R.H. Bemthuis MSc (Rob)

Big Data for Resilient Logistics

DataRel

The modern supply chain continues to seek more cost savings and greater transparency and efficiency in all processes across the whole chain. The Internet of Things (IoT) is fundamentally transforming the transport industry and logistics. Next- generation smart logistics will optimize the movement of people and goods locally and globally, improving economics, public safety, and sustainability. This requires a multi-tiered intelligent system architecture providing high degree of modularity and autonomy, satisfying demanding and sometimes conflicting requirements of smart logistics and transport in terms of scalability, availability, and security. The wide availability of sensors (e.g., smart packaging, sensing technology, temperature control, board computers, GPS trackers, etc.) in logistics supply chains and the wide-band communication of Internet of Things (IoT) allow the collection of Big Data from sensors placed at key points in the logistic supply chain. This project is a collaboration between several companies and universities, and partially funded by NWO. Within this project we are focusing on the following areas: - Logistics Internet of Things (Pervasive Systems Group) This PhD research will focus on a new approach to logistic processes involving cyber-physical networks of logistic entities (e.g., goods, vehicles, infrastructure) that embed smart sensors and business logic. These entities, called Smart Returnable Transport Items (SRTIs), are capable of collecting data from extant IoT devices and social networks. The main goal is to detect unexpected behavior and do adaptive sampling of data generated by collaborating SRTIs in the region of interest, in order to solve problems such as, lost and damaged perishable goods during transportation, waste reduction, handling, and storage throughout the lifecycle of products based on sensing and location capabilities, etc. - IoT-driven resilient multimodal planning in smart logistics (Industrial Engineering and Business Information Systems Group) This PhD research will focus on a new approach to use this data input provided by cyber-physical networks of logistic entities (e.g., goods, vehicles, infrastructure) that embed IoT devices in the real-time multi-modal planning of orders. More concretely, the main goal is to improve the resilience and performance of planning decisions in terms of sustainability, efficiency, queue delay, costs and quality, by developing Artificial Logistics Intelligence (ALI) capable of detecting and dealing with emergent phenomena (given large volumes of heterogeneous data) and capable of learning from the experience of human planners using machine learning.