Application of Genetic Algorithm and Fuzzy Sets to Logistic Decision-Making

Authors

https://doi.org/10.22105/aaa.v1i2.38

Abstract

In many areas, due to situational complexity, conceptual imprecision, or informational imperfection, management accounting is faced with a high degree of uncertainty or ambiguity. Many of these uncertainties spring from emotional and or lingual causes. Until the Fuzzy Set Theory (FST) was introduced, people learned how to model these uncertainties arising from the human mind and the environment. The present study explores the applied potentials of fuzzy sets and Genetic Algorithms (GA) in different areas of management accounting, especially logistic issues. Logistic issues in a dynamic business environment primarily involve allocating specific resources to several corresponding consumption destinations. Each resource supplies certain goods, whereas each destination demands certain quantities. In this type of issue, the goal is to identify the most economical transportation route that meets the demand without violating the supply constraints. This paper suggests using fuzzy sets to supply appropriate information regarding price, demand, and other variables. The suggestions include the calculation method of the shortest route with the least cost prices for the distribution cycle (network). Finally, as a solution for this complex problem, a GA in combination with a well-suited fuzzy function is recommended. 

Keywords:

Transportation problem, The shortest route, Logistics, Cost minimization, Imprecise information, Fuzzy sets, Genetic algorithm

References

  1. [1] Heizer, J., & Render, B. (1996). Production and operations management: strategic and tactical decisions. Prentice Hall.
  2. [2] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
  3. [3] Goldberg, D. E. (1998). Genetic algorithms in search, optimization and machine learning. Addison Wesley.
  4. [4] Biethahn, J., & Nissen, V. (2012). Evolutionary algorithms in management applications. Springer.
  5. [5] Zarif Fard, A. (1998). Application of fuzzy set theory in accounting. Journal of accounting and auditing research, 6(1), 70-100. (In Persian). https://acctgrev.ut.ac.ir/article_14403.html?lang=fa
  6. [6] Cooley, J. W., & Hicks Jr, J. O. (1983). A fuzzy set approach to aggregating internal control judgments. Management science, 29(3), 317–334. https://doi.org/10.1287/mnsc.29.3.317
  7. [7] Lin, W. T. (1984). A bayesian analysis of audit tests with fuzzy sets. TIMS/studies in management science, 20, 495–510. https://www.academia.edu/download/78108000/A_Bayesian_Analysis_of_Audit_Tests_with_20220105-25420-1a57h3u.pdf
  8. [8] Bailey Jr, A. D., & Jensen, D. L. (1979). A bayesian auditing model. Accounting & finance, 19(1), 1–19. https://doi.org/10.1111/j.1467-629X.1979.tb00001.x
  9. [9] Kaplan, S. (1973). Cognitive maps in perception and thought. Image and environment: cognitive mapping and spatial behavior, 63–78. https://books.google.com/books
  10. [10] Ijiri, Y., & Jaedicke, R. K. (1966). Reliability and objectivity of accounting measurements. The accounting review, 41(3), 474–483. https://www.jstor.org/stable/244477
  11. [11] Stephens, R. G., Dillard, J. F., & Dennis, D. K. (1985). Implications of formal grammars for accounting policy development. Journal of accounting and public policy, 4(2), 123–148.
  12. [12] March, J. G. (1987). Ambiguity and accounting: the elusive link between information and decision making. Accounting, organizations and society, 12(2), 153–168. https://doi.org/10.1016/0361-3682(87)90004-3
  13. [13] Hosseingholi Zadeh, R. (2007). Soft calculations with fuzzy sets and neural networks. Mehr-e-Imaan Publications. (In Persian). https://daneshnegar.com/fa/product/46626
  14. [14] Vignaux, G. A., & Michalewicz, Z. (1991). A genetic algorithm for the linear transportation problem. IEEE transactions on systems, man, and cybernetics, 21(2), 445–452.
  15. [15] Holland, J. (1975). Adaptation in natural and artificial systems. Ann arbor, 7, 390–401.
  16. [16] Faghidian, S. F., & Fathizade, K. (2023). Identifying and prioritizing the factors affecting the services of electronic banking website in a fuzzy environment. Financial and banking strategic studies, 1(1), 69–76. (in Persian) DOI:10.22105/fbs.2023.178955
  17. [17] Zahedi, M., Khalilzadeh, M., & Javanshir, H. (2021). Designing a new fuzzy expert system for project portfolio risk management. Innovation management and operational strategies, 1(4), 403–421. (in Persian) DOI:10.22105/imos.2021.271975.1029

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Published

2024-06-29

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Vol. 1 No. 2 (2024): Accounting and Auditing with Applications

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How to Cite

El-Morsy, S., & Alburaikan, A. . (2024). Application of Genetic Algorithm and Fuzzy Sets to Logistic Decision-Making. Accounting and Auditing With Applications , 1(2), 92-102. https://doi.org/10.22105/aaa.v1i2.38

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