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

The increase of greenhouse gases emission, global warming, and even climate change is an ongoing issue. Sustainable logistics distribution management can help reduce emission lighten its influence against our living environment. Quantum computing has become more popular in recent years for advancing artificial intelligence into the next generation. Hence, we apply quantum random number generator to provide true numbers genetic algorithm solve pollution-routing problems (PRPs) sustainable this paper. objective PRPs minimize carbon dioxide emissions, following one seventeen development goals set by United Nations. We developed a two-phase hybrid model combining modified k-means as clustering method with optimization engine aiming pollution produced trucks traveling along delivery routes. also compared computation performance another using different engine, i.e., tabu search algorithm. From experimental results, found that both models good solution quality CO2 minimization 29 out total 30 instances (30 runs each all problems).