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 CO2 emission minimization for 29 PRPs out of a total of 30 instances (30 runs each for all problems).
Highlights
Global warming and climate change are ongoing issues and required immediate attention around the world (Figure 1)
The parameters for the adjustment of the k-means clustering with genetic algorithm with QRNG (kGAQ) and the kTS were determined through the Design of Experiments
We found that the kGAQ can produce a fair solution quality for both small-scale and large-scale problems
Summary
Global warming and climate change are ongoing issues and required immediate attention around the world (Figure 1). Climate change and global warming are related to the emission of greenhouse gases into the atmosphere, theoretically. One of the important observations during the global pandemic of the new coronavirus (COVID-19) is that many regions have been on lockdown for several months. The aerosols and pollutants in the atmosphere reduced by around 9–64% [1]. It is obvious that human activities have a significant influence on the amount of pollutants in the atmosphere. Methods for reducing greenhouse gases emission in daily life and industries has become an important issue. Inefficient logistics and distribution management will produce more greenhouse gases emission, since trucks need to travel for a longer time and consume more fuel
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