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Variations of physical and chemical characteristics of biomass reduce equipment utilization and increase operational costs of biomass processing. Biomass processing facilities use sensors to monitor the changes in biomass characteristics. Integrating sensory data into the operational decisions in biomass processing will increase its flexibility to the changing biomass conditions. In this paper, we propose a multi-stage stochastic programming model that minimizes the expected operational costs by identifying the inventory level and creating an operational decision policy for equipment speed settings. These policies take the sensory information data and the current biomass inventory level as inputs to dynamically adjust inventory levels and equipment settings according to the changes in the biomass' characteristics. We ensure that a prescribed target reactor utilization is consistently achieved by penalizing the violation of the target reactor feeding rate. A case study is developed using real-world data collected at Idaho National Laboratory's biomass processing facility. We show the value of multi-stage stochastic programming from an extensive computational experiment. Our sensitivity analysis indicates that updating the infeed rate of the system, the processing speed of equipment, and bale sequencing based on the moisture level of biomass improves the processing rate of the reactor and reduces operating costs. [ABSTRACT FROM AUTHOR]

In the aftermath of a hurricane, humanitarian logistics plays a critical role in delivering relief items to the affected areas in a timely fashion. This paper proposes a novel stochastic look-ahead framework that implements a two-stage stochastic programming model in a rolling horizon approach to address the evolving uncertain logistics system state during the post-hurricane humanitarian logistics operations. The two-stage stochastic programming model that executes in this rolling horizon approach is formulated as a mixed-integer programming problem. The model aims to minimize the total cost incurred in the logistics operations, which consist of transportation cost and social cost. The social cost is measured as a function of deprivation for unsatisfied demand. Our extensive numerical results and sensitivity analysis demonstrate the effectiveness of the proposed approach in reducing the total cost incurred during the post-hurricane relief logistics operations compared to the two-stage stochastic programming model implemented in a static fashion. [ABSTRACT FROM AUTHOR]

Crack problems are often reduced to dual integral equations, which can be solved by expanding the displacement integral equation as a series in the form of Chebyshev-like or Jacobi polynomials. Schmidt's multiplying-factor integration method has been one of the most favorable techniques for determining the expansion coefficients by constructing a well-posed system of linear algebraic equations. However, Schmidt's method is less efficient for numerical computation because the matrix elements of the linear equations are evaluated from dual integrals. In this study, we propose a modified method to construct linear equations to efficiently determine the expansion coefficients. The modified technique is developed upon the application of certain multiplying factors to the traction integral equation and then integrating the resulting equation over "source" regions. Such manipulations simplify the matrix elements as single integrals. By carrying out numerical examples, we demonstrate that the technique is not only accurate but also very efficient. In particular, the method only needs approximately 1/5 of the computation time of Schmidt's method. Therefore, this method can be used to replace Schmidt's method and is expected to be very useful in solving crack problems. [ABSTRACT FROM AUTHOR]

Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US's cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system's reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. The sample average approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory's biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution, increases utilization of the reactor and reduces operational costs. [ABSTRACT FROM AUTHOR]

In this paper, we extend the adaptive partition-based approach for solving two-stage stochastic programs with fixed recourse matrix and fixed cost vector to the multistage stochastic programming setting where the stochastic process is assumed to be stage-wise independent. The proposed algorithms integrate the adaptive partition-based strategy with a popular approach for solving multistage stochastic programs, the stochastic dual dynamic programming (SDDP) algorithm, according to two main strategies. These two strategies are distinct from each other in the manner by which they refine the partitions during the solution process. In particular, we propose a refinement outside SDDP strategy whereby we iteratively solve a coarse scenario tree induced by the partitions, and refine the partitions in a separate step outside of SDDP, only when necessary. We also propose a refinement within SDDP strategy where the partitions are refined in conjunction with the machinery of the SDDP algorithm. We then use, within the two different refinement schemes, different tree-traversal strategies which allow us to have some control over the size of the partitions. We performed numerical experiments on a hydro-thermal power generation planning problem. Numerical results show the effectiveness of the proposed algorithms that use the refinement outside SDDP strategy in comparison to the standard SDDP algorithm and algorithms that use the refinement within SDDP strategy. [ABSTRACT FROM AUTHOR]

