Generalized utilization-based similarity coefficient for machine-part grouping problem in cellular manufacturing

• Autorzy: Ghosh Tamal

Identyfikatory

DOI

10.24425/mper.2019.131449

• Języki publikacji: EN

Abstrakty

EN

This article intends to justify the gap in the research of similarity coefficient driven approaches and cell formation problems (CFP) based on ratio data in cellular manufacturing systems (CMS). The actual implication of ratio data was vaguely addressed in past literature, which has been corrected recently. This research considered that newly projected CFP based on ration data. This study further revealed the lack of interest of researchers in investigation for an appropriate and improved similarity coefficient primarily for CFP based on ratio data. For that matter a novel similarity coefficient named as Generalized Utilization-based Similarity Coefficient (GUSC) is introduced, which scientifically handles ratio data. Thereafter a two-stage cell formation technique is adopted. First, the proposed GUSC based method is employed to obtained efficient machine cells. Second, a novel part allocating heuristic is proposed to obtain effective part families. This proposed approach is successfully verified on the test problems and compared with algorithms based on another similarity coefficient and a recent metaheuristic. The proposed method is shown to obtain 66.67% improved solutions.

Słowa kluczowe

EN

cellular manufacturing; machine utilization percentage; ratio data; similarity coefficient

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2019
• Tom: Vol. 10, No. 4
• Strony: 90--100
• Opis fizyczny: Bibliogr. 47 poz., rys., tab.

Twórcy

autor

Ghosh Tamal

• Norwegian University of Science and Technology, Department of Manufacturing and Civil Engineering, Teknologivegen 22, 2815 Gjøvik, Norway

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).