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We investigated the nature of the anomalies appearing in four microlensing events KMT-2020-BLG-0757, KMT-2022-BLG-0732, KMT-2022-BLG-1787, and KMT-2022-BLG-1852. The light curves of these events commonly exhibit initial bumps followed by subsequent troughs that extend across a substantial portion of the light curves. We performed thorough modeling of the anomalies to elucidate their characteristics. Despite their prolonged durations, which differ from the usual brief anomalies observed in typical planetary events, our analysis revealed that each anomaly in these events originated from a planetary companion located within the Einstein ring of the primary star. It was found that the initial bump arouse when the source star crossed one of the planetary caustics, while the subsequent trough feature occurred as the source traversed the region of minor image perturbations lying between the pair of planetary caustics. The estimated masses of the host and planet, their mass ratios, and the distance to the discovered planetary systems are $(M_{\rm host}/M_\odot, M_{\rm planet}/M_{\rm J}, q/10^{-3}, \dl/{\rm kpc}) = (0.58^{+0.33}_{-0.30}, 10.71^{+6.17}_{-5.61}, 17.61\pm 2.25,6.67^{+0.93}_{-1.30})$ for KMT-2020-BLG-0757, $(0.53^{+0.31}_{-0.31}, 1.12^{+0.65}_{-0.65}, 2.01 \pm 0.07, 6.66^{+1.19}_{-1.84})$ for KMT-2022-BLG-0732, $(0.42^{+0.32}_{-0.23}, 6.64^{+4.98}_{-3.64}, 15.07\pm 0.86, 7.55^{+0.89}_{-1.30})$ for KMT-2022-BLG-1787, and $(0.32^{+0.34}_{-0.19}, 4.98^{+5.42}_{-2.94}, 8.74\pm 0.49, 6.27^{+0.90}_{-1.15})$ for KMT-2022-BLG-1852. These parameters indicate that all the planets are giants with masses exceeding the mass of Jupiter in our solar system and the hosts are low-mass stars with masses substantially less massive than the Sun., Comment: 10 pages, 12 figures, 7 tables

In this paper the recoverable robust shortest path problem is investigated. Discrete budgeted interval uncertainty representation is used to model uncertain second-stage arc costs. The known complexity results for this problem are strengthened. It is shown that it is Sigma_3^p-hard for the arc exclusion and the arc symmetric difference neighborhoods. Furthermore, it is also proven that the inner adversarial problem for these neighborhoods is Pi_2^p-hard.

Context. Gaia18cjb is one of the Gaia-alerted eruptive young star candidates which has been experiencing a slow and strong brightening during the last 13 years, similar to some FU Orionis-type objects. Aims. The aim of this work is to derive the young stellar nature of Gaia18cjb, determine its physical and accretion properties to classify its variability. Methods. We conducted monitoring observations using multi-filter optical and near-infrared photometry, as well as near-infrared spectroscopy. We present the analysis of pre-outburst and outburst optical and infrared light curves, color-magnitude diagrams in different bands, the detection of near-IR spectral lines, and estimates of both stellar and accretion parameters during the burst. Results. The optical light curve shows an unusually long (8 years) brightening event of 5 mag in the last 13 years, before reaching a plateau indicating that the burst is still on-going, suggesting a FUor-like nature. The same outburst is less strong in the infrared light curves. The near-infrared spectra, obtained during the outburst, exhibit emission lines typical of highly accreting low-intermediate mass young stars with typical EXor features. The spectral index of Gaia18cjb SED classifies it as a Class I in the pre-burst stage and a Flat Spectrum young stellar object (YSO) during the burst. Conclusions. Gaia18cjb is an eruptive YSO which shows FUor-like photometric features (in terms of brightening amplitude and length of the burst) and EXor-like spectroscopic features and accretion rate, as V350 Cep and V1647 Ori, classified as objects in between FUors and EXors, Comment: 21 pages, 13 figures

In this paper, the recoverable robust shortest path problem under interval uncertainty representations is discussed. This problem is known to be strongly NP-hard and also hard to approximate in general digraphs. In this paper, the class of acyclic digraphs is considered. It is shown that for the traditional interval uncertainty, the problem can be solved in polynomial time for all natural, known from the literature, neighborhoods. Efficient algorithms for various classes of acyclic digraphs are constructed. Some negative results for general digraphs are strengthened. Finally, some exact and approximate methods of solving the problem under budgeted interval uncertainty are proposed.

