• Title of article

    Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: Meta-heuristic algorithms

  • Author/Authors

    Birjandi, A. Faculty of Industrial Engineering - Islamic Azad University - South Tehran Branch - Tehran - Iran , Mousavi, S.Meysam Department of Industrial Engineering - Faculty of Engineering - Shahed University - Tehran - Iran , Vahdani, B. Department of Industrial Engineering - Faculty of Industrial and Mechanical Engineering - Qazvin Branch - Islamic Azad University - Qazvin - Iranof Engineering - Shahed University - Tehran - Iran

  • Pages
    20
  • From page
    2572
  • To page
    2591
  • Abstract
    Resource constrained project scheduling problem with multiple routes for flexible project activities (RCPSP-MR) is a generalization of the RCPSP, in which for the implementation of each flexible activity in main structure of the project, several exclusive sub-networks are considered. Each sub-network is regarded as a route for the flexible activity. The routes are considered for each flexible activity that are varied in terms of: 1) Number of activities required to execute; 2) Precedence relationship between activates; 3) Allocation of different renewable and nonrenewable resources to each activity; and 4) Effectiveness on the duration and cost of project completion. In this paper, a new mathematical formulation of RCPSP-MR is firstly presented. Then, two solving approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize costs of project completion. To evaluate the effectiveness of these proposed approaches, 50 problems (in very small, small, medium, and large-sized test problems) are designed and then are solved; Finally, comparisons are provided. Computational results show that the proposed GA generates high-quality solutions in a timely fashion.
  • Keywords
    Resource constrained project scheduling problem (RCPSP) , flexible activities , multiple routes , particle swarm optimization (PSO) , genetic algorithm (GA)
  • Journal title
    Scientia Iranica(Transactions E: Industrial Engineering)
  • Serial Year
    2020
  • Record number

    2554172