A General Variable Neighborhood Search approach based on a p-median model for cellular manufacturing problems
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A General Variable Neighborhood Search approach based on a p-median model for cellular manufacturing problems Saber Ibrahim1
· Bassem Jarboui2
Received: 27 February 2020 / Accepted: 30 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract One of the practical application in cellular manufacturing systems is the cell formation problem (CFP). Its main idea is to group machines into cells and parts into part families in a way that the number of exceptional elements and the number of voids are minimized. In literature, it is proved that p-median is an efficient mathematical programming model for solving CF problems. In the present work, we develop a modified p-median based model dedicated to solve CFP respecting the objective of minimizing the sum of dissimilarities of machines. For this aim, we applied a General Variable Neighborhood Search algorithm and we collaborated it with an Estimation of Distribution Algorithm maximizing the group capability index and the grouping efficacy evaluation criteria. Thirty CF problems are taken from the literature and tested by our proposed algorithm and the experimental study demonstrated that the proposed method guided by p-median model provides high quality cells in speed running times and beats other state-of-the-art algorithms particularly for CF instances with large sizes. Keywords Cell formation problem · p-Median model · General Variable Neighborhood Search · Estimation of Distribution Algorithm · Grouping efficacy · Group capability index
1 Introduction In production environment, group technology (GT) is known as a manufacturing philosophy that utilizes similarities in product processes and design. One of the main applications of this concept is cellular manufacturing (CM) in which parts are
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Saber Ibrahim [email protected]
1
Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, P.O. Box: 6640, Buraydah 51452, Saudi Arabia
2
Department of Industrial Management, Higher Colleges of Technology, Abu Dhabi, UAE
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S. Ibrahim, B. Jarboui
grouped into part families according to their similarities and machines are clustered into machine groups according to their dissimilarities which leads to the process of one or more part families within a single cell of machines. As stated in [26] and [60], this application has several advantages which can be resumed into finding better quality and production control, growth in flexibility and reduction of setup time, throughput time, material handling costs and work-in-process inventories. The problem of finding, optimally, machine groups and part families is referred to the part cell formation problem (PCFP). Generally, the input of the CFP is a binary part-machine incidence matrix (PMIM) where rows and columns have to be rearranged to produce part families and their corresponding cells. During the last 30 years, researchers proved that the best way to optimize this problem is to diagonalize the PMIM and the ideal cell conf
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