Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Heydarian, Delaram; Jolai, Fariborz
• Is Part Of: Production & manufacturing research. Volume 6:Issue 1(2018)
• Page Start: 396
• Page End: 415
• Publication Date: 2018-01-01
• Subjects: Operator allocation -- Cellular Manufacturing System (CMS) -- learning effect -- server breakdown -- Fuzzy Simulation -- Response Surface Methodology (RSM); Manufacturing processes -- Periodicals; Production engineering -- Research -- Periodicals; Manufacturing processes; Production engineering -- Research; Periodicals; 670
• DOI: 10.1080/21693277.2018.1531080
• Languages: English
• ISSNs: 2169-3277

Abstract

ABSTRACT: This paper investigates the operator allocation problem with learning effects and server breakdown in cellular manufacturing systems (CMSs) using fuzzy computer simulation and response surface methodology (RSM). The primary contribution of this study is incorporating combined server breakdowns and learning effects in CMS under uncertainty. Machine breakdowns of all the machines as well as the probability related to each entity should be delivered in good order are considered. Also, previous studies did not consider fuzzy simulation and RSM to deal with environmental and data uncertainty in operator allocation problems. The superiority of the presented model, in comparison with the traditional one, is shown according to the number of required iterations. The proposed simulation model is run in uncertain state to obtain the total processing time. RSM algorithm identifies a fitted function in terms of the value of allocated capital to each server and total processing time. This is a practical approach for decision-makers of all Cellular Manufacturing Systems.