The aim of route optimization system (ROS) is to design a set of vehicle routes to fulfill transportation demands, in an attempt to minimize cost and/or other negative social and environmental impacts. ROS, established based on the fruitful studies of vehicle routing problem (VRP), has been applied in various industries and forms. During daily operations, dynamic traffic conditions, varying restriction policies, road constructions, drivers’ progressing familiarity with the routes and destinations are all common factors affecting the performance of ROS. However, most current systems are designed in a one-way and open-loop manner, i.e. these systems do not track how the planned vehicle routes are performed, which hinders the continuous improvement of the system and would lead to the failure of the system. This study proposes a smart product-service system (SPSS) approach to design an IoT-based ROS, arguing that the product (i.e. the ROS) and services (updating base data and learning users’ behaviors automatically to optimize the system) should be designed as a bundle. For this end, IoT devices are employed to acquire real-time information and feedbacks of vehicles and drivers, which are used to assess the execution of planned routes and dynamically modify the base data. Moreover, the driving records from IoT devices reveal drivers’ improving familiarity with routes and destinations, which will be considered to optimize the assignment of routes to drivers. Finally, we use a case of retailing industry to show the advantages of the proposed SPSS approach.
- City logistics
- IoT (Internet of things)
- ROS (route optimization system)
- SPSS (smart product-service system)
ASJC Scopus subject areas
- Information Systems
- Artificial Intelligence