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Simplifying Multiproject Scheduling Problem Based on Design Structure Matrix and Its Solution by an Improved aiNet Algorithm

机译:基于设计结构矩阵的多项目调度问题简化及其改进的aiNet算法求解

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Managing multiple project is a complex task involving the unrelenting pressures of time and cost. Many studies have proposed various tools and techniques for single-project scheduling; however, the literature further considering multimode or multiproject issues occurring in the real world is rather scarce. In this paper, design structure matrix (DSM) and an improved artificial immune network algorithm (aiNet) are developed to solve a multi-mode resource-constrained scheduling problem. Firstly, the DSM is used to simplify the mathematic model of multi-project scheduling problem. Subsequently, aiNet algorithm comprised of clonal selection, negative selection, and network suppression is adopted to realize the local searching and global searching, which will assure that it has a powerful searching ability and also avoids the possible combinatorial explosion. Finally, the approach is tested on a set of randomly cases generated from ProGen. The computational results validate the effectiveness of the proposed algorithm comparing with other famous metaheuristic algorithms such as genetic algorithm (GA), simulated annealing algorithm (SA), and ant colony optimization (ACO).
机译:管理多个项目是一项复杂的任务,涉及持续的时间和成本压力。许多研究提出了用于单项目调度的各种工具和技术。但是,进一步考虑现实世界中发生的多模式或多项目问题的文献很少。为了解决多模式资源受限的调度问题,本文设计了设计结构矩阵(DSM)和改进的人工免疫网络算法(aiNet)。首先,DSM用于简化多项目调度问题的数学模型。随后,采用由克隆选择,否定选择和网络抑制组成的aiNet算法来实现局部搜索和全局搜索,这将确保它具有强大的搜索能力,并避免了可能的组合爆炸。最后,该方法在ProGen生成的一组随机案例上进行了测试。与其他著名的元启发式算法(例如遗传算法(GA),模拟退火算法(SA)和蚁群优化(ACO))相比,计算结果验证了该算法的有效性。

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