Journal of Applied Mathematics

Optimization on Production-Inventory Problem with Multistage and Varying Demand

Duan Gang, Chen Li, Li Yin-Zhen, Song Jie-Yan, and Akhtar Tanweer

Full-text: Open access

Abstract

This paper addresses production-inventory problem for the manufacturer by explicitly taking into account multistage and varying demand. A nonlinear hybrid integer constrained optimization is modeled to minimize the total cost including setup cost and holding cost in the planning horizon. A genetic algorithm is developed for the problem. A series of computational experiments with different sizes is used to demonstrate the efficiency and universality of the genetic algorithm in terms of the running time and solution quality. At last the combination of crossover probability and mutation probability is tested for all problems and a law is found for large size.

Article information

Source
J. Appl. Math., Volume 2012 (2012), Article ID 648262, 17 pages.

Dates
First available in Project Euclid: 2 January 2013

Permanent link to this document
https://projecteuclid.org/euclid.jam/1357153582

Digital Object Identifier
doi:10.1155/2012/648262

Mathematical Reviews number (MathSciNet)
MR2997261

Zentralblatt MATH identifier
1264.90003

Citation

Gang, Duan; Li, Chen; Yin-Zhen, Li; Jie-Yan, Song; Tanweer, Akhtar. Optimization on Production-Inventory Problem with Multistage and Varying Demand. J. Appl. Math. 2012 (2012), Article ID 648262, 17 pages. doi:10.1155/2012/648262. https://projecteuclid.org/euclid.jam/1357153582


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