Multicriteria order acceptance decision support in over-demanded job shops: A neural network approach

Authors:
J. Wang;J. -Q. Yang;H. Lee
Affiliations:
Department of Industrial Technology, University of North Dakota Grand Forks, ND 58202, U.S.A.;Department of Management, University of North Dakota Grand Forks, ND 58202, U.S.A.;Department of Management and Marketing, Larmar University Beaumont, TX 77710, U.S.A.
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
1994

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Abstract

Order acceptance is an important issue in job shop production systems where demand exceeds capacity. In this paper, a neural network approach is developed for order acceptance decision support in job shops with machine and manpower capacity constraints. First, the order acceptance decision problem is formulated as a sequential multiple criteria decision problem. Then a neural network based preference model for order prioritization is described. The neural network based preference model is trained using preferential data derived from pairwise comparisons of a number of representative orders. An order acceptance decision rule based on the preference model is proposed. Finally, a numerical example is discussed to illustrate the use of the proposed neural network approach. The proposed neural network approach is shown to be a viable method for multicriteria order acceptance decision support in over-demanded job shops.