A fuzzy lot-sizing problem with two-stage composite human learning. Issue 16 (17th August 2016)

• Record Type: Journal Article
• Authors: Kazemi, Nima; Abdul-Rashid, Salwa Hanim; Shekarian, Ehsan; Bottani, Eleonora; Montanari, Roberto
• Is Part Of: International journal of production research. Volume 54:Issue 16(2016)
• Page Start: 5010
• Page End: 5025
• Publication Date: 2016-08-17
• Subjects: fuzzy EOQ model -- backorders -- lot-sizing -- human learning -- cognitive/motor capabilities; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2016.1165874
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : Due to the repetitive nature of inventory planning over the planning horizon, the operator in charge has to perform planning tasks repetitively, and consequently s/he becomes more familiar with the tasks over time. Familiarity with the tasks suggests that learning takes place in inventory planning. Even though the operator's learning over time might improve his/her efficiency, prior research on fuzzy lot-sizing problems mostly overlooked the effect of human learning in their models and its impact on the operator's performance. To close the research gap in this area, this paper models the operator's learning in a fuzzy economic order quantity model with backorders. The paper models a situation where the operator applies the acquired knowledge over the cycles in setting the fuzzy parameters at the beginning of every planning cycle, where his/her learning ability includes the cognitive and motor capabilities of a human being. Subsequently, a mathematical model which takes account of a two-stage human learning over the planning cycles is developed, which is then analytically investigated using sample data-sets. The results indicate that both operator's capabilities, cognitive and motor, affect the efficiency of the fuzzy lot-sizing inventory model, but the influence of the cognitive capability is more profound, which in turn suggests the importance of training programmes for the workforces. The results of the sensitivity analysis also draw some managerial insights for the case that some model parameters vary over the planning horizon.