共查询到20条相似文献,搜索用时 15 毫秒
1.
R. J. Kuo W. C. Cheng 《The International Journal of Advanced Manufacturing Technology》2013,67(1-4):59-71
This study intends to solve the job shop scheduling problem with both due data time window and release time. The objective is to minimize the sum of earliness time and tardiness time in order to reduce the storage cost and enhance the customer satisfaction. A novel hybrid meta-heuristic which combines ant colony optimization (ACO) and particle swarm optimization (PSO), called ant colony–particle swarm optimization (ACPSO), is proposed to solve this problem. Computational results indicate that ACPSO performs better than ACO and PSO. 相似文献
2.
Jingsheng Li Aimin Wang Chengtong Tang 《The International Journal of Advanced Manufacturing Technology》2014,74(1-4):47-64
This paper focuses on the scheduling problem of the reconfiguration manufacturing system (RMS) for execution level, where the final objective is to output a production plan. The practical situation in Chinese factory is analyzed, and the characteristics are summarized into the contradiction between flow and job shop production. In order to handle this problem, a new production planning algorithm in virtual cells is proposed for RMS using an improved genetic algorithm. The advantages of this algorithm have three parts: (1) the virtual cell reconfiguration is formed to assist making production plans through providing relationship among task families and machines from cell formation; (2) The operation buffer algorithm is developed for flow style production in cells, which can realize the nonstop processing for flow style jobs; and (3) The multicell sharing method is proposed to schedule job shop jobs in order to fully utilize manufacturing capability among machines in multicells. Based on the above advantages, an improved genetic algorithm is developed to output scheduling plan. At last, the algorithm is tested in different instances with LINGO and the other genetic algorithm, and then the scheduling solution comparison shows the proposed algorithm can get a better optimum result with the same time using the comparison algorithm. 相似文献
3.
An intelligent operations scheduling system in a job shop 总被引:1,自引:1,他引:0
Dr Jihyung Park Mujin Kang Kyoil Lee 《The International Journal of Advanced Manufacturing Technology》1996,11(2):111-119
Scheduling jobs effectively under the consideration of actual loads on machines is one of the most complicated tasks in production control. The conventional scheduling methods fail because of the complexity of the tasks. To deal with the complexity, knowledge-based approaches to job shop scheduling have been attempted. This paper presents an interactive scheduling expert system, IOSS (Intelligent Operations Scheduling System), which performs both predictive and reactive scheduling. IOSS combines the knowledge-based scheduling method with the interactive scheduling method to generate a feasible schedule and to revise the existing schedule. It is based on opportunistic and interactive repair based problem solving within a blackboard architecture. To handle conflicting events, heuristics are applied from the order point of view. Flexible reaction management is possible while keeping the changes in the generated schedule to a minimum by adjusting the schedule for tardy operations or changes in job shop status. The effectiveness of the proposed concept is demonstrated by applying the developed system to an example case. 相似文献
4.
Hybridizing tabu search with ant colony optimization for solving job shop scheduling problems 总被引:1,自引:1,他引:0
V. P. Eswaramurthy A. Tamilarasi 《The International Journal of Advanced Manufacturing Technology》2009,40(9-10):1004-1015
The manufacturing industry continues to be a prime contributor and it requires an efficient schedule. Scheduling is the allocation of resources to activities over time and it is considered to be a major task done to improve shop-floor productivity. Job shop problem comes under this category and is combinatorial in nature. Research on optimization of the job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the global optimization technique called tabu search that is combined with the ant colony optimization technique to solve the job shop scheduling problems. The neighborhoods are selected based on the strategies in the ant colony optimization with dynamic tabu length strategies in the tabu search. The inspiring source of ant colony optimization is pheromone trail that has more influence in selecting the appropriate neighbors to improve the solution. The performance of the algorithm is tested using well-known benchmark problems and is also compared with other algorithms in the literature. 相似文献
5.
