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A New Metaheuristic Algorithm Based on Particle Swarm Optimization for Discrete Time Resource Trade-off Problem

Esfandeh, Tolou | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 40331 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Kianfar, Farhad
  7. Abstract:
  8. In this research, a new metaheuristic algorithm is developed for solving the Discrete Time- Resource Trade off Problem in the field of project scheduling.In this problem ,a project contains activities interrelated by finish-start type precedence constraints and each has a specified work content and can be performed in different combinations of duration and resource requirement.Since the problem is NP-hard , the Particle Swarm Optimization is adopted due to minimization of the makespan subject to precedence relations and a single renewable resource. Basically PSO is used to solve continous problems and discrete problems have just begun to be solved by the discrete PSO.In proposed method,a continous form is applied in which a priority-based representation is combined to a new method as solution represention .Then the particles are converted to the selected mode of each activity and their feasible sequence.A Serial Scheduling Generator Scheme is also applied to scheduling.In the standard framework of PSO,the Time-Variant coefficients are used due to make an efficient balance between the global and local search and to avoid from premature convergence in the first stages of algorithm.A new improvement factor is defiend to help the process to move toward the mose improved particles and to scape from local optima.For more efficient exploration in the problem space,a local search is also combined with the method .The results obtained using a standard set of DTRTP instances,after extensive experiments,prove that the proposed method is very competitive in terms of average percent deviation found from best known solutions.TV-PSO, outperforms the Tabu Search in all of the cases and it is close to GA results
  9. Keywords:
  10. Project Scheduling ; Genetic Local Search ; Particles Swarm Optimization (PSO) ; Discrete Time Resource Trade off Problem

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