This paper discusses a class of combinatorial optimization problems with uncertain costs in the objective function. It is assumed that a sample of the cost realizations is available, which defines an empirical probability distribution for the random cost vector. A Wasserstein ball, centered at the empirical distribution, is used to define an ambiguity set of probability distributions. A solution minimizing the Conditional Value at Risk for a worst probability distribution in the Wasserstein ball is computed. The complexity of the problem is investigated. Exact and approximate solution methods for various support sets are proposed. Some known results for the Wasserstein robust shortest path problem are generalized and refined.

This paper deals with the recoverable robust shortest path problem under the interval uncertainty representation. The problem is known to be strongly NP-hard and not approximable in general digraphs. Polynomial time algorithms for the problem under consideration in DAGs are proposed.

In decision-making under uncertainty, several criteria have been studied to aggregate the performance of a solution over multiple possible scenarios. This paper introduces a novel variant of ordered weighted averaging (OWA) for optimization problems. It generalizes the classic OWA approach, which includes robust min-max optimization as a special case, as well as min-max regret optimization. We derive new complexity results for this setting, including insights into the inapproximability and approximability of this problem. In particular, we provide stronger positive approximation results that asymptotically improve the previously best-known bounds for the classic OWA approach. In computational experiments, we evaluate the quality of the proposed methods and compare the proposed setting with classic OWA and min-max regret approaches.

FU Orionis objects (FUors) are eruptive young stars, which exhibit outbursts that last from decades to a century. Due to the duration of their outbursts, and to the fact that only about two dozens of such sources are known, information on the end of their outbursts is limited. Here we analyse follow-up photometry and spectroscopy of Gaia21elv, a young stellar object, which had a several decades long outburst. It was reported as a Gaia science alert due to its recent fading by more than a magnitude. To study the fading of the source and look for signatures characteristic of FUors, we have obtained follow-up near infrared (NIR) spectra using Gemini South/IGRINS, and both optical and NIR spectra using VLT/X-SHOOTER. The spectra at both epochs show typical FUor signatures, such as a triangular shaped $H$-band continuum, absorption-line dominated spectrum, and P Cygni profiles. In addition to the typical FUor signatures, [OI], [FeII], and [SII] were detected, suggesting the presence of a jet or disk wind. Fitting the spectral energy distributions with an accretion disc model suggests a decrease of the accretion rate between the brightest and faintest states. The rapid fading of the source in 2021 was most likely dominated by an increase of circumstellar extinction. The spectroscopy presented here confirms that Gaia21elv is a classical FUor, the third such object discovered among the Gaia science alerts., Comment: Accepted to MNRAS

In this paper, an optimization problem with uncertain objective function coefficients is considered. The uncertainty is specified by providing a discrete scenario set, containing possible realizations of the objective function coefficients. The concept of belief function in the traditional and possibilistic setting is applied to define a set of admissible probability distributions over the scenario set. The generalized Hurwicz criterion is then used to compute a solution. In this paper, the complexity of the resulting problem is explored. Some exact and approximation methods of solving it are proposed.

In this paper, an optimization problem with uncertain constraint coefficients is considered. Possibility theory is used to model the uncertainty. Namely, a joint possibility distribution in constraint coefficient realizations, called scenarios, is specified. This possibility distribution induces a necessity measure in scenario set, which in turn describes an ambiguity set of probability distributions in scenario set. The distributionally robust approach is then used to convert the imprecise constraints into deterministic equivalents. Namely, the left-hand side of an imprecise constraint is evaluated by using a risk measure with respect to the worst probability distribution that can occur. In this paper, the Conditional Value at Risk is used as the risk measure, which generalizes the strict robust and expected value approaches, commonly used in literature. A general framework for solving such a class of problems is described. Some cases which can be solved in polynomial time are identified.