基于混合遗传算法的车间调度问题的研究 总被引:5,自引:0,他引:5
作业车间调度问题是最困难的组合优化问题之一,也是计算机集成制造系统中的一个关键环节,在实际生产中具有广泛应用。为此,提出了实现车间调度的混合遗传算法的设计方案,把遗传算法与模拟退火算法相结合,充分发挥遗传算法良好的全局搜索能力和模拟退火算法有效避免陷入局部极小的特性。通过实验验证了基于GASA混合算法的作业车间调度方法显著提高了搜索效率,改进了收敛性能。 相似文献
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Yong Ming Wang Nan Feng Xiao Hong Li Yin En Liang Hu Cheng Gui Zhao Yan Rong Jiang 《The International Journal of Advanced Manufacturing Technology》2008,39(7-8):813-820
The majority of large size job shop scheduling problems are non-polynomial-hard (NP-hard). In the past few decades, genetic algorithms (GAs) have demonstrated considerable success in providing efficient solutions to many NP-hard optimization problems. But there is no literature available considering the optimal parameters when designing GAs. Unsuitable parameters may generate an inadequate solution for a specific scheduling problem. In this paper, we proposed a two-stage GA which attempts to firstly find the fittest control parameters, namely, number of population, probability of crossover, and probability of mutation, for a given job shop problem with a fraction of time using the optimal computing budget allocation method, and then the fittest parameters are used in the GA for a further searching operation to find the optimal solution. For large size problems, the two-stage GA can obtain optimal solutions effectively and efficiently. The method was validated based on some hard benchmark problems of job shop scheduling. 相似文献
8.
Rong-Hwa Huang Shun-Chi Yu Chen-Wei Kuo 《The International Journal of Advanced Manufacturing Technology》2014,71(5-8):1263-1276
Reentrant flow shop scheduling allows a job to revisit a particular machine several times. The topic has received considerable interest in recent years; with related studies demonstrating that particle swarm algorithm (PSO) is an effective and efficient means of solving scheduling problems. By selecting a wafer testing process with the due window problem as a case study, this study develops a farness particle swarm optimization algorithm (FPSO) to solve reentrant two-stage multiprocessor flow shop scheduling problems in order to minimize earliness and tardiness. Computational results indicate that either small- or large-scale problems are involved in which FPSO outperforms PSO and ant colony optimization with respect to effectiveness and robustness. Importantly, this study demonstrates that FPSO can solve such a complex scheduling problem efficiently. 相似文献
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10.
R.K. Suresh K.M. Mohanasundaram 《The International Journal of Advanced Manufacturing Technology》2006,29(1-2):184-196
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. 相似文献
11.
目前的研究者对于车间作业调度问题的研究,多将其抽象为一个大家所熟知的JSP模型.显然,这样的模型无法适应具体的企业应用.针对以往的研究与实际应用脱节的问题,并根据单件小批量生产企业生产调度中的一些基本情况,提出一种解决方案,并将这种解决方法应用在模具制造单件小批量生产类型企业,从而使计划调度的理论研究服务于实际生产. 相似文献
12.
Bud Fox Wei Xiang Heow Pueh Lee 《The International Journal of Advanced Manufacturing Technology》2007,31(7-8):805-814
The ant colony optimization (ACO) algorithm is a fast suboptimal meta-heuristic based on the behavior of a set of ants that
communicate through the deposit of pheromone. It involves a node choice probability which is a function of pheromone strength
and inter-node distance to construct a path through a node-arc graph. The algorithm allows fast near optimal solutions to
be found and is useful in industrial environments where computational resources and time are limited. A hybridization using
iterated local search (ILS) is made in this work to the existing heuristic to refine the optimality of the solution. Applications
of the ACO algorithm also involve numerous traveling salesperson problem (TSP) instances and benchmark job shop scheduling
problems (JSSPs), where the latter employs a simplified ant graph-construction model to minimize the number of edges for which
pheromone update should occur, so as to reduce the spatial complexity in problem computation. 相似文献
13.
