟后,及时利用IG算法的毁坏操作和构造操作对停滞粒子和全局最优粒子进行变异,变异后利用模拟退火思想概率接收新值。全局最优粒子的改变会引导粒子跳出局部极值的约束,增加粒子的多样性,从而克服粒子群的早熟现象。同时,为了使算法能更快找到或逼近最优解,采用了循环迭代策略,在阶段优化结果的基础上,周而复始循环迭代进行求解。将提出的混合粒子群算法应用于置换流水车间调度问题,并在问题求解时与几个具有代表性的算法进行了比较。结果表明,提出的算法能够克服粒子群早熟,在求解质量方面优于其他算法。     

A hybrid heuristic to solve the parallel machines job-shop scheduling problem

This paper presents an advanced software system for solving the flexible manufacturing systems (FMS) scheduling in a job-shop environment with routing flexibility, where the assignment of operations to identical parallel machines has to be managed, in addition to the traditional sequencing problem. Two of the most promising heuristics from nature for a wide class of combinatorial optimization problems, genetic algorithms (GA) and ant colony optimization (ACO), share data structures and co-evolve in parallel in order to improve the performance of the constituent algorithms. A modular approach is also adopted in order to obtain an easy scalable parallel evolutionary-ant colony framework. The performance of the proposed framework on properly designed benchmark problems is compared with effective GA and ACO approaches taken as algorithm components.     

基于多区域采样策略的混合粒子群优化求解多目标柔性作业车间调度问题

柔性作业车间调度问题(FJSP)是一类应用广泛的组合优化问题.针对多目标FJSP求解过程复杂、算法易陷入局部最优的问题,提出了一种基于多区域采样策略的混合粒子群优化算法(HPSO-MRS),以同时优化最大完工时间和总机器延迟时间这两个目标.多区域采样策略能够区分粒子所在Pareto前沿面的位置,根据不同区域进行采样重组...     

An improved bat optimization algorithm to solve the tasks scheduling problem in open shop

Neural Computing and Applications - The open shop scheduling problem involves a set of activities that should be run on a limited set of machines. The purpose of scheduling open shops problem is to...