Young stars show variability on different time-scales from hours to decades, with a range of amplitudes. We studied two young stars, which triggered the Gaia Science Alerts system due to brightenings on a time-scale of a year. Gaia20bwa brightened by about half a magnitude, whereas Gaia20fgx brightened by about two and half magnitudes. We analyzed the Gaia light curves, additional photometry, and spectra taken with the Telescopio Nazionale Galileo and the Gran Telescopio Canarias. Several emission lines were detected toward Gaia20bwa, including hydrogen lines from H$\alpha$ to H$\delta$, Pa$\beta$, Br$\gamma$, and lines of Ca II, O I, and Na I. The H$\alpha$ and Br$\gamma$ lines were detected toward Gaia20fgx in emission in its bright state, with additional CO lines in absorption, and the Pa$\beta$ line with an inverse P Cygni profile during its fading. Based on the Br$\gamma$ lines the accretion rate was $(2.4-3.1)\times10^{-8}$ $M_\odot$ yr$^{-1}$ for Gaia20bwa and $(4.5-6.6)\times10^{-8}$ $M_\odot$ yr$^{-1}$ for Gaia20fgx during their bright state. The accretion rate of Gaia20fgx dropped by almost a factor of 10 on a time-scale of half a year. The accretion parameters of both stars were found to be similar to those of classical T Tauri stars, lower than those of young eruptive stars. However, the amplitude and time-scale of these brightenings place these stars to a region of the parameter space, which is rarely populated by young stars. This suggests a new class of young stars, which produce outbursts on a time-scale similar to young eruptive stars, but with smaller amplitudes., Comment: Accepted to MNRAS

We present follow-up photometric and spectroscopic observations, and subsequent analysis of Gaia20eae. This source triggered photometric alerts during 2020 after showing a $\sim$3 mag increase in its brightness. Its Gaia Alert light curve showed the shape of a typical eruptive young star. We carried out observations to confirm Gaia20eae as an eruptive young star and classify it. Its pre-outburst spectral energy distribution shows that Gaia20eae is a moderately embedded Class II object with $L_\mathrm{bol} = 7.22$ L$_\odot$. The color-color and color-magnitude diagrams indicate that the evolution in the light curve is mostly gray. Multiple epochs of the H$\alpha$ line profile suggest an evolution of the accretion rate and winds. The near-infrared spectra display several emission lines, a feature typical of EXor-type eruptive young stars. We estimated the mass accretion rate during the dimming phase to be $\dot{M} = 3-8 \times 10^{-7}$ M$_\odot$ yr$^{-1}$, higher than typical T Tauri stars of similar mass and comparable to other EXors. We conclude Gaia20eae is a new EXor-type candidate., Comment: Accepted for publication in ApJ. 20 pages, 11 figures

In this paper a class of optimization problems with uncertain linear constraints is discussed. It is assumed that the constraint coefficients are random vectors whose probability distributions are only partially known. Possibility theory is used to model the imprecise probabilities. In one of the interpretations, a possibility distribution (a membership function of a fuzzy set) in the set of coefficient realizations induces a necessity measure, which in turn defines a family of probability distributions in this set. The distributionally robust approach is then used to transform the imprecise constraints into deterministic counterparts. Namely, the uncertain left-had side of each constraint is replaced with the expected value with respect to the worst probability distribution that can occur. It is shown how to represent the resulting problem by using linear or second order cone constraints. This leads to problems which are computationally tractable for a wide class of optimization models, in particular for linear programming.

