The Convergence in Workers’ Skill Levels Under Learning and Forgetting: The Fixed-Point-Property Approach

Sunantha Teyarachakul, ESSEC Business School
Dogan Comez, North Dakota State University
Hakan Tarakci, Melbourne Business School

Abstract

This paper presents a study on the convergence of workers’ skill levels under learning and forgetting in processing time in a batch-manufacturing environment. The convergence properties are examined under assumptions of an infinite horizon, a constant demand rate, and a fixed lot size. Our work extends the convergence results of Teyarachakul, Chand, and Ward (2008) beyond Globerson and Levin’s (1987) exponential forgetting function and Wright’s (1936) learning curve to more general classes of learning and forgetting functions. We also discuss why early papers other than Teyarachakul, Chand, and Ward (2008) did not find other types of long-term behaviors beyond convergence to a fixed point, and illustrate how our model could be modified and applied to other circumstances.