Sustainable Green City Development Project Analysis using the Critical Path Method (CPM) and the Crashing Project Method on Time and Cost Optimization

Authors

  • Khaled Abuhasel Mechanical Engineering Department, University of Bisha, Kingdom of Saudi Arabia
Volume: 13 | Issue: 3 | Pages: 10973-10977 | June 2023 | https://doi.org/10.48084/etasr.5980

Abstract

Due to the increase in population all over the world, the term city development has emerged using the method of sustainable green cities, which are cities designed taking into account the environmental impact to reduce the required input from energy, water, and food production. This study discusses this topic with three main objectives; the first is to analyze the project work network of the Sustainable Green City Development Project, the second is to investigate the impact of speeding up the project on network planning, and the third is to determine the cost differences before and after acceleration. The Critical Path Method (CPM) is valuable for managing complex projects if the activity durations are known. However, in reality, the projected durations may vary in the course of a project due to multiple factors, such as equipment breakdowns, human errors, and material shortages. This study used the CPM and crashing methods to determine the optimal project turnaround time using critical path optimization techniques. Analysis using CPM showed that the initial project duration was 1205 weeks but can be reduced to 1197 weeks. However, with acceleration, there is a slight difference, corresponding to a 0.67% decrease in the duration and approximately 8 weeks. The study showed that the total cost of the project work is US $55M at the normal duration and became US $59M after acceleration, which is a slight increase.

Keywords:

Critical Path Method (CPM), crashing method, network planning, optimal duration, optimal cost

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[1]
Abuhasel, K. 2023. Sustainable Green City Development Project Analysis using the Critical Path Method (CPM) and the Crashing Project Method on Time and Cost Optimization. Engineering, Technology & Applied Science Research. 13, 3 (Jun. 2023), 10973–10977. DOI:https://doi.org/10.48084/etasr.5980.

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