This paper deals with a problem of production planning, which is a version of the capacitated single-item lot sizing problem with backordering under demand uncertainty, modeled by uncertain cumulative demands. The well-known interval budgeted uncertainty representation is assumed. Two of its variants are considered. The first one is the discrete budgeted uncertainty, in which at most a specified number of cumulative demands can deviate from their nominal values at the same time.The second variant is the continuous budgeted uncertainty, in which the sum of the deviations of cumulative demands from their nominal values, at the same time, is at most a bound on the total deviation provided. For both cases, in order to choose a production plan that hedges against the cumulative demand uncertainty, the robust minmax criterion is used. Polynomial algorithms for evaluating the impact of uncertainty in the demand on a given production plan in terms of its cost, called the adversarial problem, and for finding robust production plans under the discrete budgeted uncertainty are constructed. Hence, in this case, the problems under consideration are not much computationally harder than their deterministic counterparts. For the continuous budgeted uncertainty, it is shown that the adversarial problem and the problem of computing a robust production plan along with its worst-case cost are NP-hard. In the case, when uncertainty intervals are non-overlapping, they can be solved in pseudopolynomial time and admit fully polynomial timeapproximation schemes. In the general case, a decomposition algorithm for finding a robust plan is proposed.

We present a new automatic tool for time-domain astronomy - the Cambridge Photometric Calibration Server 2.0 - developed under OPTICON H2020 programme. It has been designed to respond to the need of automated rapid photometric data calibration and dissemination for transient events, primarily from Gaia space mission. CPCS has been in operation since 2013 and has been used to calibrate around 130 000 observations of hundreds of transients. We present the status of this tool's development and demonstrate improvements made in the second version. The tests present the ability to combine CCD imaging data from multiple telescopes and a whole variety of instruments. New tool provides science-ready photometric data within minutes from observations in the automatic manner., Comment: 4 pages, 1 figure, to appear on the Proceedings of the XXXIX Assembly of the Polish Astronomical Society (9-12.09.2019, Olsztyn, Poland)

In this paper a class of combinatorial optimization problems is discussed. It is assumed that a feasible solution can be constructed in two stages. In the first stage the objective function costs are known while in the second stage they are uncertain and belong to an interval uncertainty set. In order to choose a solution, the minmax regret criterion is used. Some general properties of the problem are established and results for two particular problems, namely the shortest path and the selection problem, are shown.

This paper discusses a class of uncertain optimization problems, in which unknown parameters are modeled by fuzzy intervals. The membership functions of the fuzzy intervals are interpreted as possibility distributions for the values of the uncertain parameters. It is shown how the known concepts of robustness and light robustness, for the interval uncertainty representation of the parameters, can be generalized to choose solutions under the assumed model of uncertainty in the possibilistic setting. Furthermore, these solutions can be computed efficiently for a wide class of problems, in particular for linear programming problems with fuzzy parameters in constraints and objective function. In this paper a theoretical framework is presented and results of some computational tests are shown.

In this paper a class of robust two-stage combinatorial optimization problems is discussed. It is assumed that the uncertain second stage costs are specified in the form of a convex uncertainty set, in particular polyhedral or ellipsoidal ones. It is shown that the robust two-stage versions of basic network and selection problems are NP-hard, even in a very restrictive cases. Some exact and approximation algorithms for the general problem are constructed. Polynomial and approximation algorithms for the robust two-stage versions of basic problems, such as the selection and shortest path problems, are also provided.

In this paper a class of combinatorial optimization problems is discussed. It is assumed that a solution can be constructed in two stages. The current first-stage costs are precisely known, while the future second-stage costs are only known to belong to an uncertainty set, which contains a finite number of scenarios with known probability distribution. A partial solution, chosen in the first stage, can be completed by performing an optimal recourse action, after the true second-stage scenario is revealed. A solution minimizing the Conditional Value at Risk (CVaR) measure is computed. Since expectation and maximum are boundary cases of CVaR, the model generalizes the traditional stochastic and robust two-stage approaches, previously discussed in the existing literature. In this paper some new negative and positive results are provided for basic combinatorial optimization problems such as the selection or network problems.

