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Multi-skilled project scheduling with level-dependent rework risk; three multi-objective mechanisms based on cuckoo search

Maghsoudlou, H ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1016/j.asoc.2017.01.024
  3. Publisher: Elsevier Ltd , 2017
  4. Abstract:
  5. In many projects, multi-skilled workforces are able to perform different tasks with different quality levels. In this paper, a real-life version of the multi-skilled resource constrained project scheduling problem is investigated, in which the reworking risk of each activity depends on the assigned level of multi-skilled workforces. The problem is formulated mathematically as a bi-objective optimization model to minimize total costs of processing the activities and to minimize reworking risks of the activities, concurrently. In order to solve the resulting problem, three cuckoo-search-based multi-objective mechanisms are developed based on non-dominance sorting genetic algorithm, multi-objective particle swarm and multi-objective invasive weeds optimization algorithm. The parameters of the algorithms are tuned using the Taguchi method to improve the efficiency of the solution procedures. Furthermore, a competitive multi-objective invasive weeds optimization algorithm is used to evaluate the performance of the proposed methodologies. Finally, a priority based method is employed to compare the proposed algorithms in terms of different metrics. © 2017 Elsevier B.V
  6. Keywords:
  7. Cuckoo search ; Multi-objective ; Multi-skilled ; Project scheduling ; Rework ; Genetic algorithms ; Multiobjective optimization ; Problem solving ; Scheduling ; Screening ; Taguchi methods ; Cuckoo searches ; Multi objective ; Optimization
  8. Source: Applied Soft Computing Journal ; Volume 54 , 2017 , Pages 46-61 ; 15684946 (ISSN)
  9. URL: https://linkinghub.elsevier.com/retrieve/pii/S156849461730039X