Logistic sequencing for improving environmental performance using ant colony optimization

A significant portion of air pollutions in a city comes from road transport. Shorter travelling distance and less fuel consumption would logically lead to lower emissions of greenhouse gases or particulate matters, thus relieve environmental burdens. In this regard, an appropriate selection of the logistic sequence may contribute significantly to the environment. The logistic sequence for pickup and delivery services are often determined based on decision makers' experience and intuitive judgements. While life cycle assessment (LCA), a well-versed approach, can be used for quantifying the environmental loads, it is often regarded as not suitable for making routine decisions because it takes significant time and resources for data collection as well as expert knowledge for result interpretation. Additionally, the results of LCA studies focus mainly on the environmental perspective and that other decision criteria cannot be taken into account in a single evaluation process. This paper attempts to develop a practical and objective tool, by combining a simplified LCA with the ant colony optimization algorithm, that supports evaluating several decision criteria simultaneously and determining the optimal or near optimal sequence for vehicle routing on pickup and delivery activities. This fit-for-purpose approach enables decision makers to pay attention to environmental impacts during the determination of the travelling sequences. The proposed approach has been successfully performed to identify the optimal solution through benchmarking against other possible sequences, with the aim to reducing environmental impact while balancing other decision criteria.