This paper deals with a robust recoverable approach to 0-1 programming problems. It is assumed that a solution constructed in the first stage can be modified to some extent in the second stage. This modification consists in choosing a solution in some prescribed neighborhood of the current solution. The second stage solution cost can be uncertain and a polyhedral structure of uncertainty is used. The resulting robust recoverable problem is a min-max-min problem, which can be hard to solve when the number of variables is large. In this paper we provide a framework for solving robust recoverable 0-1 programming problems with a specified polyhedral uncertainty and propose several lower bounds and approximate solutions, which can be used for a wide class of 0-1 optimization problems. The results of computational tests for two problems, namely the assignment and the knapsack ones, are also presented.

This paper discusses the shortest path problem in a general directed graph with $n$ nodes and $K$ cost scenarios (objectives). In order to choose a solution, the min-max criterion is applied. The min-max version of the problem is hard to approximate within $\Omega(\log^{1-\epsilon} K)$ for any $\epsilon>0$ unless NP$\subseteq \text{DTIME}(n^{\text{polylog} \,n})$ even for arc series-parallel graphs and within $\Omega(\log n/\log\log n)$ unless NP$\subseteq \text{ZPTIME}(n^{\log\log n})$ for acyclic graphs. The best approximation algorithm for the min-max shortest path problem in general graphs, known to date, has an approximation ratio of~$K$. In this paper, an $\widetilde{O}(\sqrt{n})$ flow LP-based approximation algorithm for min-max shortest path in general graphs is constructed. It is also shown that the approximation ratio obtained is close to an integrality gap of the corresponding flow LP relaxation.

The paper deals with a multiobjective combinatorial optimization problem with $K$ linear cost functions. The popular Ordered Weighted Averaging (OWA) criterion is used to aggregate the cost functions and compute a solution. It is well known that minimizing OWA for most basic combinatorial problems is weakly NP-hard even if the number of objectives $K$ equals two, and strongly NP-hard when $K$ is a part of the input. In this paper, the problem with nonincreasing weights in the OWA criterion and a large $K$ is considered. A method of reducing the number of objectives by appropriately aggregating the objective costs before solving the problem is proposed. It is shown that an optimal solution to the reduced problem has a guaranteed worst-case approximation ratio. Some new approximation results for the Hurwicz criterion, which is a special case of OWA, are also presented.

In this paper a class of single machine scheduling problems is considered. It is assumed that job processing times and due dates can be uncertain and they are specified in the form of discrete scenario set. A probability distribution in the scenario set is known. In order to choose a schedule some risk criteria such as the value at risk (VaR) an conditional value at risk (CVaR) are used. Various positive and negative complexity results are provided for basic single machine scheduling problems. In this paper new complexity results are shown and some known complexity results are strengthen.

In this paper the problem of selecting $p$ out of $n$ available items is discussed, such that their total cost is minimized. We assume that costs are not known exactly, but stem from a set of possible outcomes. Robust recoverable and two-stage models of this selection problem are analyzed. In the two-stage problem, up to $p$ items is chosen in the first stage, and the solution is completed once the scenario becomes revealed in the second stage. In the recoverable problem, a set of $p$ items is selected in the first stage, and can be modified by exchanging up to $k$ items in the second stage, after a scenario reveals. We assume that uncertain costs are modeled through bounded uncertainty sets, i.e., the interval uncertainty sets with an additional linear (budget) constraint, in their discrete and continuous variants. Polynomial algorithms for recoverable and two-stage selection problems with continuous bounded uncertainty, and compact mixed integer formulations in the case of discrete bounded uncertainty are constructed.

