首页 | 官方网站   微博 | 高级检索  
相似文献
 共查询到10条相似文献,搜索用时 203 毫秒
1.
Binary machine-part matrices have been widely used to identify machine groups and part families. The methods based on binary machine-part matrices mostly focus on the reduction of setup times and material handling costs. However, some other objectives such as the maximization of within-cell utilization and minimization of workload imbalance may not be achieved without considering other important factors such as processing times, lot sizes and machine capacities. Ignoring the processing times may violate the capacity constraints, and thus lead to an infeasible solution. This paper proposes a generalized grouping efficiency considering processing times and lot sizes. A simulated annealing algorithm is developed to solve the grouping problem and a neural network approach is used to provide a seed solution. Our computational experience indicates that the proposed algorithm is able to find a near optimum solution with less number of duplicated machines and better workload balance as compared to the approach reported in the literature.  相似文献   

2.
This paper reports a new genetic algorithm (GA) for solving a general machine/part grouping (GMPG) problem. In the GMPG problem, processing times, lot sizes and machine capacities are all explicitly considered. To evaluate the solution quality of this type of grouping problems, a generalized grouping efficacy index is used as the performance measure and fitness function of the proposed genetic algorithm. The algorithm has been applied to solving several well-cited problems with randomly assigned processing times to all the operations. To examine the effects of the four major factors, namely parent selection, population size, mutation rate, and crossover points, a large grouping problem with 50 machines and 150 parts has been generated. A multi-factor (34) experimental analysis has been carried out based on 324 GA solutions. The multi-factor ANOVA test results clearly indicate that all the four factors have a significant effect on the grouping output. It is also shown that the interactions between most of the four factors are significant and hence their cross effects on the solution should be also considered in solving GMPG problems.  相似文献   

3.
This paper addresses the cell formation problem with alternative part routings, considering machine capacity constraints. Given processes, machine capacities and quantities of parts to produce, the problem consists in defining the preferential routing for each part optimising the grouping of machines into manufacturing cells. The main objective is to minimise the inter-cellular traffic, while respecting machine capacity constraints. To solve this problem, the authors propose an integrated approach based on a multiple-objective grouping genetic algorithm for the preferential routing selection of each part (by solving an associated resource planning problem) and an integrated heuristic for the cell formation problem.  相似文献   

4.
Cell formation is one of the first and most important steps in designing a cellular manufacturing system. It consist of grouping parts with similar design features or processing requirements into part families and associated machines into machine cells. In this study, a bi-objective cell formation problem considering alternative process routings and machine duplication is presented. Manufacturing factors such as part demands, processing times and machine capacities are incorporated in the problem. The objectives of the problem include the minimization of the total dissimilarity between the parts and the minimization of the total investment needed for the acquisition of machines. A normalized weighted sum method is applied to unify the objective functions. Due to the computational complexity of the problem, a hybrid method combining genetic algorithm and dynamic programming is developed to solve it. In the proposed method, the dynamic programming is implemented to evaluate the fitness value of chromosomes in the genetic algorithm. Computational experiments are conducted to examine the performance of the hybrid method. The computations showed promising results in terms of both solution quality and computation time.  相似文献   

5.
In most deterministic scheduling problems job processing times are considered as invariable and known in advance. Single machine scheduling problem with controllable processing times with no inserted idle time is presented in this study. Job processing times are controllable to some extent that they can be reduced or increased, up to a certain limit, at a cost proportional to the reduction or increase. In this study, our objective is determining a set of compression/expansion of processing times in addition to a sequence of jobs simultaneously, so that total tardiness and earliness are minimized. A mathematical model is proposed firstly and afterward a net benefit compression–net benefit expansion (NBC–NBE) heuristic is presented so as to acquire a set of amounts of compression and expansion of jobs processing times in a given sequence. Three heuristic techniques in small problems and in medium-to-large instances two meta-heuristic approaches, as effective local search methods, as well as these heuristics are employed to solve test examples. The single machine total tardiness problem (SMTTP) is already NP-hard, so the considered problem is NP-hard obviously. The computational experiments demonstrate that our proposed heuristic is efficient approach for such just-in-time (JIT) problem, especially equipped with competent heuristics.  相似文献   

