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Published on: December 9, 2012
Evolutionary algorithm based approach for solving transportation problems in normal and pandemic scenario.
Amiya Biswas1, Sankar Kumar Roy2, Sankar Prasad Mondal3
1Department of Mathematics, Durgapur Government College, Durgapur 713214, India.
This study presents a modified Genetic Algorithm to optimize transportation logistics during COVID-19. The approach minimizes costs and vehicle trips while adhering to pandemic-related travel restrictions.
Area of Science:
- Operations Research
- Transportation Logistics
- Pandemic Management
Background:
- COVID-19 pandemic imposed significant challenges on transportation companies due to varying international restrictions.
- Regional categorization based on infection rates, deaths, and population influenced vehicle movement and trip frequency.
Purpose of the Study:
- To formulate and solve a fixed-charge transportation problem (FCTP) under pandemic conditions.
- To achieve a transportation scheme minimizing cost and vehicle trips, especially for high-restriction regions.
Main Methods:
- A penalty was incorporated into the objective function based on region restriction levels.
- A constraint was added to manage and limit transportation costs.
- A modified Genetic Algorithm (GA) with new crossover and mutation operators was developed to handle multi-trip and capacity constraints.
Main Results:
- The modified GA effectively solved FCTP problems with pandemic-specific constraints.
- Numerical illustrations demonstrated the effectiveness of the cost-limiting constraint.
- Comparative analysis confirmed the algorithm's superiority over existing methods.
Conclusions:
- The developed algorithm provides an effective solution for optimizing transportation logistics during pandemics.
- The study highlights the importance of adaptive models to address real-world logistical challenges posed by global health crises.

