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Evolutionary algorithm based approach for solving transportation problems in normal and pandemic scenario.

Amiya Biswas1, Sankar Kumar Roy2, Sankar Prasad Mondal3

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Summary
This summary is machine-generated.

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.

Keywords:
COVID-19 Pandemic scenarioFixed-chargeGenetic algorithmMultiple vehiclesTransportation Problem

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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.