A Generalized Process Targeting Model and an Application Involving a Production Process with Multiple Products

Mohammad A. M. Abdel-Aal and Shokri Z. Selim
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Mohammad A. M. Abdel-Aal: Systems Engineering Department, King Fahd University of Petroleum and Minerals, P.O. Box 5063, Dhahran 31261, Saudi Arabia
Shokri Z. Selim: Systems Engineering Department, King Fahd University of Petroleum and Minerals, P.O. Box 5063, Dhahran 31261, Saudi Arabia

Mathematics, 2019, vol. 7, issue 8, 1-17

Abstract: This paper presents a generalized targeting model that subsumes most known targeting problems. In this paper, a recurrent state is defined as a condition that requires reprocessing or rework. The generalized model can accommodate one or two specifications limits and can be used for the following quality characteristics: The nominal-the-better, the larger-the-better, and the smaller-the-better. This model can be used to find the optimal mean of a quality characteristic, as well as the optimal specification limits. In addition, the paper studies the conditions under which the solution to the proposed model can provide a global solution. The paper shows that, for some of the special cases and under very general conditions, the optimal lower limit should be zero and the optimal upper limit should be infinity. This paper proves that the expected profits improve for the case where only a lower limit on the quality characteristic is used, if a recurrent state is included by adding an optimized upper limit. A special case of the model is used to study the problem of determining a common mean for multiple products, as well as the optimal upper specification limits for each product. A solution procedure for maximizing the expected profits and obtaining the optimal solution is introduced. A numerical example is presented.

Keywords: quality control; targeting model; process mean; strict quasi-concavity; recurrent states; optimized limits (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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