International Journal of Iron & Steel Society of Iran

International Journal of Iron & Steel Society of Iran

Improving the Steel Household Appliances Production Through Simulation and Gray Proximity Indexed Value

Document Type : Research Paper

Authors
S. H. Seyedi 1
P. Farhadi 2
M. Gharibi 2
S. M. Seyedi 3
1 Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran
2 Department of Management, Zand Higher Education Institute, Shiraz, Iran
3 Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
10.22034/ijissi.2025.2036644.1298
Abstract
 Improving the efficiency of the production process is one of the most critical goals for manufacturing companies to reduce costs and compete in the market. Identifying and resolving process bottlenecks is essential to enhance efficiency. This issue is particularly significant in the steel products industry due to the complexity and large scale of production lines. This research investigated the efficiency improvement of the sink production process at Alborz Steel Company using Discrete event simulation and gray multi-criteria decision-making. Through simulation, the current conditions of the production line and process bottlenecks were identified, and five suggested scenarios were examined for their improvement. These scenarios are designed based on preventive maintenance, adding operators, outsourcing part of the process, adding a new device, and combining the previous four scenarios. The results show that the combined four scenarios would improve production productivity by 6.08% and reduce waste by 27.13%, with a 6.66% increase in the number of personnel in the production process. However, the criteria of cost, improvement in the company's technical capabilities, and ease of execution should also be considered in order to prioritize the scenarios. Hence, the Proximity Indexed Value approach was used in multi-criteria decision-making to prioritize the scenarios. Given the probabilistic nature of the simulation results and the uncertainty of experts' opinions, the gray Proximity Indexed Value method was developed. The prioritization of the scenarios based on simulation results and expert criteria was preventive maintenance, adding operators, adding a new device, and outsourcing part of the process. 
Keywords
Value.Efficiency Improvement
Production Process
Discrete Event Simulation
Steel
Alborz Dishwasher
Gray Proximity Indexed Value
Subjects
 [1] Tahmasbi P, Investigation in the relationship between competition, quality, price, market share, and financial efficiency in the poultry industry (Doctoral dissertation, University of Essex).
[2] Jarrell L, Kirby D, Managing quality assurance at community colleges in Ontario, Canada: experiences and perspectives of front-line quality managers, Quality Assurance in Education. 2024; 32(2): 274-90.
[3] Fakhreddin F, Foroudi P, The impact of market orientation on new product performance through product launch quality: A resource-based view, Cogent Business & Management. 2022; 9(1): 2108220.
[4] Fasih N, Mahmoodi A, Pricing and Determining Product Greenness Level in Competing Supply Chains Under Government Intervention, Journal of Industrial Engineering Research in Production Systems. 2021; 9(18): 97-109.
[5] Robert M, Giuliani P, Gurau C, Implementing industry 4.0 real-time performance management systems: the case of Schneider Electric, Production Planning & Control. 2022; 33(2-3): 244-60.
[6] Mirmezhad S.A, Seyyedi S.H, Yousefi hanoomarvar A, Developing a Performance Evaluation System Based on the BSC, ANP and Games Theory, Organizational Resources Management Researches. 2020; 9(3): 133-50.
[7] Esfandiyari R, Sadeghian R, Multi-level supply chain network Design based on the multiple objectives of reliability, cost and delivery time using a meta-heuristic solution method, Journal of Industrial Engineering Research in Production Systems. 2022; 10(20): 91-111.
[8] Seyedi S.H, Farhadi P, Ranjbar F, Lotfi M, Identify and Prioritize the Critical Success Factors for Implementing Project Management in Metro Construction, Road, 2024; 32(121): 385-400.
[9] Arsawan I.W, Koval V, Rajiani I, Rustiarini N.W, Supartha W.G, Suryantini N.P, Leveraging knowledge sharing and innovation culture into SMEs sustainable competitive advantage, International journal of productivity and performance management. 2022; 71(2): 405- 28.
[10] Najibi S.M, Seyedi S.H, Farhadi P, Kharazmi E, Shojaei P, Delavari S, Lotfi F, Kavosi Z, Development of a model for predicting hospital beds shortage and optimal policies using system dynamics approach, BMC Health Services Research. 2022; 22(1): 1525.
[11] Vázquez-Serrano J.I, Cárdenas-Barrón L.E, Vicencio-Ortiz J.C, Matis T, Gaitán-Mercado CM, Peimbert-García R.E, Hybrid optimization and discrete-event simulation model to reduce waiting  times in a primary health center. Expert Systems with Applications. 2024; 238: 121920.
[12] Mohammadi N, Seyedi S.H, Farhadi P, Shahmohamadi J, Ganjeh Z.A, Salehi Z, Development of a scenario-based blood bank model to maximize reducing the blood wastage, Transfusion Clinique et Biologique. 2022; 29(1): 16-9.
[13] Ataee Gortolmesh A, Toloie Eshlaghy A, Pourebrahimi A, Business process modeling through hybrid simulation approach (case study: One of the iranian banks), Industrial Management Journal. 2020; 11(4): 600-20.
[14] Peña-Graf F, Órdenes J, Wilson R, Navarra A, Discrete event simulation for machine-learning enabled mine production control with application to gold processing, Metals. 2022; 12(2): 225.
[15] Aseyedesmaeili K, Taghaddos H, Enhancing construction process employing lean principles and discrete event simulation, Sharif Journal of Civil Engineering. 2021; 37(2.1): 39-49.
