Transactions on Data Analysis in Social Science

Transactions on Data Analysis in Social Science

The Use of Meta-Heuristic Methods to Solve Resource-Constrained Project Scheduling and Different Administrative Situations and Allowance to Cut Activities with Cut Costs

Document Type : Original Article

Authors
A. Asgari 1
B. Afshar Nadjafi 2
1 Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University
2 1Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University
10.47176/TDASS/2021.46
Abstract
This research presents a comprehensive modeling approach for the project scheduling problem, incorporating cut allowances and multiple administrative methods for each activity while accounting for earliness and tardiness costs. The model aims to optimize project timelines and minimize total cost by balancing the trade-offs between early and late task completion. To solve this complex scheduling problem, a genetic algorithm (GA) was developed and implemented. The performance and effectiveness of the proposed GA were evaluated through a series of computational experiments. For small-sized problems, results were compared against exact solutions obtained using LINGO software, demonstrating the algorithm’s accuracy. For larger-scale problems, evaluation indicators such as solution quality and computational efficiency were employed to assess the GA’s performance. The results indicate that the proposed algorithm consistently produces high-quality solutions within reasonable computational times, confirming its capability to handle both small and large problem instances effectively. Overall, this study provides a robust and efficient algorithmic framework for addressing complex project scheduling problems with multiple administrative options and earliness/tardiness cost considerations, offering practical guidance for project managers aiming to optimize project execution and resource allocation.
Keywords
Project Scheduling
Multiple Administrative Methods
Cut Costs
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Transactions on Data Analysis in Social Science
Volume 3, Issue 1
2021
Pages 46-52

  • Receive Date 25 January 2021
  • Revise Date 03 March 2021
  • Accept Date 26 March 2021