Machine scheduling problems with a general learning effect

Abstract

Recently, learning effects in scheduling problems have received growing attention. The position-based learning model seems to be a realistic assumption for the case where the actual processing of the job is mainly machine driven. In this paper, we consider the sum-of-processing-time-based learning model. We propose a learning model which considers both the machine and human learning effects, simultaneously. We first show that the position-based learning and the sum-of-processing-time-based learning models in the literature are special cases of the proposed model. Moreover, we present the solution procedures for some single-machine and some flowshop problems.