We plan to measure the angular diameters of a sample of Penn State-Torun Planet Search (PTPS) giant exoplanet host star candidates using the Navy Precision Optical Interferometer. The radii of evolved giant stars obtained using spectroscopy are usually ill-defined because of the method's indirect nature and evolutionary model dependency. The star's radius is a critical parameter used to calculate luminosity and mass, which are often not well known for giant stars. Therefore, this problem also affects the orbital period, mass, and surface temperature of the planet. Our interferometric observations will significantly decrease the errors for these parameters. We present preliminary results from NPOI observations of six stars in the PTPS sample., Comment: 8 pages, 1 figure, 3 tables, from the SPIE Optical and Infrared Interferometry and Imaging V conference, Edinburgh, Scotland

This paper deals with the recoverable robust spanning tree problem under interval uncertainty representations. A polynomial time, combinatorial algorithm for the recoverable spanning tree problem is first constructed. This problem generalizes the incremental spanning tree problem, previously discussed in literature. The algorithm built is then applied to solve the recoverable robust spanning tree problem, under the traditional interval uncertainty representation, in polynomial time. Moreover, the algorithm allows to obtain, under some mild assumptions about the uncertainty intervals,several approximation results for the recoverable robust spanning tree problem under the Bertsimas and Sim interval uncertainty representation and the interval uncertainty representation with a budget constraint.

In this paper the robust recoverable spanning tree problem with interval edge costs is considered. The complexity of this problem has remained open to date. It is shown that the problem is polynomially solvable, by using an iterative relaxation method. A generalization of this idea to the robust recoverable matroid basis problem is also presented. Polynomial algorithms for both robust recoverable problems are proposed.

In this paper the following selection problem is discussed. A set of $n$ items is given and we wish to choose a subset of exactly $p$ items of the minimum total cost. This problem is a special case of 0-1 knapsack in which all the item weights are equal to~1. Its deterministic version has a trivial $O(n)$-time algorithm, which consists in choosing $p$ items of the smallest costs. In this paper it is assumed that the item costs are uncertain. Two robust models, namely two-stage and recoverable ones, under discrete and interval uncertainty representations, are discussed. Several positive and negative complexity results for both of them are provided.

In this paper a class of discrete optimization problems with uncertain costs is discussed. The uncertainty is modeled by introducing a scenario set containing a finite number of cost scenarios. A probability distribution in the scenario set is available. In order to choose a solution the weighted OWA criterion (WOWA) is applied. This criterion allows decision makers to take into account both probabilities for scenarios and the degree of pessimism/ optimism. In this paper the complexity of the considered class of discrete optimization problems is described and some exact and approximation algorithms for solving it are proposed. An application to a selection problem, together with results of computational tests are shown.

This paper deals with the max-min and min-max regret versions of the maximum weighted independent set problem on interval graphswith uncertain vertex weights. Both problems have been recently investigated by Nobibon and Leus (2014), who showed that they are NP-hard for two scenarios and strongly NP-hard if the number of scenarios is a part of the input. In this paper, new complexity and approximation results on the problems under consideration are provided, which extend the ones previously obtained. Namely, for the discrete scenario uncertainty representation it is proven that if the number of scenarios $K$ is a part of the input, then the max-min version of the problem is not at all approximable. On the other hand, its min-max regret version is approximable within $K$ and not approximable within $O(\log^{1-\epsilon}K)$ for any $\epsilon>0$ unless the problems in NP have quasi polynomial algorithms. Furthermore, for the interval uncertainty representation it is shown that the min-max regret version is NP-hard and approximable within 2.

In this paper a class of single machine scheduling problems is discussed. It is assumed that job parameters, such as processing times, due dates, or weights are uncertain and their values are specified in the form of a discrete scenario set. The Ordered Weighted Averaging (OWA) aggregation operator is used to choose an optimal schedule. The OWA operator generalizes traditional criteria in decision making under uncertainty, such as the maximum, average, median or Hurwicz criterion. It also allows us to extend the robust approach to scheduling by taking into account various attitudes of decision makers towards the risk. In this paper a general framework for solving single machine scheduling problems with the OWA criterion is proposed and some positive and negative computational results for two basic single machine scheduling problems are provided.

