A consensus fixing heuristic to workforce investments in field service

This is the authors' abstract. We don't add key points for this paper.

Workforce planning is a critical challenge across various sectors, encompassing strategic, tactical, and operational decision-making to optimise goals such as profit maximisation, greenhouse emission reduction, and customer satisfaction. This paper addresses the strategic workforce planning problem as investment planning in the context of field service in telecommunications, focusing on the Technician Routing and Scheduling Problem (TRSP). To tackle these complexities, we propose a novel strategic framework inspired by a new methodology called consensus fixing . Our approach evaluates a set of scenarios (here, operational days) independently using a complete TRSP model, computing the value of each potential workforce investment. A consensus scheme then fixes a single workforce investment decision across all scenarios iteratively, one by one, until a stopping criterion is met. This methodology allows for parallelisation and maintains low computational complexity, integrating detailed operational considerations into strategic decision-making. We apply our framework to real-life data from the Danish telecommunication company TDC-NET, utilising an Adaptive Large Neighbourhood Search (ANLS) to solve each scenario. Different consensus schemes are investigated, and the performance of the hired workforce is evaluated through out-of-sample testing and comparison to a novel scenario-tailored solution. Due to its fast run-time, the methodology can equip decision-makers with a wide range of detailed workforce investment solutions, including actual routes, that transparently demonstrate the trade-offs between a robust workforce and a cost-efficient one. The scenario-tailored solutions enhance this transparency by providing a benchmark for the best possible driving, hiring, and unserved task costs, helping to assess how effective a proposed workforce is. This provides decision-makers with a solid foundation for selecting a set of investments that balances customer satisfaction with operational costs. Based on the results, we also highlight several other valuable managerial insights, such as demonstrating that a more detailed set of possible investments may not always lead to better results, and showing how the robustness of the investments chosen by our methodology can be verified through a post-investment sensitivity analysis.