A PROBABILISTIC MULTI OBJECTIVE CLSC MODEL WITH GENETIC ALGORITHM ε-CONSTRAINT APPROACH

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TAHERIMOGHADAM ALIREZA; SAHRAEIAN RASHED

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: PRODUCTION AND OPERATIONS MANAGEMENT Year:2014 | Volume:5 | Issue:1 (8) Page(s): 21-37

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Information Journal Paper

Title

A PROBABILISTIC MULTI OBJECTIVE CLSC MODEL WITH GENETIC ALGORITHM ε-CONSTRAINT APPROACH

Author(s)

TAHERIMOGHADAM ALIREZA | SAHRAEIAN RASHED | Issue Writer Certificate

Keywords

CLOSEDQ2

LOOP SUPPLY CHAINQ2

GENETIC ALGORITHMQ2

EPSILON CONSTRAINTQ2

PRODUCTION PLANNINGQ2

UNCERTAIN DEMANDQ2

UNCERTAIN RETURNQ2

Abstract

In this paper an uncertain multi objective CLOSED-loop supply chain is developed. The first objective function is maximizing the total profit. The second objective function is minimizing the use of row materials. In the other word, the second objective function is maximizing the amount of remanufacturing and recycling. GENETIC ALGORITHM is used for optimization; and for finding the pareto optimal line, Epsilon-constraint method is used. Finally a numerical example is solved with proposed approach and performance of the model is evaluated in different sizes. The results show that this approach is effective and useful for managerial decisions.

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APA: Copy

TAHERIMOGHADAM, ALIREZA, & SAHRAEIAN, RASHED. (2014). A PROBABILISTIC MULTI OBJECTIVE CLSC MODEL WITH GENETIC ALGORITHM ε-CONSTRAINT APPROACH. PRODUCTION AND OPERATIONS MANAGEMENT, 5(1 (8)), 21-37. SID. https://sid.ir/paper/217492/en

Vancouver: Copy

TAHERIMOGHADAM ALIREZA, SAHRAEIAN RASHED. A PROBABILISTIC MULTI OBJECTIVE CLSC MODEL WITH GENETIC ALGORITHM ε-CONSTRAINT APPROACH. PRODUCTION AND OPERATIONS MANAGEMENT[Internet]. 2014;5(1 (8)):21-37. Available from: https://sid.ir/paper/217492/en

IEEE: Copy

ALIREZA TAHERIMOGHADAM, and RASHED SAHRAEIAN, “A PROBABILISTIC MULTI OBJECTIVE CLSC MODEL WITH GENETIC ALGORITHM ε-CONSTRAINT APPROACH,” PRODUCTION AND OPERATIONS MANAGEMENT, vol. 5, no. 1 (8), pp. 21–37, 2014, [Online]. Available: https://sid.ir/paper/217492/en

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