In this paper new complexity and approximation results on the robust versions of the representatives selection problem, under the scenario uncertainty representation, are provided, which extend the results obtained in the recent papers by Dolgui and Kovalev (2012), and Deineko and Woeginger (2013). Namely, it is shown that if the number of scenarios is a part of input, then the min-max (regret) representatives selection problem is not approximable within a ratio of $O(\log^{1-\epsilon}K)$ for any $\epsilon>0$, where $K$ is the number of scenarios, unless the problems in NP have quasi-polynomial time algorithms. An approximation algorithm with an approximation ratio of $O(\log K/ \log \log K)$ for the min-max version of the problem is also provided.

In this paper a class of bottleneck combinatorial optimization problems with uncertain costs is discussed. The uncertainty is modeled by specifying a discrete scenario set containing a finite number of cost vectors, called scenarios. In order to choose a solution the Ordered Weighted Averaging aggregation operator (shortly OWA) is applied. The OWA operator generalizes traditional criteria in decision making under uncertainty such as the maximum, minimum, average, median, or Hurwicz criterion. New complexity and approximation results in this area are provided. These results are general and remain valid for many problems, in particular for a wide class of network problems., Comment: arXiv admin note: text overlap with arXiv:1305.5339

In this paper a class of combinatorial optimization problems with uncertain costs is discussed. The uncertainty is modeled by specifying a discrete scenario set containing $K$ distinct cost scenarios. The Ordered Weighted Averaging (OWA for short) aggregation operator is applied to choose a solution. The well-known criteria such as: the maximum, minimum, average, Hurwicz and median are special cases of OWA. By using OWA, the traditional min-max approach to combinatorial optimization problems with uncertain costs, often regarded as too conservative, can be generalized. The computational complexity and approximability of the problem of minimizing OWA for the considered class of problems are investigated and some new positive and negative results in this area are provided. These results remain valid for many important problems, such as network or resource allocation problems.

The paper deals with a lot sizing problem with ill-known demands modeled by fuzzy intervals whose membership functions are possibility distributions for the values of the uncertain demands. Optimization criteria, in the setting of possibility theory, that lead to choose robust production plans under fuzzy demands are given. Some algorithms for determining optimal robust production plans with respect to the proposed criteria, and for evaluating production plans are provided. Some computational experiments are presented.

We propose to measure the radii of the Penn State - Torun Planet Search (PTPS) exoplanet host star candidates using the CHARA Array. Stellar radii estimated from spectroscopic analysis are usually inaccurate due to indirect nature of the method and strong evolutionary model dependency. Also the so-called degeneracy of stellar evolutionary tracks due to convergence of many tracks in the giant branch decreases the precision of such estimates. However, the radius of a star is a critical parameter for the calculation of stellar luminosity and mass, which are often not well known especially for giants. With well determined effective temperature (from spectroscopy) and radius the luminosity may be calculated precisely. In turn also stellar mass may be estimated much more precisely. Therefore, direct radii measurements increase precision in the determination of planetary candidates masses and the surface temperatures of the planets., Comment: 3 pages, 1 figure, in Proceedings IAU Symposium 282: From Interacting Binaries to Exoplanets: Essential Modelling Tools, 2011, eds. M. T. Richards & I. Hubeny, accepted version (2 pages) is available on-line at: http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=8544000&fulltextType=RA&fileId=S1743921311027360

In this paper the minimum spanning tree problem with uncertain edge costs is discussed. In order to model the uncertainty a discrete scenario set is specified and a robust framework is adopted to choose a solution. The min-max, min-max regret and 2-stage min-max versions of the problem are discussed. The complexity and approximability of all these problems are explored. It is proved that the min-max and min-max regret versions with nonnegative edge costs are hard to approximate within $O(\log^{1-\epsilon} n)$ for any $\epsilon>0$ unless the problems in NP have quasi-polynomial time algorithms. Similarly, the 2-stage min-max problem cannot be approximated within $O(\log n)$ unless the problems in NP have quasi-polynomial time algorithms. In this paper randomized LP-based approximation algorithms with performance ratio of $O(\log^2 n)$ for min-max and 2-stage min-max problems are also proposed.