Applied Mathematics & Information Sciences

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A global trend in adopting projects as production functions is prevalent in many industries. Project management professionals have become an important domain and many operational research models were developed for supporting project planning work such as project scheduling and budgeting. In practices, however, frequent work disruptions in the course of project execution can significantly induce waste in resources and relevant costs. The situation becomes one of the most difficult for production economics and few models have been specifically developed to support project cost management under disrupted work conditions. To encompass green and commercial objectives, this research proposes a genetic algorithm model to optimize resource allocation and project schedule for the lowest cost during the project execution stage. Furthermore, the proposed model enables the dynamic impact analysis of work delays on project cost and completion time, which can contribute significant cost savings. A case study demonstrates the applicability of the proposed model, which enables project managers to handle work disruptions by developing an after-impact schedule with optimal resource reallocation in a timely manner. With the support of the proposed model, resource waste can be reduced and in the demonstrative case there was a realistic extra savings for total project cost.

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