A Genetic Algorithm with Quantum Random Number Generator for Solving the Pollution-Routing Problem in Sustainable Logistics Management
| Autor: | Shih-Che Lo, Yi-Cheng Shih |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Computer science Geography Planning and Development 0211 other engineering and technologies TJ807-830 02 engineering and technology Management Monitoring Policy and Law TD194-195 Renewable energy sources quantum computing sustainable logistic Genetic algorithm 0202 electrical engineering electronic engineering information engineering genetic algorithm GE1-350 Hardware random number generator Cluster analysis Quantum computer Sustainable development 021103 operations research Environmental effects of industries and plants Renewable Energy Sustainability and the Environment Tabu search Environmental sciences Greenhouse gas pollution-routing problem 020201 artificial intelligence & image processing Minification |
| Zdroj: | Sustainability Volume 13 Issue 15 Sustainability, Vol 13, Iss 8381, p 8381 (2021) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su13158381 |
| Popis: | 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 CO2 emission minimization for 29 PRPs out of a total of 30 instances (30 runs each for all problems). |
| Databáze: | OpenAIRE |
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