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Integrated resource leveling and cash flow optimization for construction projects using symbiotic organisms search algorithm

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2020
Seyisoğlu, Başak
Construction projects are well-known for their inherent complexity as they involve a considerable amount of challenging tasks to be performed in conformance with the contractual documents. Unrealistic schedules that disregard the prominent components such as resources are one of the major reasons for failure of the construction projects. The most widely used scheduling technique is the Critical Path Method (CPM) which usually provides a schedule with unfavorable resource fluctuations. To reduce the negative effects of variations in resource demand, resource leveling methods are usually applied. Moreover, the primary cause of contractors’ failure is identified as poor financial management. Cash flow related problems can be overcome by virtue of proper cash flow analysis which secures the contractor’s cash flow. To deal with issues appertaining to each constituent part of a construction project, a thorough project schedule, including resources and cash flow analysis, should be readily available prior to project execution. Despite a multitude of endeavors in the literature, preparation of a comprehensive project schedule under consideration of different goals is hard to achieve due to computational expensiveness. In this study, it is suggested that a practical approach is needed for industry practitioners who suffer from lack of an optimization model that integrates resource leveling and cash flow optimization in project scheduling. For this purpose, this thesis introduces a new integrated optimization method, named “Combined Resource and Cash flow (CRC)” that considers resource leveling and cash flow optimization simultaneously. The application of proposed integrated method is demonstrated using an example project from the literature. The solutions are obtained with the implementation of Symbiotic Organisms Search (SOS) algorithm. The solutions revealed that the use of CRC method produces promising results compared to the existing methods.