A leader-wingman-inspired cooperative-competitive optimization algorithm for sustainable part collection routing problem

Due to the challenge of just-in-time part collection in the automobile industry, as well as the growing interest in energy conservation, this paper focuses on the design of the sustainable part collection routes with flexible time windows and heterogeneous fleet. A bi-objective integer programming model is therefore established, aiming to minimize the total energy consumption for executing all collection tasks and the total penalty costs imposed on early and late deliveries. To efficiently solve this problem which is inherently NP-hard, a novel metaheuristic optimization algorithm based on the leader-wingman framework is proposed. The algorithm employs dual populations, one denoted as 'leaders' and the other as 'wingmen', to mimic the cooperation and competition mechanisms in drone operations. This division of labor encourages relentless improvement and adaptability throughout the optimization process, striking a balance between exploiting high-potential regions and exploring uncharted territories. Moreover, a two-stage repair mechanism is designed to repair the infeasible solutions. Finally, computational experiments reveal that the proposed algorithm outperforms the other five state-of-the-art algorithms in both solution quality and convergence speed, and the use of heterogeneous fleet could achieve significant reductions in energy consumption over a homogeneous one. Moreover, a specific case is applied to demonstrate the practicality of the proposed model and algorithm.