6.
This research presents, implements and tests a two-stage procedure for cost effective part family and machine cell formation. First, the problem is formulated as a mixed integer mathematical model for simultaneous machine grouping and part family assignment. This model, which we refer to as the single-stage model, considers the cost trade-offs of cell configuration, machine procurement and salvage, subcontracting, inter-cell movement, and capital investment, all of which reflect the significance of real life planning aspects. To alleviate the computational burden of this single-stage model, we decompose it into two stages: the first stage is a heuristic for machine cell and part family formations; the second stage integrates the heuristic method with a mathematical program to optimize the various cost aspects. The efficacy of the proposed models is shown through a number of example problems. The results show that the two-stage procedure is powerful in the planing stages of large-size problems where the cost aspects are crucial.  相似文献   

7.
One major problem in cellular manufacturing is the grouping of component parts with similar processing requirements into part families, and machines into manufacturing cells to facilitate the manufacturing of specific part families assigned to them. The objective is to minimize the total inter-cell and intra-cell movements of parts during the manufacturing process. In this paper, a mathematical model is presented to describe the characteristics of such a problem. An approach based on the concept of genetic algorithms is developed to determine the optimal machine-component groupings. Illustrative examples are used to demonstrate the efficiency of the proposed approach. Indeed, the results obtained show that the proposed genetic approach is a simple and efficient means for solving the machine-component grouping problem.  相似文献   

8.
Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity. The user’s access over the internet creates massive data processing over the internet. Big data require an intelligent feature selection model by addressing huge varieties of data. Traditional feature selection techniques are only applicable to simple data mining. Intelligent techniques are needed in big data processing and machine learning for an efficient classification. Major feature selection algorithms read the input features as they are. Then, the features are preprocessed and classified. Here, an algorithm does not consider the relatedness. During feature selection, all features are misread as outputs. Accordingly, a less optimal solution is achieved. In our proposed research, we focus on the feature selection by using supervised learning techniques called grey wolf optimization (GWO) with decomposed random differential grouping (DrnDG-GWO). First, decomposition of features into subsets based on relatedness in variables is performed. Random differential grouping is performed using a fitness value of two variables. Now, every subset is regarded as a population in GWO techniques. The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research. Once the features are optimized, we classify using advanced kNN process for accurate data classification. The result of DrnDG-GWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm. The accuracy and time complexity of the proposed algorithm are 98% and 5 s, which are better than the existing techniques.  相似文献   

9.
王旭  赵曙光 《计算机应用》2014,34(1):179-181
针对高维优化问题难以解决并且优化耗费时间长的问题,提出了一种解决高维优化问题的差分进化算法。将协同进化思想引入到差分进化领域,采用一种由状态观测器和随机分组策略组成的协同进化方案。其中,状态观测器根据搜索状态反馈信息适时地调用随机分组策略重新分组;随机分组策略将高维优化问题分解为若干较低维的子问题,而后分别进化。该方案有效地增强了算法解决高维优化问题的搜索速度和搜索能力。经典型的实例测试,并与其他一流差分进化算法比较,实验结果表明:所提算法能有效地求解不同类型的高维优化问题,在搜索速度方面有明显提升,尤其对可分解的高维优化问题极具竞争力。  相似文献   

10.
One fundamental problem in cellular manufacturing is the formation of product families and machine cells. Many solution methods have been developed for the cell formation problem. Since efficient grouping is the prerequisite of a successful Cellular Manufacturing installation the research in this area will likely be continued. In this paper, we consider the problem of cell formation in cellular manufacturing systems with the objective of maximizing the grouping efficacy. We propose a Genetic Algorithm (GA) to obtain machine-cells and part-families. Developed GA has three different selection and crossover operators. The proper operators and parameters of the GA were determined by design of experiments. A set of 15 test problems with various sizes drawn from the literature is used to test the performance of the proposed algorithm. The corresponding results are compared to several well-known algorithms published. The comparative study shows that the proposed GA improves the grouping efficacy for 40% of the test problems.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号