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A Genetic Algorithm with Quantum Random Number Generator for Solving the Pollution-Routing Problem in Sustainable Logistics Management

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  • Shih-Che Lo

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan)

  • Yi-Cheng Shih

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan)

Abstract

The increase of greenhouse gases emission, global warming, and even climate change is an ongoing issue. Sustainable logistics and distribution management can help reduce greenhouse gases emission and lighten its influence against our living environment. Quantum computing has become more and more popular in recent years for advancing artificial intelligence into the next generation. Hence, we apply quantum random number generator to provide true random numbers for the genetic algorithm to solve the pollution-routing problems (PRPs) in sustainable logistics management in this paper. The objective of the PRPs is to minimize carbon dioxide emissions, following one of the seventeen sustainable development goals set by the United Nations. We developed a two-phase hybrid model combining a modified k -means algorithm as a clustering method and a genetic algorithm with quantum random number generator as an optimization engine to solve the PRPs aiming to minimize the pollution produced by trucks traveling along delivery routes. We also compared the computation performance with another hybrid model by using a different optimization engine, i.e., the tabu search algorithm. From the experimental results, we found that both hybrid models can provide good solution quality for CO 2 emission minimization for 29 PRPs out of a total of 30 instances (30 runs each for all problems).

Suggested Citation

  • Shih-Che Lo & Yi-Cheng Shih, 2021. "A Genetic Algorithm with Quantum Random Number Generator for Solving the Pollution-Routing Problem in Sustainable Logistics Management," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8381-:d:602661
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    References listed on IDEAS

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    Cited by:

    1. Shih-Che Lo, 2022. "A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions Reduction in Logistics Management," Logistics, MDPI, vol. 6(3), pages 1-15, September.
    2. Shih-Che Lo & Ying-Lin Chuang, 2023. "Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management," Mathematics, MDPI, vol. 11(4), pages 1-19, February.

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