The public reporting of hospitals' quality of care is providing additional motivation for hospitals to deliver high-quality patient care. Hospital Compare, a consumer-oriented website by the Centers for Medicare and Medicaid Services (CMS), provides patients with detailed quality of care data on most US hospitals. Given that many quality metrics are the aggregate result of physicians' individual clinical decisions, the question arises if and how hospitals could influence their physicians so that their decisions positively contribute to hospitals' quality goals. In this paper, we develop a decision-theoretic model to explore how three different hospital interventions—incentivization, training, and nudging—may affect physicians' decisions. We focus our analysis on Outpatient Measure 14 (OP-14), which is an imaging quality metric that reports the percentage of outpatients with a brain computed tomography (CT) scan, who also received a same-day sinus CT scan. In most cases, same-day brain and sinus CT scans are considered unnecessary, and high utilizing hospitals aim to reduce their OP-14 metric. Our model captures the physicians' imaging decision process accounting for medical and behavioral factors, in particular the uncertainty in clinical assessment and a physician's diagnostic ability. Our analysis shows how hospital interventions of incentivization, training, and nudging affect physician decisions and consequently OP-14. This decision-theoretic model provides a foundation to develop insights for policy makers on the multi-level effects of their policy decisions. [ABSTRACT FROM AUTHOR]

The Asian summer monsoon (ASM) affects more than one-third of the world's population due to its close connection with floods, droughts thus water resources in densely populated Asian countries. The effects of aerosols emitted in remote regions on the ASM, in contrast to local emissions, remain largely unclear. Here we demonstrate through a hierarchy of climate models that aerosol emissions from the central African wildfires could strengthen the circulation of the ASM (South Asian Monsoon in particular), increase precipitation over South Asia and reduce precipitation immediately north and south of it. The enhanced latent heating over South Asia provides a critical positive feedback to the initial strengthening of monsoon westerlies associated with wildfire-driven anomalous Rossby wave source. The atmospheric dynamical bridge discovered here effectively connects African biomass burning with hydroclimate variability over East Asia in boreal summer and offers a new source of monsoon predictability across a range of timescales. [ABSTRACT FROM AUTHOR]

In this paper, we propose partition-based decomposition algorithms for solving two-stage stochastic integer program with continuous recourse. The partition-based decomposition method enhance the classical decomposition methods (such as Benders decomposition) by utilizing the inexact cuts (coarse cuts) induced by a scenario partition. Coarse cut generation can be much less expensive than the standard Benders cuts, when the partition size is relatively small compared to the total number of scenarios. We conduct an extensive computational study to illustrate the advantage of the proposed partition-based decomposition algorithms compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

We consider well-known decomposition techniques for multistage stochastic programming and a new scheme based on normal solutions for stabilizing iterates during the solution process. The given algorithms combine ideas from finite perturbation of convex programs and level bundle methods to regularize the so-called forward step of these decomposition methods. Numerical experiments on a hydrothermal scheduling problem indicate that our algorithms are competitive with the state-of-the-art approaches such as multistage regularized decomposition, nested decomposition and stochastic dual dynamic programming. [ABSTRACT FROM AUTHOR]

A mathematical formulation is presented for the dynamic stress intensity factor (mode I) of a finite permeable crack subjected to a time-harmonic propagating longitudinal wave in an infinite poroelastic solid. In particular, the effect of the wave-induced fluid flow due to the presence of a liquid-saturated crack on the dynamic stress intensity factor is analyzed. Fourier sine and cosine integral transforms in conjunction with Helmholtz potential theory are used to formulate the mixed boundary-value problem as dual integral equations in the frequency domain. The dual integral equations are reduced to a Fredholm integral equation of the second kind. It is found that the stress intensity factor monotonically decreases with increasing frequency, decreasing the fastest when the crack width and the slow wave wavelength are of the same order. The characteristic frequency at which the stress intensity factor decays the fastest shifts to higher frequency values when the crack width decreases. [ABSTRACT FROM AUTHOR]

