Grouping efficiency measures and their impact on factory measures for the machine-part cell formation problem: A simulation study |
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| Affiliation: | 1. Department of Business Information Technology, Pamplin College of Business, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA;2. Department of Engineering, East Carolina University, Greenville, NC 27858;1. Department of Business Information Technology, Virginia Tech, Blacksburg, VA 24061-0235, USA;2. Department of Management, Virginia Tech, Blacksburg, VA 24061-0235, USA;3. Department of Business Information & Analytics, University of Denver, Denver, CO 80208-8931, USA;1. Department of Electrical and Electronic Engineering, Ubon Ratchathani University, Warin Chamrap, Ubon Ratchathani 34190, Thailand;2. Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA;1. Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry, Chongqing University, Chongqing 400030, PR China;2. Physics and Electronic Engineering Department, Yangtze Normal University, Chongqing 408003, PR China;1. Department of Aerospace Engineering, University of Maryland, College Park, MD 20742, USA;2. Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA;3. Department of Aerospace Engineering and the Institute for Systems Research, University of Maryland, College Park, MD 20742, USA;1. Department of Paediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA;2. Department of Pharmacology/Toxicology, University of Utah College of Pharmacy, Salt Lake City, UT, USA |
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| Abstract: | Over the past 25 years, the machine-part cell formation problem has been the subject of numerous studies. Researchers have applied various methodologies to the problem in an effort to determine optimal clusterings of machines and optimal groupings of parts into families. The quality of these machine and part groupings have been evaluated using various objective functions, including grouping efficacy, grouping index, grouping capability index, and doubly weighted grouping efficiency, among others. In this study, we investigate how appropriate these grouping quality measures are in determining cell formations that optimize factory performance. Through the application of a grouping genetic algorithm, we determine machine/part cell formations for several problems from the literature. These cell formations are then simulated to determine their impact on various factory measures, such as flow time, wait time, throughput, and machine utilization, among others. Results indicate that it is not always the case that a “more efficient” machine/part cell formation leads to significant changes or improvements in factory measures over a “less efficient” cell formation. In other words, although researchers are working to optimize cell formations using efficiency measures, cells formed this way do not always demonstrate optimized factory measures. |
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