An integrated selection and scheduling for disjunctive network problems
An integrated selection and scheduling for disjunctive network problems
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DOI：
10.1016/j.cie.2011.12.022
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发表时间：
2013-05
期刊：
影响因子：
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通讯作者：
S. O. Tasan;M. Gen
中科院分区：
文献类型：
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作者：
S. O. Tasan;M. Gen
在网络优化问题中，当问题规模变大时，如真实的项目管理、装配和运输问题，传统的集成选择和调度求解方法的应用变得复杂。这类问题通常由具有可选子图的析取网络组成。传统上，为了处理分离网络中的替代子图，研究人员依次考虑问题的首先选择，然后求解（调度）。然而，使用传统的方法导致问题的结构完整性的损失。当这种方法失去其完整的结构时，网络问题也失去了其完整性。因此，这两个问题，即选择和时间安排，必须一并考虑。为了提供一种新的方法来保持问题的完整性，我们提出了一个集成的遗传算法来解决这个选择和调度问题一起使用多阶段的决策方法。在这项研究中，两个新定义的问题，不同的析取网络和不同的特点，即资源受限的多项目调度（rc-mPSP）模型的替代项目和可变的活动时间，和U形装配线平衡（uALB）模型的替代替代调度，已被解决使用所提出的解决方案的方法，以突出所提出的解决方案的适用性和性能。
In network optimization problems, the application of conventional integrated selection and scheduling solution methods becomes complicated when the size of the problems, such as real life project management, assembly and transportation problems, get bigger. These kinds of problems often consist of disjunctive networks with alternative subgraphs. Traditionally, in order to handle alternative subgraphs in a disjunctive network, researchers consider first selection and then solution (scheduling) of the problem sequentially. However, the use of traditional approaches result in the loss of the problem structural integrity. When the approach losses its integrated structure, the network problem also losses its integrity. Therefore, these two issues, i.e. selection and scheduling, have to be considered together. To provide a new approach to maintain the problem integrity, we proposed an integrated genetic algorithm for solving this selection and scheduling problems together using a multi-stage decision approach. In this study, two newly defined problems with different disjunctive networks and different characteristics, i.e. resource constrained multiple project scheduling (rc-mPSP) models with alternative projects and variable activity times, and U-shaped assembly line balancing (uALB) models with alternative subassemblies, have been solved using the proposed solution approach to highlight the applicability and performance of the proposed solution approach.