Economic manufacturing quantities under conditions of learning and forgetting (EMQ/LaF)

Abstract

Abstract. It has been empirically observed that productivity improves as production continues due to system 'learning’, but that it deteriorates once the activity is stopped due to system 'forgetting’. Both learning and forgetting follow an exponential form with a 'doubling factor’ ranging between 0.75 and 0.98. We review and critique two previously proposed models, correct some minor errors in them, and expand one of them to accommodate a finite horizon. We also propose a new model that is more in harmony with the established learning function, for the determination of the optimal number and size of the lots in the finite and infinite horizon. The methodology used throughout is dynamic programming. We investigate the impact of all three models on the optimal lot sires and their costs, and establish the functional relations between the total cost and the various factors affecting them.

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Notes on contributors

SALAH E. ELM'AGHRABY

Salah E, Elmaghraby is University Professor of Operations Research and Industrial Engineering at North Carolina State University. His teaching and research interests focus on the areas of production control, activity networks, and discrete and dynamic programming. He has been principal investigator of research grants from the National Science Foundation, the Office of Naval Research, The Army Research Office, N.C. Department of Correction, NASA, and the Academy of Applied Sciences. Dr Elmaghraby is the author of three books and author or co-author of over 65 technical papers published in professional journals. He was a department editor of the HE Transactions, a member of the ORSA lectureship series and an associate editor for Management Science. Dr Elmaghraby has been consulting for major companies in USA, Italy and Kuwait. He has served as a senator at his university and received numerous honours and awards.