A fuzzy EOQ model with backorders and forgetting effect on fuzzy parameters: An empirical study. (June 2016) - Kazemi, Nima; Olugu, Ezutah Udoncy; Abdul-Rashid, Salwa Hanim; Ghazilla, Raja Ariffin Raja
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.