Modern Research in Decision Making

Development of STEM Decision-Making Technique using Simulation Approaches and Utility Function

Document Type : Original Article

Authors

Parviz Rahimi Kakehjoob 1
Hiwa Farughi 2

1 PhD student, Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

2 Professor, Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

Abstract
Various techniques and approaches have been presented to solve multi-objective decision making problems under different assumptions. The STEM technique is also one of the most widely used techniques for dealing with this group of problems, especially for solving multi-objective linear programming (MOLP) models. In the current research, a new approach has been proposed as the development of the mentioned method. In this regard, the second phase of the method is integrated with the simulation process and the concept of utility function has been used in order to determine the probability of selecting targets. For each of the selected targets, several random adjustment rates are defined. Random selection of satisfactory functions based on their utility and using simulation tools, will "create diversity in the selected objectives for adjustment". Also, by considering different and random adjustment rates for selected functions, while facilitating the decision-making process and providing an analytical report to the decision-maker, it will be possible to apply "different levels of satisfaction" of the decision-maker. A three-objective problem with five constraints, is solved using the proposed method and its results are presented. The results indicate that applying the above changes will lead to solving some of the basic limitations of this method that have been mentioned in previous studies. Comparing the proposed technique with the basic method, shows the overall superiority of the proposed method, especially in the criteria related to interaction with the decision maker.

