Fairness-Aware Energy-Effective Electricity Technician Dispatch Problem

Abstract

Combinatorial optimization problems involve identifying the best solution from a wide set of alternatives; these often arise in logistics, scheduling, and resource allocation. These problems, such as the Traveling Salesman Problem (TSP) and the Multi-Depot Vehicle Routing Problem (MDVRP), are generally NP-hard, with solution spaces that increase exponentially, making brute-force methods impractical. We address a specific application of the MDVRP: the Electricity Technician Dispatch Problem (ETDP), which involves planning and optimizing technician routes for maintenance services to customers at various geographical locations while satisfying specific constraints and objectives. We focus on a variant of the ETDP that optimizes multiple objectives, including economic, environmental, and social. Economic objectives aim to reduce operational costs, such as fuel costs and technician wages. Environmental objectives focus on sustainability, for example, by minimizing gas emissions. Social objectives include fairness of workload and customer satisfaction. We will explore the ETDP problem in single- and multi-objective contexts and solve it using nature-inspired techniques.