This thesis is about how to represent and solve decision problems in Bayesian decision theory (e.g. Fishburn 1988). A general representation named decision networks is proposed based on influence diagrams (Howard and Matheson 1984). This new representation incorporates the idea, from Markov decision processes (e.g. Puterman 1990, Denardo 1982), that a decision may be conditionally independent o...

Drone delivery is a fast and innovative method for delivering parcels, food, medical supplies. Furthermore, this low-contact mode contributes to reducing the spread of pandemic vaccine-preventable diseases. Focusing on supplies, paper studies optimizing distribution operations at drone hub that dispatches drones hospitals located different geographic locations. Each hospital generates stochasti...

The paper studies effective approximate solutions to combinatorial counting and uniform generation problems. Using a technique based on the simulation of ergodic Markov chains, it is shown that, for self-reducible structures, almost uniform generation is possible in polynomial time provided only that randomised approximate counting to within some arbitrary polynomial factor is possible in polyn...

Inspired by the path coordination problem arising from robo-taxis, warehouse management, and mixed-vehicle routing, we model a group of heterogeneous players responding to stochastic demands as congestion game under Markov decision process dynamics. Players share common state-action space but have unique transition dynamics, each player’s cost is <italic xmlns:mml="http://www.w3.org/1998/Math/M...