×

Multicriteria inventory classification using a genetic algorithm. (English) Zbl 0957.90003

Summary: One of the application areas of genetic algorithms is parameter optimization. This paper addresses the problem of optimizing a set of parameters that represent the weights of criteria, where the sum of all weights is 1. A chromosome represents the values of the weights, possibly along with some cut-off points. A new crossover operation, called continuous uniform crossover, is proposed, such that it produces valid chromosomes given that the parent chromosomes are valid. The new crossover technique is applied to the problem of multicriteria inventory classification. The results are compared with the classical inventory classification technique using the analytical hierarchy process.

MSC:

90B05 Inventory, storage, reservoirs
90B50 Management decision making, including multiple objectives
90C59 Approximation methods and heuristics in mathematical programming
Full Text: DOI

References:

[1] Balakrishnan, P. V.; Jacob, V. S., Triangulation in Decision Support Systems: algorithms for product design, Decision Support Systems, 14, 313-327 (1995)
[2] Bean, J. C., Genetics and random keys for sequencing and optimization, ORSA Journal on Computing, 6, 2, 154-160 (1994) · Zbl 0807.90060
[3] Davies, M. A., Using the AHP in marketing decision-making, Journal of Marketing Management, 10, 1-3, 57-74 (1994)
[4] Flores, B. E.; Whybark, D. C., Multiple criteria ABC analysis, International Journal of Operations and Production Management, 6, 3, 38-46 (1986)
[5] Flores, B. E.; Olson, D. L.; Dorai, V. K., Management of multicriteria inventory classification, Mathematical and Computer Modeling, 16, 12, 71-82 (1992) · Zbl 0800.90388
[6] Goldberg, D. E.; Samtani, M. P., Engineering optimization via genetic algorithm, (Proceedings of the Ninth Conference on Electronic Computation (1986)), 471-482
[7] Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addison-Wesley: Addison-Wesley Reading, MA · Zbl 0721.68056
[8] Gulsen, M.; Smith, A. E.; Tate, D. M., A genetic algorithm approach to curve fitting, International Journal of Production Research, 33, 7, 1911-1923 (1995) · Zbl 0913.65008
[9] Guvenir, H. A.; Sirin, I., A genetic algorithm for classification by feature partitioning, (Forest, S., Proceedings of the Fifth International Conference on Genetic Algorithms (1993)), 543-548
[10] Guvenir, N., Application of AHP to multicriteria inventory classification, (M.BA. Thesis (1993), Graduate School of Business Administration, Bilkent University: Graduate School of Business Administration, Bilkent University Ankara, Turkey)
[11] Holland, J. H., Adaptation in Natural and Artificial Systems (1975), MIT Press: MIT Press Cambridge, MA · Zbl 0317.68006
[12] Lu, M. H.; Madu, C. N.; Kuei, C. H., Integrating QFD, AHP and benchmarking in strategic marketing, The Journal of Business and Industrial Marketing, 9, 1, 41-50 (1994)
[13] Saaty, T. L., The Analytic Hierarchy Process (1980), McGraw-Hill: McGraw-Hill New York · Zbl 1176.90315
[14] Silver, E. A.; Peterson, R., Decision Systems for Inventory Management and Production Planning (1985), Wiley: Wiley New York
[15] Uckun, S.; Bagchi, S.; Kawamura, K.; Miyabe, Y., Managing genetic search in job shop scheduling, IEEE Expert, 15-24 (October 1995)
[16] Ulengin, B.; Ulengin, F., Forecasting foreign exchange rates: a comparative evaluation of AHP, Omega, 22, 5, 505-519 (1994)
[17] Zahedi, F., The analytical hierarchy process — a survey of the method and its applications, Interfaces, 16, 4, 96-108 (1996)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.