Integrated Multi-Criteria Optimization of Distributed Service Networks : An Operational Research Framework for Strategic Zoning, Resource Allocation and Real-Time Dispatching
Keywords:
Distributed Service Networks, Operational Research, Multi-Criteria Optimization, Zoning, Resource Allocation, Dispatching, Repositioning, Emergency Services, Mathematical Modeling, Real-Time Systems.Abstract
Efficient design and management of distributed service networks is critical for enhancing performance in both public and private sectors. This study presents an integrated operational research framework aimed at optimizing strategic zoning, resource allocation, and real-time dispatching within distributed service networks. The research emphasizes a multi-criteria decision-making approach, considering trade-offs between model complexity, data accuracy, computational effort, and decision-maker objectives. The model supports the optimization of sub-components—zoning based on geographical and municipal constraints, location planning under uncertainty, strategic allocation of resources, real-time dispatching policies, and short-term repositioning strategies. Methodology:The framework utilizes mathematical programming, including integer and linear programming, supported by simulation-based sensitivity analysis. Multi-objective optimization techniques are employed to balance criteria such as response time minimization, cost reduction, equityand system performance. Each functional component (zoning, location, allocation, dispatchingand repositioning) is modeled individually and integrated into a unified framework, validated using synthetic and real-world datasets from emergency service systems. Results: Experimental outcomes demonstrate that the integrated approach significantly reduces average response times and improves overall system performance compared to traditional single-criteria models. The zoning module enhanced compactness and accessibility, the resource allocation models ensured optimal utilization of service units, and the dispatching algorithms effectively minimized both expected and maximum response times under varying demand scenarios. The repositioning strategy proved effective in dynamically adapting to real-time fluctuations.
References
- Arabani, A. B., & Farahani, R. Z. (2012). Facility location dynamics: An overview of classifications and applications. Computers & Industrial Engineering, 62(1), 408–420.
- Deb, K., Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601.
- Farahani, R. Z., SteadieSeifi, M., & Asgari, N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling, 34(7), 1689–1709.
- Ghobadi, S., & Hashemi, H. (2016). A multi-objective model for reliable service facility location under uncertainty using NSGA-II: A case study in health care. Computers & Industrial Engineering, 102, 388–403.https://doi.org/10.1016/j.cie.2016.10.017
- Govindan, K., Khodaverdi, R., & Jafarian, A. (2015). A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner Production, 105, 212–223.https://doi.org/10.1016/j.jclepro.2014.01.045
- Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2014). Multi criteria decision making approaches for green supplier evaluation and selection: A literature review. Journal of Cleaner Production, 98, 66–83.
- Hakimi, S. L. (1964). Optimum locations of switching centers and the absolute centers and medians of a graph. Operations Research, 12(3), 450–459.
- Ho, W., Ma, X., & Ji, P. (2017). A hybrid MCDM approach for sustainable supplier selection in the fashion industry. Sustainability, 9(5), 761.https://doi.org/10.3390/su9050761
- Jena, S. K., Sarmah, S. P., & Sarkar, A. (2017). A NSGA-II algorithm for a multi-objective closed-loop supply chain model with demand uncertainty. Computers & Industrial Engineering, 112, 755–770.https://doi.org/10.1016/j.cie.2017.01.021
- Rezaei, J., Hemmes, A., &Tavasszy, L. A. (2017). Multi-criteria decision-making for complex sustainability trade-offs in supply chain design. Transportation Research Part D: Transport and Environment, 56, 275–287.https://doi.org/10.1016/j.trd.2017.08.006
- Wang, J., Liang, L., & Huang, Z. (2017). Multi-objective optimization models for location planning in supply chains under uncertainty. Computers & Operations Research, 77, 1–14.
- Yang, H., Sun, D., Yuan, H., & Wang, Y. (2013). A multi-objective optimization approach for emergency service logistics planning. Transportation Research Part E: Logistics and Transportation Review, 50, 87–100.
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