Intelligent Task Resource Planning Based on Adaptive Dynamic Resource Composition Model

Lu, Lingyun; Tang, Xinhuai

doi:10.1088/1742-6596/1176/3/032017

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Li et al [9] considered the priorities of different combat tasks based on the traditional collaborative planning problem, and planned the task sequence according to different priorities, which was more in line with the actual combat. In addition, based on considering the priority of tasks and resources at the same time, many scholars use greedy strategy to locally search for the solution space of the multi-imensional dynamic list scheduling model (MDLS) [10][11][12] and multi-PRI list dynamic scheduling model (MPLDS) [13][14][15], which have achieved good results in the scheduling problem of cooperative task time. However, this method does not take into account the coupling between the execution subjects and time of different and the same combat tasks in the solving process.…”

Section: Operational Task Time Sequence Cooperative Schedulingmentioning

confidence: 99%

A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number representation

2021

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In modern warfare, the comprehensiveness of combat domain and the complexity of tasks pose great challenges to operational coordination.To address this challenge, we use the improved triangular fuzzy number to express the combat mission time, first present a new multi-objective operational cooperative time scheduling model that takes the fluctuation of combat coordinative time and the time flexibility between each task into account. The resulting model is essentially a large-scale multi-objective combinatorial optimization problem, intractably complicated to solve optimally. We next propose multi-objective improved Bat algorithm based on angle decomposition (MOIBA/AD) to quickly identify high-quality solutions to the model. Our proposed algorithm improves the decomposition strategy by replacing the planar space with the angle space, which helps greatly reduce the difficulty of processing evolutionary individuals and hence the time complexity of the multi-objective evolutionary algorithm based on decomposition (MOEA/D). Moreover, the population replacement strategy is enhanced utilizing the improved bat algorithm, which helps evolutionary individuals avoid getting trapped in local optima. Computational experiments on multi-objective operational cooperative time scheduling (MOOCTS) problems of different scales demonstrate the superiority of our proposed method over four state-of-the-art multi-objective evolutionary algorithms (MOEAs), including multi-objective bat Algorithm (MOBA), MOEA/D, non-dominated sorting genetic algorithm version II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO). Our proposed method performs better in terms of four performance criteria, producing solutions of higher quality while keeping a better distribution of the Pareto solution set.

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