Session 11: Logistics Operations with Real-Time Data – Chair: Paul Grefen
Remco Dijkman (Eindhoven University of Technology) – Benefits and challenges of using real-time data in optimizing operations
Planning and optimization algorithms typically use historical averages, estimates or rules of thumb as parameters in their optimization. For example, in truck route planning an average speed of 65 km/h is assumed for travel times and unloading times are computed as 15 minutes per location + 5 minutes per pallet. We know these averages are not precise, that we can compute more precise distributions ourselves, and that companies like TomTom and Google can provide us with accurate real-time and historical information on travel times. However, in order to keep our optimization and planning problems manageable, we use the less precise averages anyway. In this presentation, we provide some insights from a number of case studies into the averages, estimates and rules of thumb that are used in practice, in particular in the transportation sector, with some surprising results: more detailed estimates are not always better. We also provide a research agenda that defines the challenges that must be overcome to link the research domain of real-time data aggregation and the research domain of planning and optimization.
Sander Peters (Eindhoven University of Technology) – Prescriptive resource allocation: improving processes by data driven analytics to optimize resource usage
Resource allocation in business process management is of increasingly importance. In order to make optimal resource allocations, predictions about the business process need to be generated. Analytical prediction of the performance of increasingly complex process models is a problem in business process management practice. Simulation of business processes provides an initial step into the generation of predictions on business processes to develop prescriptive resource allocation models. Prescriptive resource allocation can improve the resource usage and performance of the business process.
Andrej Dobrkovic (University of Twente) – Enhancing synchromodal scheduling processes through intelligence amplification
The scheduling process in a typical business environment consists of predominantly repetitive tasks that have to be completed in limited time and often containing some form of uncertainty. The intelligence amplification is a symbiotic relationship between a human and an intelligent agent. This partnership is organized to emphasize the strength of both entities, with the human taking the central role of the objective setter and supervisor, and the machine focusing on executing the repetitive tasks. In this presentation we discuss how to augment the capabilities of typical decision maker with intelligence amplification. We use a serious game to demonstrate how human and machine can complement each other in the synchromodal logistics environment. Focusing on the scheduling tasks, we show how can the intelligent agents improve planer’s awareness, relieve the burden of doing repetitive tasks, and provide assistance to handle exceptions. Finally, we discuss the potential of intelligence amplification to improve the efficiency and effectiveness of the scheduling process.