It is well known that engineering our way out of traffic congestion is more complicated than just building new roads. In fact, expanding the road network may attract new traffic, and increase gridlock. Using tools from algorithmic game theory and mathematical optimization, we analyze how much fuel and time might be saved by rerouting vehicle flow on existing road networks, and by building new roads or actually closing streets.
Bryndís Stefánsdóttir: "Classifying and modeling setups and cleanings in lot sizing and scheduling: A case study on cheese production"
Much attention in the lot sizing and scheduling literature has been focused on reducing the number and size of setups. Cleanings, in contrast, remain a key cost driver in large parts of the process industries. In this study, we first develop a general classification scheme for setups and cleanings. Secondly, we develop a generic MILP model for lot sizing and scheduling in the typical process-industry setting of flowshops, accurately representing different classes of setups and cleanings. Thirdly, we apply the model to the case of cheese production in no-wait flowshops, demonstrating the adaptability of the generic model to industry-specific settings as well as the computational efficiency of the approach.
Venue: TUM Campus Garching, Room 01.10.033
Date: Monday, January 22th, 2018, 14:00