[16] Badakhshan E, Pishvaee M.S, Sahebi H, A Simulation-based Optimization Model for Integration of Cash and Material-Flow Planning within a Supply Chain, Journal of Industrial Management Perspective. 2016; 6(1): 31-51.
[17] JavidMoayed M, Afshar Kazemi M.A, Modeling the Market Analysis Process through the SD-DES Hybrid Simulation Approach (Case Study: Mobile Market of Iran), Industrial Management Studies. 2021; 19(62): 23-66.
[18] Chowdary B.V, Rayside A, Sustainable recycling strategies to reduce plastic waste: application of circular economy principles and discrete event simulation modelling to beverage manufacturing industry, International Journal of Process Management and Benchmarking. 2024; 16(2): 139-63.
[19] Paape N, van Eekelen J.A, Reniers M.A, Automated design space exploration for poultry processing systems using discrete-event simulation, International Journal of Food Engineering. 2024.
[20] Rotunno G, Lo Zupone G, Carnimeo L, Fanti M.P, Discrete event simulation as a decision tool: A cost benefit analysis case study, Journal of Simulation. 2024; 18(3): 378-94.
[21] Wang Z, Liao W, Smart scheduling of dynamic job shop based on discrete event simulation and deep reinforcement learning, Journal of Intelligent Manufacturing. 2024; 35(6): 2593-610.
[22] Ortiz-Barrios M, Arias-Fonseca S, Ishizaka A, Barbati M, Avendaño-Collante B, Navarro-Jiménez E, Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study, Journal of Business Research. 2023; 160: 113806.
[23] Taleb M, Khalid R, Ramli R, Nawawi M.K, An integrated approach of discrete event simulation and a  non-radial super efficiency data envelopment analysis for performance evaluation of an emergency department, Expert Systems with Applications. 2023; 220: 119653.
[24] Prajapat N, Tiwari A, A review of assembly optimisation applications using discrete event simulation, International Journal of Computer Integrated Manufacturing, 2017; 30(2-3): 215-28.
[25] Lidberg S, Aslam T, Pehrsson L, Ng A.H, Optimizing real-world factory flows using aggregated discrete event simulation modelling: Creating decision-support through simulation-based optimization and knowledge-extraction, Flexible Services and Manufacturing Journal. 2020; 32(4): 888-912.
[26] Eftekhari A, Torabi A,Drawing a developed strategy map in balanced scorecard model in an iron and steel company, IEEE Engineering Management Review. 2022; 50(3): 58-69.
[27] Zandieh M, Motallebi S, Determination of production planning policies for different products in process industries: using discrete event simulation, Production Engineering. 2018; 12(6): 737-46.
[28] Vieira G.E, Kück M, Frazzon E, Freitag M, Evaluating the robustness of production schedules using discrete-event simulation, IFAC-PapersOnLine. 2017; 50(1): 7953-8.
[29] Afifi M, Fotouh A, Al-Hussein M, Abourizk S, Integrated lean concepts and continuous/discrete-event simulation to examine productivity improvement in door assembly-line for residential buildings, International Journal of Construction Management. 2022; 22(13): 2423-34.
[30] Detty R.B, Yingling J.C, Quantifying benefits of conversion to lean manufacturing with discrete event simulation: a case study, International journal of production research. 2000; 38(2): 429-45.
[31] Yuan Z, Qiao Y, Guo Y, Wang Y, Chen C, Wang W, Research on lean planning and optimization for precast component production based on discrete event simulation, Advances in Civil Engineering. 2020; 2020(1): 8814914.
[32] Zupan H, Herakovic N, Production line balancing with discrete event simulation: A case study, IFACpapersonline. 2015; 48(3): 2305-11.
[33] Gonçalves A.T, Fagundes L.D, Miranda R.D, Lima R.D, Discrete event simulation as a decision-making tool for end-of-life tire reverse logistics in a Brazilian  city consortium, Environmental Science and Pollution Research. 2019; 26: 23994-4009.
[34] Zandieh M, Motallebi S, Improvement of Business Performance by Enhancing the Productivity and Profitability of Production Lines.
[35] Corrotea H, Portales H, Amigo L, Gatica G, Troncoso-Palacio A, Mondragón D, Ramos M. Maintenance process analysis in a port cargo company through discrete event simulation, Procedia Computer Science. 2024; 231: 415-20.
[36] Mwanza J, Telukdarie A, Igusa T, Optimising maintenance workflows in healthcare facilities: a multi-scenario discrete event simulation and simulation annealing approach, Modelling. 2023; 4(2): 224-50.
[37] Martinez P, Ahmad R, Quantifying the impact of inspection processes on production lines through stochastic discrete-event simulation modeling. Modelling. 2021; 2(4): 406-24.
[38] Solding P, Thollander P, Moore P.R, Improved energy-efficient production using discrete event simulation, Journal of Simulation. 2009; 3(4): 191-201.
[39] Huynh B.H, Akhtar H, Li W, Discrete event simulation for manufacturing performance management and optimization: a case study for model factory, In2020 9th international conference on industrial technology and management (icitm). 2020: 16-20.
[40] Navarra A, Discrete Event Simulation for the Integrated Management of Mining and Metallurgical Systems, InConference of Metallurgists. 2023: 957-963.
[41] Navarra A, Marambio H, Oyarzún F, Parra R, Mucciardi F, System dynamics and discrete event simulation of copper smelters, Minerals & Metallurgical Processing. 2017; 34:96-106.
[42] García-Cascales M.S, Lamata M.T, On rank reversal and TOPSIS method, Mathematical and computer modelling. 2012; 56(5-6): 123-32.
[43] Mufazzal S, Muzakkir S.M, A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals, Computers & Industrial Engineering. 2018; 119: 427-38.
[44] Dahooie J.H, Dehshiri S.J, Banaitis A, Binkytė- Vėlienė A, Identifying and prioritizing cost reduction solutions in the supply chain by integrating value engineering and gray multi-criteria decisionmaking, Technological and Economic Development of Economy. 2020; 26(6): 1311-38.