Keywords

MOLP
STEM
Utility Function
Simulation
[1] Benayoun, R., Montgolfier, J., Tergny, J., Laritchev, O. (1971). Linear programming with multiple objective functions: step method (STEM). Mathematical Programming 1, 366-375. https://doi.org/10.1007/BF01584098
[2] Jeong, I.J., Kim, W.J. (2005). D-STEM: a modified step method with desirability function concept. Computers & Operations Research, 32, 3175-3190. https://doi.org/10.1016/j.cor.2004.05.006
[3] Xevi, E., Khan, S. (2007). A STEP Method based multiple objective methodology for irrigation water management to model preferences and tradeoffs. International Congress on Modelling and Simulation, 149-155. DOI:10.13140/2.1.4489.8086
[4] Lee, P., Kang, S. (2018). An Interactive Multi objective Optimization Approach to Supplier Selection and Order Allocation Problems Using the Concept of Desirability. Information 2018, 9, 130. https://doi.org/10.3390/info9060130
[5] Teghem Jr, J., Dufrane, D., Thauvoye, M., Kunsch, P. (1986). Strange: An interactive method for multi objective linear programming under uncertainty. European Journal of Operational Research, 26, 65-82. https://doi.org/10.1016/0377-2217(86)90160-8
[6] Mollaghasemi, M., W.Evans, G. (1994). Multicriteria Design of Manufacturing Systems Through Simulation Optimization. IEEE Transactions on Systems, Man, And Cybernetics, Vol. 24, No. 9.  DOI: 10.1109/21.310518
[7] Al-Agha, A. (2015). A multi-objective linear programming model for national planning. International Journal of Economics, Commerce and Management, Vol. III, No. 4.
[8] Nadershahi, M., Safi.S, A.D., Tavakkoli moghaddam, R. (2019). Development of a Genetic Algorithm-based Decision Neural Network for the Preference Assessment in Multi-objective Decision-Making Problems. Journal of Modern Research in Decision Making (Scientific Research Quarterly), 4, 3, 127-153. [in Persian]  DOR: 20.1001.1.24766291.1398.4.3.6.4
[9] Devi, S., Madhusmita Nayak, M., Patnaik, S. (2021). A Study on Decision Making by Estimating Preferences Using Utility Function and Indifference Curve. Intelligent and Cloud Computing, Smart Innovation, Systems and Technologies 194, 373-386. https://doi.org/10.1007/978-981-15-5971-6_41
[10] Belenson, Sh., C.Kapur, K. (1971). An algorithm for solving multi criterion linear programming problems with examples. Operational Research Quarterly, Vol. 24, No. 1. https://doi.org/10.2307/3008036
[11] Wallenius, J. (1975). Comparative Evaluation of Some Interactive Approaches to Multi criterion Optimization. Management Science, 21(12), 1387-1396. https://doi.org/10.1287/mnsc.21.12.1387
[12] Nijkamp, P., Rietveld, P. (1976). Multi-objective programming models New Ways in Regional Decision-Making. Regional science and Urban Economics, 6, 253-274. https://doi.org/10.1016/0166-0462(76)90002-8
[13] Dinkelbach, W., Isermann, H. (1980). Resource allocation of an academic department in the presence of multiple criteria. Computer & Operational Research, 7, 99-106.
[14] Masud, A., Hwang, C.L. (1981). Interactive Sequential Goal Programming. Operational Research, 32, 391-400. https://doi.org/10.1057/jors.1981.76
[15] Evans, G. (1984). An Overview of Techniques for Solving Multi objective Mathematical Programs. Management Science, 3 30(11), 1268-1282. https://doi.org/10.1287/mnsc.30.11.1268
[16] Brockhoff, K. (1985). Experimental test of MCDM algorithms in a modular approach. European Journal of Operational Research, 22, 159-166. https://doi.org/10.1016/0377-2217(85)90224-3
[17] Michalowski, W. (1987). Evaluation Of a Multiple Criteria Interactive Programming Approach: An Experiment. Information Systems and Operational Research, vol. 25, No. 2. https://doi.org/10.1080/03155986.1987.11732036
[18] Gardiner, L., Steuer, R. (1994). Unified interactive multiple objective programming. European Journal of Operational Research, 74, 391-406.  https://doi.org/10.1016/0377-2217(94)90219-4
[19] Park, K., Kim, K.J. (2005). Optimizing multi-response surface problems: How to use multi-objective optimization techniques. IIE Transactions, 37, 523-532. https://doi.org/10.1080/07408170590928992
[20] Pokharel, Sh. (2008). A two-objective model for decision making in a supply chain. International Journal of Production Economics, 111, 378-388.  https://doi.org/10.1016/j.ijpe.2007.01.006
[21] Roostaee, R., Izadikhah, M., Hosseinzadeh Lotfi, F. (2012). An Interactive Procedure to Solve Multi-Objective Decision-Making Problem: An Improvement to STEM Method. Journal of Applied Mathematics. https://doi.org/10.1155/2012/324712
[22] Mohanty, R.R., Paul, J.C., Panigrahi, B. (2015). An interactive multi-objective linear programming approach for watershed planning- a case study. Journal of Soil and Water Conservation, 14(1), 63-68. https://doi.org/10.52151/jae2016534.1613
[23] Amiri, M., Azizmohammadi, M., Hoseinnezhadi, M. (2014) Application of multi-level, multi-objective mathematical model to determine the optimal level of effective quality factors in plastic injection quality and using fuzzy dual response surface methodology (Case Study: Movable arm rest Teflon Bush for bus seat). Journal of Management Research in Iran, 18, 2, 1-24. [in Persian] DOR: 20.1001.1.2322200.1393.18.2.1.4
[24] Peric, T., Babic, Z., Matejas, J. (2018). Comparative analysis of application efficiency of two iterative multi objective linear programming methods (MP method and STEM method). CEJOR, 26, 565-583. https://doi.org/10.1007/s10100-018-0543-x
[25] Razuminkov, S.V. (2020). Application of the STEM Method in the Multi-Criteria Task of Linear Programming in the Design of a Cloud Technology Development Strategy. 2020 International Russian Automation Conference. DOI:10.1109/RusAutoCon49822.2020.9208066
[26] Henriques, C., Luque, M., Marcenaro-Gutierrez, O. (2020). Coupling distinct MOLP interactive approaches with a novel DEA hybrid model. International Transactions in Operational Research, 00, 1-22. https://doi.org/10.1111/itor.12879 
[27] Mosa, M., Azar, A., Rajabzadeh.G, A. (2021) A bi-objective MILP model for lot sizing and scheduling problem: possibilistic fuzzy goal programming approach. Journal of Modern Research in Decision Making (Scientific Research Quarterly), 6, 2, 181-212.  DOR: 20.1001.1.24766291.1400.6.2.8.8
[28] Omid, A., Azar, A., Dehghan Nayeri, M, & Moghbel, A. (2021) Developing a network Data Envelopment Analysis approach to compare the environmental efficiency of active industries in Tehran. Journal of Management Research in Iran, 25, 3, 193-216. [in Persian] DOR: 20.1001.1.2322200.1400.25.3.8.2
[29] Chen, L., Miettinen, K., Xin, B., Ojalehto, V. (2022) Comparing reference point based interactive multi objective Optimization methods without a human decision maker. Journal of Global Optimization, 85, 757-788. https://doi.org/10.1007/s10898-022-01230-3
[30] Fathollahi-Fard, A.M, Guangdong, T., Hue, K., Yapjng, F., Kuan Yew, W. (2023) Efficient Multi-objective Metaheuristic Algorithm for Sustainable Harvest Planning Problem. Computers & Operations Research, 158, 106304. https://doi.org/10.1016/j.cor.2023.106304
[31] Corrente, S, Greco, S., Matarazzo, B., Slowinski, R. (2024) Explainable interactive evolutionary Mult objective optimization. Omega, 122, 102925.  https://doi.org/10.1016/j.omega.2023.102925
[32] Zhou, X., Tan, W., Sun, Y., Huang, T., Yang, Ch. (2024) Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS. Expert Systems with Applications, 240, 122539. DOI:10.1016/j.eswa.2023.122539
[33] Ciaburro, G. (2020). Hands-On Simulation Modeling with Python. Packt Publishing Ltd, pp 6.
[34] Romero, C., Rehman, T. (2003). Multi criteria analysis for agricultural decisions. Developments in agricultural economics,11, 81-88.

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us