Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Design of An Agent-Based Simulation Model of Service Supply Chain

Document Type : Research Paper

Authors
1 Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
3 Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran
Abstract
The present research aimed to design an agent-based simulation model of the service supply chain. In this respect, library studies were first conducted, and then the research gap was found. Suppliers were divided into suppliers of fast-moving consumer goods and slow-moving consumer goods, repairmen, and medicine suppliers, while services were divided into general, emergency, specialized, and nursing services, and patients were divided into people with insurance and those without insurance. Also, conditions of the service supply chain in this hospital were investigated and analyzed from different aspects. Next, this supply chain was implemented using NetLogo software, and the amount of unfulfilled demand and other cases were checked, and various weaknesses and gaps were shown. In the following, it was tried to decrease the existing gaps by developing different scenarios. Although the results of all the scenarios showed improved conditions, apparently, fast-moving consumer goods are the least affected by the decreased demand gap compared to the developed scenarios. In the maintenance section, the cost of preventive and in fact its increase can have the greatest effect. Regarding the increase of people covered by health insurance, the increase of insured people is a better scenario than reducing the treatment costs, and in fact, the reduction in treatment costs increase the number of people covered by health care services as it should.
Keywords
Subjects

Backs, S., Jahnke, H., Lüpke, L., Stücken, M., & Stummer, C. (2021). Traditional versus fast fashion supply chains in the apparel industry: an agent-based simulation approach. Annals of Operations Research, 305, 487-512. https://doi.org/10.1007/s10479-020-03703-8
Bae, K. H., Mustafee, N., Lazarova-Molnar, S., & Zheng, L. (2022). Hybrid modeling of collaborative freight transportation planning using agent-based simulation, auction-based mechanisms, and optimization. Simulation, 98(9), 753-771. https://doi.org/10.1177/00375497221075614
Basingab, M., Rabelo, L., Rahal, A., Nagadi, K., Bukhari, H., & Andejany, M. (2022). Economic analysis of a massively populated internet of things system: An agent-based simulation approach. Engineering Management Journal, 34(1), 129-143.
Chobar, A. P., Adibi, M. A., & Kazemi, A. (2022). Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment, Development and Sustainability, 1-28.
Delshad, M. M., Chobar, A. P., Ghasemi, P., & Jafari, D. (2024). Efficient Humanitarian Logistics: Multi-Commodity Location–Inventory Model Incorporating Demand Probability and Consumption Coefficients. Logistics, 8(1), 9.
Esmaelian, M., Tavana, M., Santos Arteaga, F. J., & Mohammadi, S. (2015). A multicriteria spatial decision support system for solving emergency service station location problems. International Journal of Geographical Information Science, 29(7), 1187-1213.
Hajian Heidary, M. (2021). Agent-based simulation-optimization model for a bi-objective stochastic multi-period supply chain design problem. Journal of Industrial Engineering and Management Studies, 8(2), 175-195.
He, Q., Ghobadian, A., Gallear, D., Beh,L .S., & O Regan,N. (2016).Towards conceptualizing revers servise, supply chain Management: An International Journal, 21(2), 166-197
Hilletofth, P., & Lättilä, L. (2012). Agent based decision support in the supply chain context. Industrial Management & Data Systems, 112(8), 1217-1235.
Iraj, M., Chobar, A. P., Peivandizadeh, A., & Abolghasemian, M. (2024). Presenting a two-echelon multi-objective supply chain model considering the expiration date of products and solving it by applying MODM. Sustainable Manufacturing and Service Economics, 3, 100022.
