Deadheading minimization in the last-mile delivery

Deadheading in last-mile delivery significantly increases operational costs, fuel consumption, and carbon emissions which makes critical challenges to the logistics sector. To address this, a multi-objective optimization framework is developed and solved using five heuristic algorithms: Genetic Algorithm(GA), Particle Swarm Optimization(PSO), Aquila Optimization(AO), Grey Wolf Optimization (GWO), and Simulated Annealing (SA). Adaptive parameter tuning is applied to enhance algorithm performance, with each method evaluated based on cost, solving time, and delivery assignment efficiency. Among them, Simulated Annealing emerges as the most effective approach, offering valuable insights into the trade-offs between solution quality, computational efficiency, and operational feasibility.