Group Scheduling with General Autonomous and Induced Learning Effect

Abstract

In recent research, induced learning requires management involvement. The initial investment can be interpreted as induced learning via an investment in the creation of technological knowledge or knowledge transfer. Induced learning has received attention in productivity improvement. In this paper, we consider scheduling problems with autonomous and induced learning under group technology. The jobs' processing times decrease according to a learning rate which conversely increases the investment cost. The objectives of the scheduling problems are to minimize a linear combination of the makespan and the investment cost and a linear combination of the total completion time and the investment cost. We present polynomial algorithms to determine the optimal job sequence, group sequence, and the percentage reduction of the standard learning rate.