Jian Fang Yugeng Xi 《The International Journal of Advanced Manufacturing Technology》1997,13(3):227-232
In this paper, the job shop scheduling problem in a dynamic environment is studied. Jobs arrive continuously, machines breakdown, machines are repaired and due dates of jobs may change during processing. Inspired by the rolling horizon optimisation method from predictive control technology, a periodic and event-driven rolling horizon scheduling strategy is presented and adapted to continuous processing in a changing environment. The scheduling algorithm is a hybrid of genetic algorithms and dispatching rules for solving the job shop scheduling problem with sequence-dependent set-up time and due date constraints. Simulation results show that the proposed strategy is more suitable for a dynamic job shop environment than the static scheduling strategy. 相似文献
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Tamer F. Abdelmaguid Ashraf O. Nassef 《The International Journal of Advanced Manufacturing Technology》2010,46(9-12):1239-1251
This paper extends the traditional job shop scheduling problem (JSP) by incorporating the routing and scheduling decisions of the material handling equipment. It provides a generic definition and a mixed integer linear programming model for the problem considering the case of heterogeneous multiple-load material handling equipment. A constructive heuristic is developed for solving the problem. This heuristic is based on the well-known Giffler and Thompson’s algorithm for the JSP with modifications that account for the routing decisions of the material handling equipment and their effect on the start times of the manufacturing operations. Different dispatching rules are integrated into the heuristic, and experiments are conducted to study their effect on the makespan along with the determination of the computational time requirements of the developed heuristic. 相似文献
16.
Fardin Ahmadizar Alireza Zarei 《The International Journal of Advanced Manufacturing Technology》2013,66(9-12):2063-2074
This paper deals with a fuzzy group shop scheduling problem. The group shop scheduling problem is a general formulation that includes the flow shop, the job shop, and the open shop scheduling problems. Job release dates and processing times are considered to be triangular fuzzy numbers. The objective is to find a job schedule that minimizes the maximum completion time or makespan. First, the problem is formulated in a form of fuzzy programming and then prepared in a form of deterministic mixed binary integer linear programming by applying the chance-constrained programming. To solve the problem, an efficient genetic algorithm hybridized with an improvement procedure is developed. Both Lamarckian and Baldwinian versions are then implemented and evaluated through computational experiments. 相似文献
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MULTI-SHOP SCHEDULING PROBLEM 总被引:2,自引:1,他引:1
HU Yanhai Institute of CIM Shanghai Jiaotong University Shanghai China Faculty of Engineering Ningbo University Ningbo China YAN Junqi MA Dengzhe Institute of CIM Shanghai Jiaotong University Shanghai China YE Feifan Faculty of Engineering Ningbo University Ningbo China ZHANG Jie Institute of CIM Shanghai Jiaotong University Shanghai China 《机械工程学报(英文版)》2007,20(3):109-112
A new concept of multi-shop (M ) is put forward which contains all basic shops including open shop (O), job shop (J ), flow shop (F ) and hybrid flow shop (H ) so that these basic shop can be scheduled together. Several algorithms including ant colony optimization (ACO), most work remaining (MWR), least work remaining (LWR), longest processing time (LPT) and shortest processing time (SPT) are used for scheduling the M. Numerical experiments of the M adopting data of some car and reC series benchmark instances are tested. The results show that the ACO algorithm has better performance for scheduling the M than the other algorithms, if minimizing the makespan ( C m*ax) is taken as the objective function. As a comparison, the separate shops contained in the M are also scheduled by the ACO algorithm for the same objective function, when the completing time of the jobs in the previous shop is taken as the ready time of these jobs in the following shop. The results show that the M has the advantage of shortening the makespan upon separate shops. 相似文献
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20.
Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem 总被引:1,自引:1,他引:0
Seyed Habib A. Rahmati M. Zandieh M. Yazdani 《The International Journal of Advanced Manufacturing Technology》2013,64(5-8):915-932
The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm. 相似文献