An integrated selection and scheduling for disjunctive network problems
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. (C) 2011 Elsevier Ltd. All rights reserved.
disjunctive network problems alternative subgraphs genetic algorithms multi-stage decisions fuzzy logic controller resource constrained multiple project scheduling problem (rc-mpsp)