We propose two new Lagrangian dual problems for chance-constrained stochastic programs based on relaxing nonanticipativity constraints. We compare the strength of the proposed dual bounds and demonstrate that they are superior to the bound obtained from the continuous relaxation of a standard mixed-integer programming (MIP) formulation. For a given dual solution, the associated Lagrangian relaxation bounds can be calculated by solving a set of single scenario subproblems and then solving a single knapsack problem. We also derive two new primal MIP formulations and demonstrate that for chance-constrained linear programs, the continuous relaxations of these formulations yield bounds equal to the proposed dual bounds. We propose a new heuristic method and two new exact algorithms based on these duals and formulations. The first exact algorithm applies to chance-constrained binary programs, and uses either of the proposed dual bounds in concert with cuts that eliminate solutions found by the subproblems. The second exact method is a branch-and-cut algorithm for solving either of the primal formulations. Our computational results indicate that the proposed dual bounds and heuristic solutions can be obtained efficiently, and the gaps between the best dual bounds and the heuristic solutions are small. [ABSTRACT FROM AUTHOR]

We study solution approaches for the design of reliably connected networks. Specifically, given a network with arcs that may fail at random, the goal is to select a minimum cost subset of arcs such the probability that a connectivity requirement is satisfied is at least $$1 - \epsilon $$ 1 - ϵ , where $$\epsilon $$ ϵ is a risk tolerance. We consider two types of connectivity requirements. We first study the problem of requiring an $$s$$ s - $$t$$ t path to exist with high probability in a directed graph. Then we consider undirected graphs, where we require the graph to be fully connected with high probability. We model each problem as a stochastic integer program with a joint chance constraint, and present two formulations that can be solved by a branch-and-cut algorithm. The first formulation uses binary variables to represent whether or not the connectivity requirement is satisfied in each scenario of arc failures and is based on inequalities derived from graph cuts in individual scenarios. We derive additional valid inequalities for this formulation and study their facet-inducing properties. The second formulation is based on probabilistic graph cuts, an extension of graph cuts to graphs with random arc failures. Inequalities corresponding to probabilistic graph cuts are sufficient to define the set of feasible solutions and violated inequalities in this class can be found efficiently at integer solutions, allowing this formulation to be solved by a branch-and-cut algorithm. Computational results demonstrate that the approaches can effectively solve instances on large graphs with many failure scenarios. In addition, we demonstrate that, by varying the risk tolerance, our model yields a rich set of solutions on the efficient frontier of cost and reliability. [ABSTRACT FROM AUTHOR]

We assess the ability of individual models (single-model ensembles) and the multi-model ensemble (MME) in the European Union-funded ENSEMBLES project to simulate the intraseasonal oscillations (ISOs; specifically in 10-20-day and 30-50-day frequency bands) of the Indian summer monsoon rainfall (ISMR) over the Western Ghats (WG) and the Bay of Bengal (BoB), respectively. This assessment is made on the basis of the dynamical linkages identified from the analysis of observations in a companion study to this work. In general, all models show reasonable skill in simulating the active and break cycles of the 30-50-day ISOs over the Indian summer monsoon region. This skill is closely associated with the proper reproduction of both the northward propagation of the intertropical convergence zone (ITCZ) and the variations of monsoon circulation in this band. However, the models do not manage to correctly simulate the eastward propagation of the 30-50-day ISOs in the western/central tropical Pacific and the eastward extension of the ITCZ in a northwest to southeast tilt. This limitation is closely associated with a limited capacity of models to accurately reproduce the magnitudes of intraseasonal anomalies of both the ITCZ in the Asian tropical summer monsoon regions and trade winds in the tropical Pacific. Poor reproduction of the activity of the western Pacific subtropical high on intraseasonal time scales also amplify this limitation. Conversely, the models make good reproduction of the WG 10-20-day ISOs. This success is closely related to good performance of the models in the representation of the northward propagation of the ITCZ, which is partially promoted by local air-sea interactions in the Indian Ocean in this higher-frequency band. Although the feature of westward propagation is generally represented in the simulated BoB 10-20-day ISOs, the air-sea interactions in the Indian Ocean are spuriously active in the models. This leads to active WG rainfall, which is not present in the observed BoB 10-20-day ISOs. Further analysis indicates that the intraseasonal variability of the ISMR is generally underrepresented in the simulations. Skill of the MME in seasonal ISMR forecasting is strongly dependent on individual model performance. Therefore, in order to improve the model skill with respect to seasonal ISMR forecasting, we suggest it is necessary to better represent the robust dynamical links between the ISOs and the relevant circulation variations, as well as the proportion of intraseasonal variability in the individual models. [ABSTRACT FROM AUTHOR]