نتایج جستجو برای: dynamic cell formation problem
تعداد نتایج: 3232885 فیلتر نتایج به سال:
This paper presents a new nonlinear mathematical model to solve a cell formation problem which assumes that processing time and inter-arrival time of parts are random variables. In this research, cells are defined as a queue system which will be optimized via queuing theory. In this queue system, each machine is assumed as a server and each part as a customer. The grouping of machines and parts...
cellular manufacturing system (cms) is highly important in modern manufacturing methods. given the ever increasing market competition in terms of time and cost of manufacturing, we need models to decrease the cost and time of manufacturing. in this study, cms is considered in condition of dynamic demand in each period. the model is developed for facing dynamic demand that increases the cost of ...
in this study, the stochastic cell formation problem with developing model within queuing theory with stochastic demand, processing time and reliability has been presented. machine as server and part as customer are assumed where servers should service to customers. since, the cell formation problem is np-hard, therefore, deterministic methods need a long time to solve this model. in this study...
this paper presents a new nonlinear mathematical model to solve a cell formation problem which assumes that processing time and inter-arrival time of parts are random variables. in this research, cells are defined as a queue system which will be optimized via queuing theory. in this queue system, each machine is assumed as a server and each part as a customer. the grouping of machines and parts...
Cellular manufacturing system, an application of group technology, has been considered as an effective method to obtain productivity in a factory. For design of manufacturing cells, several mathematical models and various algorithms have been proposed in literature. In the present research, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing cell ...
In this paper, solving a cell formation (CF) problem in dynamic condition is going to be discussed by using some traditional metaheuristic methods such as genetic algorithm (GA), simulated annealing (SA) and tabu search (TS). Most of previous researches were done under the static condition. Due to the fact that CF is a NP-hard problem, then solving the model using classical optimization methods...
In spite of many researches in literature investigating dynamic of cell formation (CF) problem, further research needs to be elaborated to assay hidden aspects of cellular manufacturing system (CMS), due to inherent complexity and uncertainty on optimizing this problem. In this paper, sensitivity analysis of modified self-adaptive differential evolution (MSDE) algorithm is proposed for basic pa...
This paper deals with the cellular manufacturing system (CMS) that is based on group technology concepts. CMS is defined as identifying the similar parts that are processed on the same machines and then grouping them as a cell. The most proposed models for solving CMS are focused on cell formation problem while machine layout is considered in few papers. This paper addresses a mathematical mo...
This paper presents the idea of creating cells while reducing part-level sequence-dependent setup time in general cellular manufacturing systems (CMS). Setup time reduction in CMS has gained modest attention in the literature. This could be attributed to the fact that the fundamental problem in cell formation in CMS has been mainly related to material handling and machine utilization while setu...
Cell formation problem in Cellular Manufacturing System (CMS) design has derived the attention of researchers for more than three decades. However, use of sequence data for cell formation has been the least investigated area. Sequence data provides valuable information about the flow patterns of various jobs in a manufacturing system. This paper presents a new mathematical model to solve a cell...
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