Facts about the course
- Study points:
- Responsible department:
- Faculty of Logistics
- Course Leader:
- Homayoun Shaabani
- Lecture Semester:
- Teaching language:
- ½ year
LOG904-127 Last Mile Delivery: Data Analytics and Models (Autumn 2019)
About the course
Course content (abstract): Last mile delivery, the final component of the supply chain, has the mission of achieving the ultimate objective of delivering goods to customers. It constitutes one of the more complex stages of the whole chain, and accounts for a significant fraction of its total cost. Its complexity is a consequence of the specific conditions of the scenarios in which it is done, the urban environments, the factors determining how it is done, e.g. the ad hoc interaction between the implied stakeholders, shippers, carriers, administration and residents, and the peculiarities of the demand and its current trends resulting from the growing of e-commerce.
An efficient management of the last mile delivery should be based on a suitable acquisition of information combined with appropriate models. From the modeling perspective, there is a substantial difference between the conventional location and routing models for delivery services and the street routing and multi echelon models supporting last mile delivery. The information to support decisions comes from the efficient processing of the available data. These processes have evolved recently due to the pervasive penetration of Information and Communication Technologies (ICT) based on mobile devices. Data collection procedures enable gathering huge amount of data that can be analyzed using advanced data analytics techniques to estimate and forecast demand, tacking vehicles and analyzing tracked routes for improved routing models, support real-time services and fleet management among other applications.
The objective of the seminar is to introduce to students the specificities of last mile delivery systems and the appropriate models to deal with them. The technologies, the data that they can supply, the data collection, and the data analytics techniques to process them and generate the required inputs to the decision support models.
Topics covered: Last mile delivery systems, specificities, organizational and operational characteristics. Last mile delivery models: street routing, time-dependent routing, multi-echelon location-routing. ICT applications, data availability and data collection. Extracting information from data to support decisions, applications of principal component analysis, regression, forecasting to last mile demand estimation and forecasting, to travel time analysis, and so on.
The course is connected to the following study programs
- Master of Science in Logistics
- Experience-based Master in Logistics
- Master of Science in Petroleum Logistics
- Exchange programme - Master's level
Vehicle Routing, Mathematical Modelling in Logistics, Integer Optimization Models in Logistics, Applied Statistics, Data Mining
The student's learning outcomes after completing the course
Students will learn what makes a last mile delivery system different from other logistics applications, which are the suitable decision support models to address them, how ICT can be used to collect the required data and which are the appropriate data analytics techniques to extract the suitable information from the data. They will also learn how to apply in practice the acquired skills.
Forms of teaching and learning
learning will be done by combining the lectures with the discussion of use cases. During the course, the student will have to develop a mini project, with the flavor of a real case, covering the major topics.
Form of assessment: Home assessment without presentation
Grading scale: Letter (A - F)
Support material: All printed and written supporting material