Dynamic Multi-Criteria Task Assignment in Field Service Management: A Proximity-Skill-Priority Optimization Framework

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Author(s)

Erike Azubuike Izuchukwu 1,* John David Eno 1 Nwandu Ikenna Caeser 1 Orban Aondowase James 2 Elei Florence Obiageli 1

1. Federal University of Technology, Owerri, Nigeria

2. University of Szeged, Hungary

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2026.05.03

Received: 3 Jul. 2026 / Revised: 5 Aug. 2026 / Accepted: 8 Sep. 2026 / Published: 8 Oct. 2026

Index Terms

Field Service Management, Multi-Criteria Optimization, Task Assignment, PSP Algorithm, Weighting Coefficients

Abstract

Field service management (FSM) has been faced with the need to efficiently assign tasks to technicians in field service operations (FSOs). The problem of inefficient task allocation has led to either an over utilization or an under utilization of the organization’s workforce and resources across cities and states. This study focuses on developing a resource-efficient multi-criteria algorithm (Proximity-Skill-Priority, PSP algorithm) for optimized field service task management. A mathematical modelling approach was used to design a unified score that takes into consideration the proximity of the technician to the task location, the skillset of each technician and the priority level of the task at hand while ensuring effective workload balance to ensure unbiased task assignment. The study used adaptive weighting coefficients to ensure that real-time adjustments are made when there are varying time conditions. The model was evaluated through a simulation experiment and benchmarked against three single-rule baselines: Proximity-First, Skill-First, and Priority-First assignment. The performance of the algorithms was measured in a 100-run simulation conducted on a synthetic dataset that is parameterized with the attributes of technicians, task priorities, and geospatial information.  The lowest average response time and travel distance were achieved by Proximity-First and Priority-First rules, whereas Skill-First produced the highest first-time-fix-rate surrogate at the cost of substantially higher travel and response values. PSP produced a more balanced trade-off: it improved first-time-fix performance when compared to the proximity-first and priority-first heuristics. At the same time, it balances the severe travel penalty observed under Skill-First assignment. Statistical analysis using Friedman and Wilcoxon signed-rank tests shows that there are significant differences in operation across the different algorithms for the evaluation metrics. These findings show that PSP is best interpreted as a compromise strategy that balances service quality and operational efficiency rather than maximizing any single metric in isolation.

Cite This Paper

Erike Azubuike Izuchukwu, John David Eno, Nwandu Ikenna Caeser, Orban Aondowase James, Elei Florence Obiageli, "Dynamic Multi-Criteria Task Assignment in Field Service Management: A Proximity-Skill-Priority Optimization Framework", International Journal of Education and Management Engineering (IJEME), Vol.16, No.5, pp. 32-43, 2026. DOI:10.5815/ijeme.2026.05.03