Jahangiri, S., Abolghasemian, M., Ghasemi, P., & Chobar, A. P. (2023). Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions. International journal of industrial and systems engineering, 43(1), 1-19.
Leung, C. S. K., & Lau, H. Y. K. (2018). A hybrid multi-objective AIS-based algorithm applied to simulation-based optimization of material handling system. Applied Soft Computing Journal, 71, 553–567. http://doi.org/10.1016/j.asoc.2018.07.034
Lillrank, P., & Sarkka,M. (2011).The service machine as µ servise operation framework. Strategic Outsourcing.An exploratory case study from India Telecom industry. OPSEARCH, 53(2),358-374
Lohmer, J., Bugert, N., & Lasch, R. (2020). Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study. International journal of production economics, 228, 107882.
Mansouri, S., Azar, A., Divandari, A., & Ramezanian, R. (2017). Agent-Based Simulation of Banking Service Supply Chain Based on Service-Dominant Logic. Journal of Business Management, 9(3), 661-688.
McGarraghy, S., Olafsdottir, G., Kazakov, R., Huber, É., Loveluck, W., Gudbrandsdottir, I. Y., ... & Bogason, S. G. (2022). Conceptual system dynamics and agent-based modelling simulation of interorganisational fairness in food value chains: Research agenda and case studies. Agriculture, 12(2), 280.
Mehrani, K., Mirshahvalad, A., & Abbasi, E. (2019). Comparison of the Accuracy of Black Hole Algorithms and Gravitational Research and the Hybrid Method in Portfolio Optimization. International Journal of Finance & Managerial Accounting, 4(14), 111-126. 
Mehrani, K., Mirshahvalad, A., & Abbasi, E. (2019). Portfolio Optimization Using Black Hole Meta Heuristic Algorithm. Specialty Journal of Accounting and Economics, 5(2), 1-13.
Mohaghar, A., Abbasi, H. (2021). Designing and Explaining the Sustainability Model for Banking Supply Chain (A Case Study of Mellat Bank). Management Research in Iran, 23(3), 53-73.
Movahed, A. B., Movahed, A. B., & Nozari, H. (2024). Smart Life and Sustainability. ISciHub.
Nozari, H., & Aliahmadi, A. (2022). Lean supply chain based on IoT and blockchain: Quantitative analysis of critical success factors (CSF). Journal of Industrial and Systems Engineering, 14(3), 149-167.
Nozari, H., Szmelter-Jarosz, A., & Rahmaty, M. (2024). Smart Marketing Based on Artificial Intelligence of Things (AIoT) and Blockchain and Evaluating Critical Success Factors. In Smart and Sustainable Interactive Marketing (pp. 68-82). IGI Global.
Rahman, T., Taghikhah, F., Paul, S. K., Shukla, N., & Agarwal, R. (2021). An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic. Computers & Industrial Engineering, 158, 107401.
Rouzafzoon, J., & Helo, P. (2016). Developing service supply chains by using agent based simulation. Industrial management & data systems, 116(2), 255-270.
Sakhuja, S., Jain, V., Kumar, S., & Chandra, C. (2016). A Structured Review of Service Supply Chain Discipline: Potentials, Challenges, and Integrated Framework. Journal of the Academy of Business Education, 17.
Salahi, F., Daneshvar, A., Homayounfar, M., & Pourghader Chobar, A. (2023). Presenting an integrated model for production planning and preventive maintenance scheduling considering uncertainty of parameters and disruption of facilities. Journal of Industrial Management Perspective, 13(1), 105-39.
Tahmasebifard, H., Fazael, S., Souran, V., Mirzaagha, M., & Pouyan, M. M. (2018). The Effect of Competitive Intelligence on Marketing Capabilities and Organizational Performance. Australian Journal of Business and Management Research, 5(8), 11-19.
Utomo, D. S., Onggo, B. S., & Eldridge, S. (2018). Applications of agent-based modelling and simulation in the agri-food supply chains. European Journal of Operational Research, 269(3), 794-805. https://doi.org/10.1016/j.ejor.2017.10.041 
Volume 16, Issue 2 - Serial Number 2
Spring 2024
Pages 185-201

  • Receive Date 19 November 2023
  • Revise Date 06 March 2024
  • Accept Date 28 March 2024