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Minimizing Costs of Transportation Problems Using the Genetic Algorithm

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Proceedings of Sixth International Congress on Information and Communication Technology

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 235))

Abstract

The research aims to minimize the total costs and reach an optimal solution in transporting the gasoline product from the main warehouses in Baghdad Governorate to filling stations: by relying on the dataset obtained from the Oil Products Distribution Company of the Iraqi Ministry of Oil, by using the traditional method (linear programming) and the modern method (genetic algorithm), and then compare them to help decision-makers make the right decision. The preference was achieved for the modern method, which was able to make a slight improvement in the final results. This is because the mathematical model of the problem is a linear model, where the cost in the technique of linear programming (1,424,165) dinars and in the technique of the genetic algorithm (1,424,157) dinars.

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References

  1. Hillier FS (2015) Introduction to operations research, Tata McGraw-Hill Education

    Google Scholar 

  2. Sharma G, Abbas SH, Gupta VK (2015) Solving multi-objective transportation problem to reduce transportation cost and time. J Advc Math 11(1)

    Google Scholar 

  3. Gupta R, Gulati N (2019) Survey of transportation problems. In: International conference on machine learning, big data, cloud and parallel computing (COMITCon). IEEE, pp 417–422

    Google Scholar 

  4. Mohammed NAR, Lahji AA, Syed JK (2013) Transportation of eggs: a case study using multi-objective transportation problem (MOTP). J Inf Oper Manag 4(1):327

    Google Scholar 

  5. Korukoğlu S, Balli S (2011) A improved vogel’s approximatio method for the transportation problem. Math Comput App 16(2):370–381

    Google Scholar 

  6. Joshi RV (2013) Optimization techniques for transportation problems of three variables. IOSR J Math 9(1):46–50

    Google Scholar 

  7. Paşa T (2018) The genetic algorithm for solving the non-linear transportation problem. Rev Air Force Acad 2:37–44

    Google Scholar 

  8. Bharathi K, Vijayalakshmi C (2016) Optimization of multi-objective transportation problem using evolutionary algorithms. Global J Pure App Math 12(2):1387–1396

    Google Scholar 

  9. Talukder S (2011) Mathematicle modelling and applications of particle swarm optimization

    Google Scholar 

  10. Rivera G et al (2020) Genetic algorithm for scheduling optimization considering heterogeneous containers: a real-world case study. Axioms 9(1):27

    Google Scholar 

  11. Bzorg-Haddad O et al (2017) Meta-heuristic and evolutionary algorithms for engineering optimization, Wiley, Hoboken, NJ

    Google Scholar 

  12. Okagbue HI, Peter NJ, Akinola AO, Iroham CO, Opoko AP (2020) Smart review of the application of genetic algorithm inconstruction and housing. Int J Advc Trends Comp Sci Eng

    Google Scholar 

  13. Junbin Z et al (2014) Genetic algorithm particle swarm optimization based hardware evolution strategy. WSEAS Trans circuits Syst 2:274–283

    Google Scholar 

  14. Ashour MAH (2019) Optimum cost of transporting problems with hexagonal fuzzy numbers. J Southwest Jiaotong Univ 54(6). https://doi.org/10.35741/issn.0258-2724.54.6.10

  15. Ashour MAH, Al-Dahhan IAH, Al-Qabily SMA (2019) Solving game theory problems using linear programming and genetic algorithms. In: International conference on human interaction and emerging technologies. Springer, Cham, pp 247–252. https://doi.org/10.1007/978-3-030-25629-6_39

  16. Hassun DS, Jaber A, Ibrahim N (2013) Operation Research, aljazirat library

    Google Scholar 

  17. Hamada, A (2018) Solve the problem of direct transportation in order to find the optimal solution some oil depots in Baghdad are case studies: Al-Muthanna J Adm Econ Sci. https://doi.org/10.18018/MJAES/2018-8/36

  18. Mondal, RN et al (2015) An innovative method for unraveling transportation problems with mixed constraints. Am J Math Stat 5(4):190–195 https://doi.org/10.5923/j.ajms.20150504.04

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Author information

Authors and Affiliations

  1. Statistics Department, College of Administration & Economic, University of Baghdad, Baghdad, Iraq

    Marwan Abdul Hameed Ashour & Alyaa Abdulameer Ahmed

  2. Continuing Education Center/ University of Baghdad, Baghdad, Iraq

    Iman Amer Hameed Al-dahhan

Authors
  1. Marwan Abdul Hameed Ashour
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  2. Alyaa Abdulameer Ahmed
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  3. Iman Amer Hameed Al-dahhan
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Corresponding author

Correspondence to Marwan Abdul Hameed Ashour .

Editor information

Editors and Affiliations

  1. Middlesex University, London, UK

    Xin-She Yang

  2. University of Reading, Reading, UK

    Simon Sherratt

  3. JIS University, Kolkata, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Ashour, M.A.H., Ahmed, A.A., Al-dahhan, I.A.H. (2022). Minimizing Costs of Transportation Problems Using the Genetic Algorithm. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_18

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