Optimal production inventory decision with learning and fatigue behavioral effects in labor-intensive manufacturing

• Published in: Scientia Iranica. Transaction E, Industrial engineering Vol. 27; no. 2; pp. 918 - 934
• Main Authors: Fu, K, Chen, Zh, Zhang, Y, Wee, H M
• Format: Journal Article
• Language: English
• Published: Tehran Sharif University of Technology 01.03.2020
• Subjects: Behavioral economics; Case studies; Computer simulation; Customer satisfaction; Decision making; Economic conditions; Economic models; Fatigue; Group technology; Human error; Industrial production; Labor; Labor economics; Labor productivity; Learning; Literature reviews; Manufacturing; Mathematical models; Operations management; Production costs; Productivity; Relearning; Sensitivity analysis; Workers
• DOI: 10.24200/sci.2018.50614.1788

Behavioral economics has received much attention recently. Learning and fatigue are two typical behavioral phenomena in industrial production operation processes. The existence of learning and fatigue results in a dynamic change in productivity. In this paper, a classical Economic Production Quantity (EPQ) model is extended to consider the behavioral economic value of learning and fatigue. Based on a real case study, each production cycle was divided into five phases, namely learning phase, stable phase, fatigue phase, fatigue recovery (rest) phase, and relearning phase. The new production inventory decision model was formulated with dynamic productivity and learning-stable-fatiguerecovery effect. Numerical simulation and sensitivity analysis showed that appropriate rest would alleviate employees' fatigue and increase productivity, resulting in a lower average production cost. On the other hand, when the rest time was too long, exceeding a certain value, it led to the decline of the actual labor productivity, resulting in an increase in the average cost of the system.