Expert System Models for Forecasting Forklifts Engagement in a Warehouse Loading Operation: A Case Study

  • Dejan Mirčetić University of Novi Sad, Faculty of Technical Sciences, Department of Traffic Engineering, Chair for Intermodal Transport and Logistics
  • Nebojša Ralević University of Novi Sad, Faculty of Technical Sciences, Department of Fundamentals Sciences, Chair of Mathematics
  • Svetlana Nikoličić University of Novi Sad, Faculty of Technical Sciences, Department of Traffic Engineering, Chair for Intermodal Transport and Logistics
  • Marinko Maslarić University of Novi Sad, Faculty of Technical Sciences, Department of Traffic Engineering, Chair for Intermodal Transport and Logistics
  • Đurđica Stojanović University of Novi Sad, Faculty of Technical Sciences, Department of Traffic Engineering, Chair for Intermodal Transport and Logistics
DOI: https://doi.org/10.7307/ptt.v28i4.1900
Keywords: forklifts, loading operation, expert systems, machine learning, ANFIS, CART tree,

Abstract

The paper focuses on the problem of forklifts engagement in warehouse loading operations. Two expert system (ES) models are created using several machine learning (ML) models. Models try to mimic expert decisions while determining the forklifts engagement in the loading operation. Different ML models are evaluated and adaptive neuro fuzzy inference system (ANFIS) and classification and regression trees (CART) are chosen as the ones which have shown best results for the research purpose. As a case study, a central warehouse of a beverage company was used. In a beverage distribution chain, the proper engagement of forklifts in a loading operation is crucial for maintaining the defined customer service level. The created ES models represent a new approach for the rationalization of the forklifts usage, particularly for solving the problem of the forklifts engagement in
cargo loading. They are simple, easy to understand, reliable, and practically applicable tool for deciding on the engagement of the forklifts in a loading operation.

References

Waters D. Logistics: An Introduction to Supply Chain Management. New York: Palgrave Macmillan; 2003.

Chopra S, Meindl P. Supply chain management-Strategy, Planning, and Operation. 3rd ed. New Jersey: Pearson Prentice Hall; 2007.

McGillivray R, Saipe A. Logging on. Materials Management and Distribution; 1996 January. p. 19-23.

Druzdzel MJ, Flynn RR. Decision Support Systems. In: Kent A, editor. Encyclopedia of Library and Information Science. 2nd Ed. New York: Marcel Dekker, Inc.; 2002.

Turban E. Decision Support and Expert Systems. 2nd ed. New York: Macmillan; 1998.

Mircetic D, Lalwani C, Lirn T, Maslaric M, Nikolicic S. Anfis Expert System For Cargo Loading As Part Of Decision Support System In Warehouse. 19th International Symposium on Logistics (ISL 2014); 2014; Vietnam, Ho Chi Minh City: Centre for Concurrent Enterprise Nottingham University Business School.

Rainer RK, Turban E. Introduction to Information Systems: Supporting and Transforming Business. 2nd ed. John Wiley & Sons; 2008.

Turban E, Aronson J, Liang T-P. Decision Support Systems and Intelligent Systems. 7th ed. Pearson Prentice Hall; 2005.

Hastie T, Tibshirani R, Friedman J, Hastie T, Friedman J, Tibshirani R. The elements of statistical learning. Springer; 2009.

Marakas GM. Decision support systems in the 21st century. Upper Saddle River: Prentice Hall; 2003.

Min H, Eom SB. An Integrated Decision Support System for Global Logistics. International Journal of Physical Distribution & Logistics Management. 1994;24:29-39.

Eom HB, Lee SM. A Survey of Decision Support System Applications (1971–April 1988). Interfaces. 1990;20(3):65-79.

Eom SB, Lee SM, Kim EB, Somarajan C. A survey of decision support system applications (1988-1994). J Oper Res Soc. 1998 Feb;49(2):109-20. PubMed PMID:WOS:000072043500002.

Eom S, Kim E. A survey of decision support system applications (1995-2001). J Oper Res Soc. 2006 Nov;57(11):1264-78. PubMed PMID:WOS:000241588500002.

Kanović Ž, Bugarski V, Bačkalić T. Ship lock control system optimization using GA, PSO and ABC: a comparative review. Promet – Traffic & Transportation. 2014;26(1):23-31.

Olson DL, Courtney JF. Decision Support Models and Expert Systems. New York: Macmillan; 1992.

Turban E. Decision Support and Expert Systems: Management Support Systems. 4th ed. New York: Macmillan; 1995.

Kock ED. Decentralising the Codification of Rules in a Decision Support Expert Knowledge Base [MSc thesis]. University of Pretoria; 2005. Available from: http://repository.up.ac.za/handle/2263/22959

Riid A. Transparent Fuzzy Systems: Modeling and Control [PhD thesis]. Tallinn: Tallinn Technical University; 2002 [cited 2014 June 24]. Available from: http://www.dcc.ttu.ee/andri/teosed/tfs-mac.pdf

Takagi T, Sugeno M. Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Transactions on Systems, Man, and Cybernetics. 1985;15(1):116-32.

Jang J-SR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on System, Man, and Cybernetics. 1993;23(3):665-85.

Liao SH. Expert system methodologies and applications – a decade review from 1995 to 2004. Expert Syst Appl. 2005 Jan;28(1):93-103. PubMed PMID:WOS:000225261500009.

Zadeh LA. The Concept of a Linguistic Variable and its

Applications to Approximate Reasoning-I. Information

Sciences. 1975;8:199-249.

Mendel JM. Fuzzy Logic Systems for Engineering: А Тutorial. IEEE. 1995 Mar;83(3):345-77.

Wang L-X. A Course in Fuzzy Systems and Control. Upper Saddle River: Prentice Hall PTR; 1997.

Teodorović D, Šelmić M. Computer intelligence in traffic and transportation [in Serbian]. Belgrade: University of Belgrade; 2012.

Breiman L, Friedman J, Stone C, Olshen R. Classification and regression trees. CRC press; 1984.

Lewis RJ. An introduction to classification and regression tree (CART) analysis. Annual Meeting of the Society for Academic Emergency Medicine in San Francisco, California; 2000.

Jang J-SR, Sun C-T, Mizutani E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River: Prentice Hall; 1997.

Published
2016-08-31
How to Cite
1.
Mirčetić D, Ralević N, Nikoličić S, Maslarić M, Stojanović Đurđica. Expert System Models for Forecasting Forklifts Engagement in a Warehouse Loading Operation: A Case Study. Promet [Internet]. 2016Aug.31 [cited 2024Dec.26];28(4):393-01. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/1900
Issue
Vol 28 No 4 (2016)
Section
Articles

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