Stochastic models for strategic resource allocation in nonprofit foreclosed housing acquisitions

Increased rates of mortgage foreclosures in the U.S. have had devastating social and economic impacts during and after the 2008 financial crisis. As part of the response to this problem, nonprofit organizations such as community development corporations (CDCs) have been trying to mitigate the negative impacts of mortgage foreclosures by acquiring and redeveloping foreclosed properties. We consider the strategic resource allocation decisions for these organizations which involve budget allocations to different neighborhoods under cost and return uncertainty. Based on interactions with a CDC, we develop stochastic integer programming based frameworks for this decision problem, and assess the practical value of the models by using real-world data. Both policy-related and computational analyses are performed, and several insights such as the trade-offs between different objectives, and the efficiency of different solution approaches are presented.     

Confidence-based optimisation for the newsvendor problem under binomial,Poisson and exponential demand

We introduce a novel strategy to address the issue of demand estimation in single-item single-period stochastic inventory optimisation problems. Our strategy analytically combines confidence interval analysis and inventory optimisation. We assume that the decision maker is given a set of past demand samples and we employ confidence interval analysis in order to identify a range of candidate order quantities that, with prescribed confidence probability, includes the real optimal order quantity for the underlying stochastic demand process with unknown stationary parameter(s). In addition, for each candidate order quantity that is identified, our approach produces an upper and a lower bound for the associated cost. We apply this approach to three demand distributions in the exponential family: binomial, Poisson, and exponential. For two of these distributions we also discuss the extension to the case of unobserved lost sales. Numerical examples are presented in which we show how our approach complements existing frequentist—e.g. based on maximum likelihood estimators—or Bayesian strategies.     

Structural dynamics and stability analysis of 2D-FG microbeams with two-directional porosity distribution and variable material length scale parameter

Abstract

Since the two-directional functionally graded (2D-FG) materials can satisfy the new requirements raised based on the elimination of the stress concentration, delamination and cracking problems accompanying with the low cost and lightweight on the structures without sacrificing the stiffness and strength, the structural analyses of these structures become more important than ever. Moreover, the usage of the micro-electromechanical systems composed of 2D-FG materials has been increasing in automotive, military, space, biomedical, and nuclear energy industries. Within this study, the free vibration and buckling behaviors of 2D-FG porous microbeams are investigated based on the modified couple stress theory by employing a transverse shear-normal deformation beam theory and using finite element method. The effects of the thickness to material length scale parameter (MLSP) accompanying with the micro-porosity volume fraction ratio, boundary condition, aspect ratio, and gradient index on the dimensionless fundamental frequencies and dimensionless critical buckling loads of the 2D-FG porous microbeams are investigated. Moreover, with assumption of the variable material length scale parameters (VMLSP), the computed results are compared with ones obtained by employing constant MLSP. It is found that VMLSP increases the stiffness of the 2D-FG porous microbeams and effects the free vibration and buckling responses of these structures.     

An efficient computational method for a stochastic dynamic lot-sizing problem under service-level constraints

We provide an efficient computational approach to solve the mixed integer programming (MIP) model developed by Tarim and Kingsman [8] for solving a stochastic lot-sizing problem with service level constraints under the static-dynamic uncertainty strategy. The effectiveness of the proposed method hinges on three novelties: (i) the proposed relaxation is computationally efficient and provides an optimal solution most of the time, (ii) if the relaxation produces an infeasible solution, then this solution yields a tight lower bound for the optimal cost, and (iii) it can be modified easily to obtain a feasible solution, which yields an upper bound. In case of infeasibility, the relaxation approach is implemented at each node of the search tree in a branch-and-bound procedure to efficiently search for an optimal solution. Extensive numerical tests show that our method dominates the MIP solution approach and can handle real-life size problems in trivial time.     

Double direct templating of periodically nanostructured ZnS hollow microspheres

We introduce a "double direct templating" method for obtaining hollow microspheres with periodically nanostructured walls. Silica or polystyrene colloids are dispersed in a self-assembled hexagonal lyotropic liquid crystal containing precursors for ZnS. The semiconductor ZnS mineralizes on the surface of the colloid, expressing a pore morphology that is a copy of the structure of the liquid crystal. After etching of the sacrificial colloids, hollow capsules with templated and uniform mesoporous shells are obtained.     

Determination of atorvastatin in human plasma by magnetic solid-phase extraction combined with HPLC and application to a pharmacokinetic study

A simple and sensitive analytical procedure by solid-phase extraction method combined with high-performance liquid chromatography and using of graphene–magnetite nanomaterials as sorbent has been developed for the determination of atorvastatin in human plasma. A magnetic solid-phase extraction method as a simple, fast, and efficient extraction technique has been used for sample preparation. A solid nanocomposite material, graphene nanosheets decorated with magnetite nanoparticles, was used as a magnetic adsorbent and the adsorption process was optimized in this study. RP C18 column was used with mobile phase composed of acetonitrile–10?mM orthophosphoric acid by isocratic elution with the flow rate of 1?mL?min^?1. Fluorimetric detection was used by the excitation wavelength at 282?nm and the emission wavelength at 400?nm. It was found that the calibration curve was linear in the 30–150?ng?mL^?1. Limit of detection and limit of quantitation values were found to be 10 and 30?ng?mL^?1, respectively. The intra-day and inter-day relative standard deviation values were less than 5.27%. It has been concluded that the new developed method provides fast, simple, cost reduced, and sensitive assay for atorvastatin determination in human plasma. This method is also applied to a pharmacokinetic study.     

Constraint programming for stochastic inventory systems under shortage cost

One of the most important policies adopted in inventory control is the replenishment cycle policy. Such a policy provides an effective means of damping planning instability and coping with demand uncertainty. In this paper we develop a constraint programming approach able to compute optimal replenishment cycle policy parameters under non-stationary stochastic demand, ordering, holding and shortage costs. We show how in our model it is possible to exploit the convexity of the cost-function during the search to dynamically compute bounds and perform cost-based filtering. Our computational experience show the effectiveness of our approach. Furthermore, we use the optimal solutions to analyze the quality of the solutions provided by an existing approximate mixed integer programming approach that exploits a piecewise linear approximation for the cost function.