This paper presents the setup and pressure calibration of an 800-ton multi-anvil apparatus installed at the Vrije Universiteit (Amsterdam, the Netherlands) to simulate pressure-temperature conditions in planetary interiors. This high-pressure device can expose cubic millimeter sized samples to near-hydrostatic pressures up to~10 GPa and temperatures exceeding 2100°C. The apparatus is part of the Distributed Planetary Simulation Facility (DPSF) of the EU Europlanet 2020 Research Infrastructure, and significantly extends the pressure-temperature range that is available through international access to this facility.

a b s t r a c tScientific evidence on the impact of small-scale living facilities (SSLFs) on quality of life of nursing home clients remains scarce. In this study a simulation model is developed to examine the performance of SSLFs, in terms of meeting the time preferences of their residents. We model scheduled care using historical data and unscheduled care using a Poisson-Gamma mixture model. The model is used to explore the impact of a change in demand characteristics, duration of care delivery, travel time, allocation flexibility, shifts, number of clients and allocation policy. The results show that to further improve the performance, the focus should lie on: (1) increasing the allocation flexibility of care workers and the number of clients per SSLF, and (2) time dependent staffing. Furthermore, this study shows that simulation is a useful tool for assessing and improving daily nursing home operations. The presented simulation model provides a basis for building a decision support tool for nursing home managers.

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This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17 th INFORMS Revenue Management and Pricing Section Conference on June 29-30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical performance was analyzed in simulated market environments. This allows consideration of market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition.

We consider price optimization under the finite-mixture logit model. This model assumes that customers belong to one of a number of customer segments, where each customer segment chooses according to a multinomial logit model with segment-specific parameters. We reformulate the corresponding price optimization problem and develop a novel characterization. Leveraging this new characterization, we construct an algorithm that obtains prices at which the revenue is guaranteed to be at least [Formula: see text] times the maximum attainable revenue for any prespecified [Formula: see text]. Existing global optimization methods require exponential time in the number of products to obtain such a result, which practically means that the prices of only a handful of products can be optimized. The running time of our algorithm, however, is exponential in the number of customer segments and only polynomial in the number of products. This is of great practical value, because in applications, the number of products can be very large, whereas it has been found in various contexts that a low number of segments is sufficient to capture customer heterogeneity appropriately. The results of our numerical study show that (i) ignoring customer segmentation can be detrimental for the obtained revenue, (ii) heuristics for optimization can get stuck in local optima, and (iii) our algorithm runs fast on a broad range of problem instances. This paper was accepted by Omar Besbes, revenue management and market analytics.