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Journals / An International Journal of Optimization and Control: Theories & Applications (IJOCTA) / 2013 / Cilt: 3 - Sayı: 1

Generalized transformation techniques for multi-choice linear programming problems

Srikumar ACHARYA
Mitali Madhumita ACHARYA
Pages
45–54
Publication Date
2022-10-11
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Finally, anumerical example is presented to illustrate the transformation technique and the solution procedure.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The multi-choice programming allows the decision maker to consider multiple number ofresources for each constraint or goal.
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Key Findings & Data:Recently, Biswal and Acharya [2] have proposed a methodology to transform the multi-choicelinear programming problem to an equivalent mathematical programming model, which can accommodate a maximum of eight goals on the right hand side of any constraint.
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Conclusion & Impact:Finally, anumerical example is presented to illustrate the transformation technique and the solution procedure.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The multi-choice programming allows the decision maker to consider multiple number ofresources for each constraint or goal. Multi-choice linear programming problem can not be solveddirectly using the traditional linear programming technique. However, to deal with the multi-choiceparameters, multiplicative terms of binary variables may be used in the transformed mathematicalmodel. Recently, Biswal and Acharya [2] have proposed a methodology to transform the multi-choicelinear programming problem to an equivalent mathematical programming model, which can accommodate a maximum of eight goals on the right hand side of any constraint. In this paper we present twomodels as generalized transformation the multi-choice linear programming problem. Using any one ofthe transformation techniques a decision maker can handle a parameter with finite number of choices.Binary variables are introduced to formulate a non-linear mixed integer programming model. Using anon-linear programming software optimal solution of the proposed model can be obtained. Finally, anumerical example is presented to illustrate the transformation technique and the solution procedure.

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Cite This Article

ACHARYA, S., ACHARYA, M. M. (2022). Generalized transformation techniques for multi-choice linear programming problems. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 3 - Sayı: 1, 45–54. https://ajindex.com/articles/440728

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References (1)

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