关键词: 装备维修保障, 伴随维修, 资源配置, 任务调度, 多目标优化

Abstract: The modern war has a fast pace and spans a wide range of regions, which puts forward higher requirements for the accompanying repair support mode. How to integrate the process of resource allocation and task scheduling in a complex battlefield, and give full play to the effectiveness of the accompanying repair system, has become the urgent demand and development direction of equipment maintenance support. Comprehensively considering the factors of multi-center, open, multi-repair state, time window, non-traversal, capacity limitation and so on, the idea of replenishing goods at any time is put forward for the first time, which combines the regional risk coefficient with the current cost of the maintenance teams. In order to maximize the maintenance benefit and minimize the risk cost, a more perfect mathematical model is established. Aiming at the above problem, the Multi-objective Artificial Bee Colony (MOABC) algorithm is improved, and the Multi-objective Artificial Bee Colony for Memorizing Multiple Honey Sources per Bee (MOABC-MMHS) algorithm is proposed. The algorithm shows good performance in terms of solution quality and convergence speed. Then, aiming at the problem that a small number of extreme values affect the mean, the use of Coverage (C) indicator is improved and displayed in the way of frequency. Finally, through experimental simulation and evaluation of multiple indicators, the scientific nature of the model and algorithm is verified, and the joint optimization of resource allocation and task scheduling for accompanying repair is realized. The study shows that the above model and algorithm are able to provide the appropriate assistant decision-making for accompanying repair support.

Key words: equipment maintenance support, accompanying repair, resource allocation, task scheduling, multi-objective optimization