دانلود مقاله ISI انگلیسی شماره 62760
ترجمه فارسی عنوان مقاله

حل یک مدل طراحی چند طبقه ای یک سیستم تولید دینامیکی سلولی با یک الگوریتم ژنتیک کارآمد

عنوان انگلیسی
Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
62760 2014 15 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Manufacturing Systems, Volume 33, Issue 1, January 2014, Pages 218–232

ترجمه کلمات کلیدی
سیستم تولید داینامیک سلولی، طرح چند طبقه، برنامه ریزی عدد صحیح مختلط، الگوریتم ژنتیک
کلمات کلیدی انگلیسی
Dynamic cellular manufacturing systems; Multi-floor layout; Mixed-integer programming; Genetic algorithm

چکیده انگلیسی

This paper presents a mixed-integer programming model for a multi-floor layout design of cellular manufacturing systems (CMSs) in a dynamic environment. A novel aspect of this model is to concurrently determine the cell formation (CF) and group layout (GL) as the interrelated decisions involved in the design of a CMS in order to achieve an optimal (or near-optimal) design solution for a multi-floor factory in a multi-period planning horizon. Other design aspects are to design a multi-floor layout to form cells in different floors, a multi-rows layout of equal area facilities in each cell, flexible reconfigurations of cells during successive periods, distance-based material handling cost, and machine depot keeping idle machines. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. The objective is to minimize the total costs of intra-cell, inter-cell, and inter-floor material handling, purchasing machines, machine processing, machine overhead, and machine relocation. Two numerical examples are solved by the CPLEX software to verify the performance of the presented model and illustrate the model features. Since this model belongs to NP-hard class, an efficient genetic algorithm (GA) with a matrix-based chromosome structure is proposed to derive near-optimal solutions. To verify its computational efficiency in comparison to the CPLEX software, several test problems with different sizes and settings are implemented. The efficiency of the proposed GA in terms of the objective function value and computational time is proved by the obtained results.