Reference

[1]A. Sadeghi, A. Maleki, M. H. Ahmadi, and A. H. Kiani, “Comparative evaluation of renewable energy investments : a multi-criteria decision-making approach,” Energy Convers. Manag. X, vol. 28, no. May, 2025, doi: 10.1016/j.ecmx.2025.101190.
[2]F. Al-hawari and H. Barham, “A machine learning based help desk system for IT service management,” J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 6, pp. 702–718, 2021, doi: 10.1016/j.jksuci.2019.04.001.
[3]N. Wilde and J. Alonso-Mora, “Statistically Distinct Plans for Multi-Objective Task Assignment,” IEEE Transactions on Robotics, vol. 40, pp. 2217–2232, 2024, doi: 10.1109/TRO.2024.3359530.
[4]S. Choudhury, J. K. Gupta, M. J. Kochenderfer, D. Sadigh, and J. Bohg, “Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints,” in Robotics: Science and Systems XVI, 2020, doi: 10.15607/RSS.2020.XVI.068.
[5]M. Van Der Zwan, G. Ermiş, and A. Sharpanskykh, “Journal of Air Transport Management Multi-agent task allocation and path planning for autonomous ground support equipment,” J. Air Transp. Manag., vol. 129, no. May, p. 102855, 2025, doi: 10.1016/j.jairtraman.2025.102855.
[6]Y. Wang, J. Chen, Z. Wu, P. Chen, X. Li, and J. Hao, “Efficient task migration and resource allocation in cloud – edge collaboration : A DRL approach with learnable masking ✩,” Alexandria Eng. J., vol. 111, no. June 2024, pp. 107–122, 2025, doi: 10.1016/j.aej.2024.10.015.
[7]M. Gabellini, F. Calabrese, A. Regattieri, D. Loske, and M. Klumpp, “A hybrid approach integrating genetic algorithm and machine learning to solve the order picking batch assignment problem considering learning and fatigue of pickers,” Comput. Ind. Eng., vol. 191, no. July 2023, p. 110175, 2024, doi: 10.1016/j.cie.2024.110175.
[8]B. Afshar-Nadjafi, “Multi-skilling in scheduling problems: A review on models, methods and applications,” Comput. Ind. Eng., vol. 151, p. 107004, Jan. 2021, doi: 10.1016/J.CIE.2020.107004.
[9]D. Carrascal, E. Rojas, J. A. Carral, I. Martinez-yelmo, and J. Alvarez-horcajo, “Heliyon Topology-aware scalable resource management in multi-hop dense networks,” Heliyon, vol. 10, no. 18, p. e37490, 2024, doi: 10.1016/j.heliyon.2024.e37490.
[10]Y. Xie, A. Gardi, M. Liang, and R. Sabatini, “Hybrid AI-based 4D trajectory management system for dense low altitude operations and Urban Air Mobility,” Aerosp. Sci. Technol., vol. 153, p. 109422, Oct. 2024, doi: 10.1016/J.AST.2024.109422.
[11]J. Aminu, R. Latip, Z. M. Hanafi, S. Kamarudin, and D. Gabi, “Systematic review of metaheuristic-based task scheduling strategies in edge computing environments,” Discov. Comput., 2025.
[12]H. Li, X. Wu, M. Ribeiro, and B. Santos, “Deep reinforcement learning approach for real-time airport gate assignment,” Operations Research Perspectives, vol. 14, art. 100338, 2025, doi: 10.1016/j.orp.2025.100338.
[13]G. Hofsté, A. Lund, A. Coroiu, M. Ottavi, and D. Lüdtke, “The online reconfiguration of a distributed on-board computer: The time and network behaviour of a dependable scheduling algorithm,” J. Syst. Archit., vol. 164, no. April, 2025, doi: 10.1016/j.sysarc.2025.103420.
[14]L. G. Bont, C. Blattert, L. Rath, and J. Schweier, “Automatic detection of forest management units to optimally coordinate planning and operations in forest enterprises,” J. Environ. Manage., vol. 372, no. November, 2024, doi: 10.1016/j.jenvman.2024.123276.
[15]S. Ren, L. Shi, Y. Liu, W. Cai, and Y. Zhang, “Robotics and Computer-Integrated Manufacturing A personalised operation and maintenance approach for complex products based on equipment portrait of product-service system,” Robot. Comput. Integr. Manuf., vol. 80, no. October 2022, p. 102485, 2023, doi: 10.1016/j.rcim.2022.102485.
[16]Z. Yang, “Application of evolutionary deep learning algorithm in construction engineering management system,” Syst. Soft Comput., vol. 7, no. May, p. 200317, 2025, doi: 10.1016/j.sasc.2025.200317.
[17]D. L. Pereira, “A Multiperiod Workforce Scheduling and Routing Problem with Dependent Tasks,” Computers & Operations Research, vol. 118, art. 104930, 2020, doi: 10.1016/j.cor.2020.104930.
[18]R. Pitakaso, P. Golinska-dawson, P. Luesak, and T. Srichok, “Journal of Open Innovation : Technology , Market , and Complexity Embracing open innovation in hospitality management: Leveraging AI-driven dynamic scheduling systems for complex resource optimization and enhanced guest satisfaction,” J. Open Innov. Technol. Mark. Complex., vol. 11, no. 1, p. 100487, 2025, doi: 10.1016/j.joitmc.2025.100487.
[19]J. K. Konjaang, J. Murphy, and L. Murphy, “Journal of Network and Computer Applications Energy-efficient virtual-machine mapping algorithm ( EViMA ) for workflow tasks with deadlines in a cloud environment,” J. Netw. Comput. Appl., vol. 203, no. March, 2022.
[20]F. Sukmana, B. Santosa, and A. Wibisono, “Enhancing dispatching rules for work order scheduling using genetic algorithms and technician resource management,” Procedia Comput. Sci., vol. 284, no. Isico 2025, pp. 1017–1027, 2026, doi: 10.1016/j.procs.2026.07.089.
[21]S. Peng and D. Wang, “Optimizing Order Dispatching and Task Scheduling Under Dynamic Workforce Elasticity: A Graph Transformer Proximal Policy Optimization Approach for Fabric Warehouses,” Algorithms, vol. 19, no. 6, 2026, doi: 10.3390/a19060495.
[22]D. Zago, A. Hottung, F. M. Gilbert, R. Cancelliere, and K. Tierney, “Reinforcement Learning Guided Neural Deconstruction Search for Flexible Job Scheduling,” Machine Learning, vol. 115, no. 8, art. 180, 2026, doi: 10.1007/s10994-026-07116-9.
[23]K. Dornala, “Predictive analytics for dynamic field technician routing in telecommunications service provisioning,” Sarcouncil J. Eng. Comput. Sci., vol. 4, no. 10, pp. 227–238, 2025. doi: 10.5281/zenodo.17430658.
[24]E. Bangerter, D. Schindl, M. P. Paneque, N. E. Tellache, and R. Griset, “A column-generation approach for an electricity technician routing and scheduling problem with a lexicographic objective,” arXiv preprint arXiv:2604.05153, 2026, doi: 10.48550/arXiv.2604.05153.
[25]M. Elyasi, A. Dems, Y. Adulyasak, O. Arslan, and J.-F. Cordeau, “The technician routing and scheduling problem with skills and time-sensitive returns under uncertainty,” Transportation Research Part B: Methodological, vol. 214, art. 103601, 2026, doi: 10.1016/j.trb.2026.103601.
[26]R. Mousavi, J. F. Côté, and M. Darvish, “The vehicle routing problem with driver scheduling,” Comput. Oper. Res., vol. 194, no. February, p. 107507, 2026, doi: 10.1016/j.cor.2026.107507.
[27]U. Tahir, “Optimizing Service Operations Through Cost-to-Serve Analysis and Automated Scheduling A Service Optimization Case Study,” Aalto University, 2025.
[28]N. Tahouri, “Beyond Availability: A Digital Twin-Based Competency Matching Framework for Maritime Crew Assignment Under Operational Uncertainty,” in International Conference on Artificial Intelligence and Interdisciplinary Innovations in Science and Technology - Paris, 2026, pp. 1–23.