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Dergiler / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2022 / Cilt: 12 - Sayı: 3

New Solution Approaches for Multi-Objective Solid Transportation Problem Using Some Aggregation Operators

Nurdan KARA
Hale GONCE KÖÇKEN
Sayfa
1776–1789
Yayın Tarihi
2022-11-06
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Furthermore, graphical comparisons and sensitivity analysis are presented with the solution obtained.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:A solid transportation problem emerges when the decision variables are represented by three items: the source, the destination, and the mode of transport.
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Temel Bulgular & Veri:In applications, the STP generally requires considering multiple objectives such as cost minimization, time minimization, security level maximization, etc.
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Sonuç & Katkı:Furthermore, graphical comparisons and sensitivity analysis are presented with the solution obtained.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Multi-objective optimization · fuzzy aggregation operator · solid transportation problem.

Abstract

A solid transportation problem emerges when the decision variables are represented by three items: the source, the destination, and the mode of transport. In applications, the STP generally requires considering multiple objectives such as cost minimization, time minimization, security level maximization, etc. In this way, a multi-objective solid transportation problem arises. This paper deals with the solution of the problem and analyzes the effect of several important fuzzy aggregation operators on the solution of the problem. In this context, the most commonly used aggregation operators are investigated for this problem. To explain the solution approach, a numerical example from the literature is given and a Pareto-optimal solution set is provided to offer the decision-maker. Furthermore, graphical comparisons and sensitivity analysis are presented with the solution obtained.

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Bu Makaleye Atıf Yap

KARA, N., KÖÇKEN, H. G. (2022). New Solution Approaches for Multi-Objective Solid Transportation Problem Using Some Aggregation Operators. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 12 - Sayı: 3, 1776–1789. https://ajindex.com/articles/651465

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Kaynakça (13)

  1. Ahlatçıoğlu M, Sivri M, 1988. A Solution Proposal for Solving Three-Dimension Transportation Problem. İ.T.Ü, Istanbul-Turkey, 22: 9-12.
  2. Anuradha D, Jayalakshmi M, Deepa G, Sujatha V, 2019. Solution of Multiobjective Solid Transportation Problem in Fuzzy Approach. AIP Conference Proceedings, No.1, P020009-1- 020009-5.5. Arslan O, Archetti C, Jabali O, Laporte G, Speranza MG, 2020. Minimum cost network design in strategic alliances. Omega, Vol. 96, 102079.
  3. Bit AK, Biswal MP, Alam SS, 1993. Fuzzy programming approach to multiobjective solid transportation problem. Fuzzy Sets and Systems, 57:183-194.
  4. Bodkhe, SG, Bajaj VH, Dhaigude DB, 2010. Fuzzy programming technique to solve multi-objective solid transportation problem with some non-linear membership functions. Advances in Computational Research, 2(1):15-20.
  5. Chen Lin, Jin P, Zhang B, 2017. Uncertain goal programming models for bicriteria solid transportation problem. Applied Soft Computing, 51: 49–59.
  6. Chen B, Liu Y, Zhou T, 2017. An entropy based solid transportation problem in uncertain environment”. Journal of Ambient Intelligence and Humanized Computing, 10(1): 357 –363. Cui Q, Sheng Y, 2012. Uncertain programming model for solid transportation problem. Information, 15: 342–348.
  7. Dalman H, Sivri M, 2017. Multi-objective Solid Transportation Problem in Uncertain Environment. Iranian Journal of Science and Technology, Transactions A: Science, 41:505-514.
  8. Guu SM, Wu Y, 1997. Weighted coefficients in two-phase approach for solving the multiple objective programming problems. Fuzzy Sets and Systems, 85: 45-48.
  9. Hu Y, Zhao X, Liu J, Liang B, Ma C, 2020. An efficient algorithm for solving minimum cost flow problem with complementarity slack conditions. Mathematical Problems in Engineering, 2020:1-5.
  10. Kaur, D, Mukherjee S, Basu K, 2015. Solution of a multi-objective and multi-index real-life transportation problem using different fuzzy membership functions. Journal of Optimization Theory and Applications, 164:666-678.
  11. Khan IU, Rafique F, 2021. Minimum-cost capacitated fuzzy network, fuzzy linear programming formulation, and perspective data analytics to minimize the operations cost of American airlines. Soft Computing, 25(2):1411-1429.
  12. Khurana A, Adlakha V, 2015. On multi-index fixed charge bi-criterion transportation problem. Opsearch, 52(4):733-745.
  13. Khurana A, Adlakha V, Lev B, 2018. Multi-index constrained transportation problem with bounds on availabilities, requirements and commodities. Operations Research Pers