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CLC number: TP301.6

On-line Access: 2012-08-02

Received: 2011-12-28

Revision Accepted: 2012-06-13

Crosschecked: 2012-07-06

Cited: 3

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.8 P.601-612


Developing a multi-objective, multi-item inventory model and three algorithms for its solution

Author(s):  Ommolbanin Yousefi, Mirbahadorgholi Aryanezhad, Seyed Jafar Sadjadi, Arash Shahin

Affiliation(s):  Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran; more

Corresponding email(s):   oyousefi@iust.ac.ir

Key Words:  Joint replenishment problem, Multi-objective genetic algorithm, RAND algorithm

Ommolbanin Yousefi, Mirbahadorgholi Aryanezhad, Seyed Jafar Sadjadi, Arash Shahin. Developing a multi-objective, multi-item inventory model and three algorithms for its solution[J]. Journal of Zhejiang University Science C, 2012, 13(8): 601-612.

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%T Developing a multi-objective, multi-item inventory model and three algorithms for its solution
%A Ommolbanin Yousefi
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%A Seyed Jafar Sadjadi
%A Arash Shahin
%J Journal of Zhejiang University SCIENCE C
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100384

T1 - Developing a multi-objective, multi-item inventory model and three algorithms for its solution
A1 - Ommolbanin Yousefi
A1 - Mirbahadorgholi Aryanezhad
A1 - Seyed Jafar Sadjadi
A1 - Arash Shahin
J0 - Journal of Zhejiang University Science C
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1100384

We develop a multi-objective model in a multi-product inventory system. The proposed model is a joint replenishment problem (JRP) that has two objective functions. The first one is minimization of total ordering and inventory holding costs, which is the same objective function as the classic JRP. To increase the applicability of the proposed model, we suppose that transportation cost is independent of time, is not a part of holding cost, and is calculated based on the maximum of stored inventory, as is the case in many real inventory problems. Thus, the second objective function is minimization of total transportation cost. To solve this problem three efficient algorithms are proposed. First, the RAND algorithm, called the best heuristic algorithm for solving the JRP, is modified to be applicable for the proposed problem. A multi-objective genetic algorithm (MOGA) is developed as the second algorithm to solve the problem. Finally, the model is solved by a new algorithm that is a combination of the RAND algorithm and MOGA. The performances of these algorithms are then compared with those of the previous approaches and with each other, and the findings imply their ability in finding Pareto optimal solutions to 3200 randomly produced problems.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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