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An adaptive crossover genetic algorithm with simulated annealing for multi mode resource constrained project scheduling with discounted cash flows

Author

Listed:
  • Vijay S. Bilolikar
  • Karuna Jain
  • Mahesh Sharma

Abstract

This paper presents an adaptive crossover genetic algorithm with simulated annealing metaheuristic procedure for solving a multimode resource-constrained project scheduling problem with discounted cash flows for minimising costs. To solve the problem, a genetic algorithm is proposed for the global search, and simulated annealing is used for the local search. Two crossover operators are employed. A mathematical model is developed for the problem. Detailed computational experiments are performed on a standard problem set with randomly generated resource costs to evaluate the performance of the proposed hybrid approach.

Suggested Citation

  • Vijay S. Bilolikar & Karuna Jain & Mahesh Sharma, 2016. "An adaptive crossover genetic algorithm with simulated annealing for multi mode resource constrained project scheduling with discounted cash flows," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 25(1), pages 28-46.
    • Handle: RePEc:ids:ijores:v:25:y:2016:i:1:p:28-46
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    Citations

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    Cited by:

    1. Abdollah Arasteh, 2020. "Considering Project Management Activities for Engineering Design Groups," SN Operations Research Forum, Springer, vol. 1(4), pages 1-29, December.
    2. Mohammad Rostami & Morteza Bagherpour, 2020. "A lagrangian relaxation algorithm for facility location of resource-constrained decentralized multi-project scheduling problems," Operational Research, Springer, vol. 20(2), pages 857-897, June.

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