A fuzzy EOQ model with backorders and forgetting effect on fuzzy parameters: An empirical study. (June 2016)

• Record Type: Journal Article
• Authors: Kazemi, Nima; Olugu, Ezutah Udoncy; Abdul-Rashid, Salwa Hanim; Ghazilla, Raja Ariffin Raja
• Is Part Of: Computers & industrial engineering. Volume 96(2016)
• Page Start: 140
• Page End: 148
• Publication Date: 2016-06
• Subjects: Fuzzy EOQ -- Backorders -- Semi-structured interview -- Learning -- Forgetting -- Knowledge depreciation; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.03.004
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: This paper considers a fuzzy lot-sizing problem with forgetting effect. A new fuzzy EOQ model with backorders and forgetting is developed. The result suggests decreasing the maximum inventory and the total inventory cost. Abstract: The study of learning effect on inventory models with imprecise parameters is a research topic that has recently emerged. The research papers have published so far studied this aspect from a theoretical point of view and thus the literature lacks the investigation of this topic from a practical standpoint. To close this research gap, we conducted a semi-structured interview with a number of industry experts to gain insights into the prevalence of learning and forgetting in real applications. Based on the insights gained from the interviews, we have developed a recently published model by countering the assumption of full transfer of learning. The model developed herein proposes a situation where the knowledge gained by the operator in setting imprecise parameters deteriorates over the planning cycles due to intermittent planning process. A numerical study suggests that accounting for the effect of knowledge depreciation/forgetting on imprecise parameters leads to reduction in maximum inventory, which consequently reduces the total cost of the system.