An Elite Knowledge-Based Multi-Objective Discrete Artificial Bee Colony Joint Scheduling Algorithm

In the context of rising energy costs and environmental concerns, the challenge of energy consumption has become paramount for the manufacturing industry. Traditional production manufacturing systems are seeking innovative solutions to enhance efficiency and reduce energy consumption. One promising approach involves the integration of unmanned aerial vehicles (UAVs) into the production process, leveraging their flexibility and efficiency. This research addresses the joint scheduling problem of parallel industrial machining units and UAVs (PMSP-UAV), with the dual objectives of minimizing makespan and total energy consumption. The energy consumption metrics considered during the production process include startup, running, and idle energy. To simultaneously optimize the makespan and energy consumption objectives, we propose a knowledge-based multi-objective discrete artificial bee colony (MODABC) algorithm. This algorithm incorporates external archiving (EA) and an elite knowledgebased guidance strategy to enhance convergence. A knowledge-based local search method is applied to the elites to enhance their quality, while the elite knowledge-based guidance scout bee phase prevents premature convergence. Finally, the efficacy of the proposed algorithm is rigorously validated through extensive testing on a dataset comprising over 100 real-world instances derived from an operational factory setting.