POLIBITS, Vol. 62, pp. 85-91, 2020.
Abstract: Last-mile is the component of the supply chains that has the most potential to be optimized and give advantage to retailers and delivery companies. At the same time, it is the hardest to deal with. Factors such as traffic, weather, unexpected events, or the simple fact that a customer is not at home affect directly the efficiency of the overall shipping process. This work-in-progress article proposes a framework for the improvement of the first-try delivery by studying traffic on the streets and past delivery success as a way of approximating customers’ presence at home. In contrast to existing solutions, it is proposed to work only with data that does not compromise the customers’ privacy and to get insights about traffic features in cities without the need of deploying expensive equipment to obtain data. The main goal is to provide a route plan to the delivery team and route planners, which allows finishing the distribution of the parcels in the least amount of time, while being able to effectively deliver the highest amount of them. This will be translated into less resource consumption and increased customer satisfaction. The research work is conducted following the principles of design science research for information systems. The implementation will use methods of computational intelligence to address the lack of precise information, following a transdisciplinary approach as industrial partners support the development of this study.
Keywords: Smart logistics, last-mile delivery, swarm intelligence, fuzzy logic
PDF: Towards a Computational Intelligence Framework to Smartify the Last-Mile Delivery
PDF: Towards a Computational Intelligence Framework to Smartify the Last-Mile Delivery
https://doi.org/10.17562/PB-62-10
See table of contents of POLIBITS 62.