Martijn R. K. Mes

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We propose a sequential sampling policy for noisy discrete global optimization and ranking and selection, in which we aim to efficiently explore a finite set of alternatives before selecting an alternative as best when exploration stops. Each alternative may be characterized by a multi-dimensional vector of categorical and numerical attributes and has(More)
Tactical planning in hospitals involves elective patient admission planning and the allocation of hospital resource capacities. We propose a method to develop a tactical resource allocation and patient admission plan that takes stochastic elements into consideration, thereby providing robust plans. Our method is developed in an Approximate Dynamic(More)
The block time (BT) schedule, which allocates Operating Rooms (ORs) to surgical specialties, causes inflexibility for scheduling outside the BT, which negatively affects new surgeons, new specialties, and specialties that have fluctuation in the number of surgeries. For this inflexibility, we introduce the concept of releasing ORs, and present a generic(More)
Approximate dynamic programming (ADP) is a general methodological framework for multistage stochastic optimization problems in transportation, finance, energy, and other applications where scarce resources must be allocated optimally. We propose a new approach to the exploration/exploitation dilemma in ADP. First, we show how a Bayesian belief structure can(More)
We have developed a decision support application for the Dutch Aviation Police and Air Support unit for routing their helicopters in anticipation of unknown future incidents. These incidents are not known in advance, yet do require a swift response. A response might include the dispatch of a police helicopter to support the police on the ground. If a(More)
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