A parameter-tuned genetic algorithm to optimize two-echelon continuous review inventory systems

► A one-warehouse m-retailers supply chain with non-repairable items is considered. ► A mathematical model to minimize the total annual inventory subject to constraints on the average annual order frequency, expected number of backorders, and budget is developed. ► A parameter-tuned GA is proposed t...

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Bibliographic Details
Published in:Expert systems with applications Vol. 38; no. 9; pp. 11708 - 11714
Main Authors: Pasandideh, Seyed Hamid Reza, Niaki, Seyed Taghi Akhavan, Tokhmehchi, Nafiseh
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.09.2011
Subjects:
Budgeting
Continuous review policy
Expert systems
Genetic algorithm
Genetic algorithms
Inventories
Mathematical models
Meta-heuristic algorithms
Multi-echelon inventory
Nonlinear-integer programming
Policies
Stockings
Stockpiling
Meta-heuristic algorithms
Nonlinear-integer programming
Genetic algorithm
Multi-echelon inventory
Continuous review policy
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:► A one-warehouse m-retailers supply chain with non-repairable items is considered. ► A mathematical model to minimize the total annual inventory subject to constraints on the average annual order frequency, expected number of backorders, and budget is developed. ► A parameter-tuned GA is proposed to solve the model. ► A numerical example is given to demonstrate the proposed methodology. This paper deals with a two-echelon inventory system for a non-repairable item where the system consists of one warehouse and m identical retailers and uses continuous-review ( R, Q) ordering policy. To find an effective stocking policy for this system, a mathematical model with the objective of minimizing the total annual inventory investment subject to constraints on the average annual order frequency, expected number of backorders, and budget is formulated. The mathematical model of the problem at hand is shown to be nonlinear integer-programming and hence a parameter-tuned genetic algorithm is proposed to solve it efficiently. A numerical example is provided at the end to illustrate the applicability of the proposed methodology.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2011.03.056