Annals of Operations Research, 17 (1989) 291-304 291 THE INCORPORATION OF LEARNING IN PRODUCTION PLANNING MODELS Dennis E. KROLL Dept. of Industrial Engineering. Bradley University, Peoria, IL 61625. U.S.A. and K. Ravi KUMAR Dept. of Decision Systems, University of Southern California, Los Angeles. CA 90089-1421, U.S.A. Abstract Production managers employ numerous aggregate planning models to smooth work loads and minimize labor and inventory costs. Some of the more recently developed models incorporate the learning that occurs during repetitive work. This article discusses the history of both aggregate planning and learning models, the various combined models, and their appropriateness to a given environment. 1. Introduction As the modern manufacturing plant evolved early in this century, managers noted that smooth work load on an aggregate basis eased their scheduling task and, in general, minimized labor and inventory costs. Similarly, as long-cycle repetitive tasks arose in these plants (especially in the aircraft industries) managers noted that cycle times shortened as a task was repeated, i.e., learning took place. These two areas of aggregate planning and learning curves developed separately until 1968 when the first of a number of integrated models brought the concepts together. In this paper we initially trace the historical development of aggregate planning and learning theory as they evolved independently. In the next section we review research models on aggregate planning incorporating the effects of learning. We critique the individual models (aggregate planning models, learning models, as well as composite models) in the course of the review with regard to appropriate- ness, applicability, and complexity of implementation. We present our conclu- sions and guidance, in the form of essential modeling features in future models, for researchers in this area in the last section. © J.C. Baltzer A.G. Scientific Publishing Company