Research on Joint Material Supply Task Scheduling Algorithm Under Unmanned In-the-Loop Material Supply System

The ever-growing advancements in unmanned and intelligent technologies have resulted in a notable increase in the utilization of unmanned equipment in material supply systems. However, the current command and control methods impose significant limitations on the scale and operational efficiency of these systems. To address this challenge, the future of material supply systems is anticipated to evolve towards Unmanned In-the-Loop System (UILSS). Consequently, this paper establishes a joint materiel supply task scheduling model for Unmanned In-the-Loop Material Supply System (UILMSS) in conjunction with the concept of joint operations. Additionally, a coding plan based on ordered time legs is proposed, and an improved Dual-Population Genetic Algorithm (DPGA) is employed to develop a joint task scheduling method for UILMSS. The effectiveness and superiority of the proposed method has been validated through simulations using a hypothetical scenario. The simulation results have convincingly demonstrated the potential of the proposed method, providing valuable technical support to UILMSS in the context of joint operations.