Inventory control for point-of-use locations in hospitals

Most inventory management systems at hospital departments are characterised by lost sales, periodic reviews with short lead times, and limited storage capacity. We develop two types of exact models that deal with all these characteristics. In a capacity model, the service level is maximised subject to a capacity restriction, and in a service model the required capacity is minimised subject to a service level restriction. We also formulate approximation models applicable for any lost-sales inventory system (cost objective, no lead time restrictions etc). For the capacity model, we develop a simple inventory rule to set the reorder levels and order quantities. Numerical results for this inventory rule show an average deviation of 1% from the optimal service levels. We also embed the single-item models in a multi-item system. Furthermore, we compare the performance of fixed order size replenishment policies and (R, s, S) policies

Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS

Summary: 1. Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. 2. R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. 3. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to specific suggestions about how to change the mathematical description of models to make them more amenable to parameter estimation. 4. A companion web site (https://groups.nceas.ucsb.edu/nonlinear-modeling/projects) presents detailed examples of application of the three tools to a variety of typical ecological estimation problems; each example links both to a detailed project report and to full source code and data

Iterative approximation of k-limited polling systems

The present paper deals with the problem of calculating queue length distributions in a polling model with (exhaustive) k-limited service under the assumption of general arrival, service and setup distributions. The interest for this model is fueled by an application in the field of logistics. Knowledge of the queue length distributions is needed to operate the system properly. The multi-queue polling system is decomposed into single-queue vacation systems with k-limited service and state-dependent vacations, for which the vacation distributions are computed in an iterative approximate manner. These vacation models are analyzed via matrix-analytic techniques. The accuracy of the approximation scheme is verified by means of an extensive simulation study. The developed approximation turns out be accurate, robust and computationally efficient

Simulating avian species and foraging group responses to fuel reduction treatments in coniferous forests

Over a century of fire suppression activities have altered the structure and composition of mixed conifer forests throughout the western United States. In the absence of fire, fuels have accumulated in these forests causing concerns over the potential for catastrophic wildfires. Fuel reduction treatments are being used on federal and state lands to reduce the threat of wildfire by mechanically removing biomass. Although these treatments result in a reduction in fire hazard, their impact on wildlife is less clear. We use a multi-species occupancy modeling approach to build habitat-suitability models for 46 upland forest birds found in the Lake Tahoe Basin in the Sierra Nevada based on forest structure and abiotic variables. Using a Bayesian hierarchical framework, we predict species-specific and community-level responses to changes in forest structure and make inferences about responses of important avian foraging guilds. Disparities within and among foraging group responses to canopy cover, tree size and shrub cover emphasized the complexities in managing forests to meet biodiversity goals. Based on our species-specific model results, we predicted changes in species richness and community similarity under forest prescriptions representing three management practices: no active management, a typical fuel reduction treatment that emphasizes spacing between trees, and a thinning prescription that creates structural heterogeneity. Simulated changes to structural components of the forest analogous to management practices to reduce fuel loads clearly affected foraging groups differentially despite variability in responses within guilds. Although species richness was predicted to decrease slightly under both simulated fuels reduction treatments, the prescription that incorporated structural heterogeneity retained marginally higher species richness. The composition of communities supported by different management alternatives was influenced by urbanization and management practice, emphasizing the importance of creating heterogeneity at the landscape scale

Service Level Constrained Inventory Systems

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151878/1/poms13060_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151878/2/poms13060.pd

A Multispecies Hierarchical Model to Integrate Count and Distance-Sampling Data

Integrated community models—an emerging framework in which multiple data sources for multiple species are analyzed simultaneously—offer opportunities to expand inferences beyond the single-species and single-data-source approaches common in ecology. We developed a novel integrated community model that combines distance sampling and single-visit count data; within the model, information is shared among data sources (via a joint likelihood) and species (via a random-effects structure) to estimate abundance patterns across a community. Parameters relating to abundance are shared between data sources, and the model can specify either shared or separate observation processes for each data source. Simulations demonstrated that the model provided unbiased estimates of abundance and detection parameters even when detection probabilities varied between the data types. The integrated community model also provided more accurate and more precise parameter estimates than alternative single-species and single-data-source models in many instances. We applied the model to a community of 11 herbivore species in the Masai Mara National Reserve, Kenya, and found considerable interspecific variation in response to local wildlife management practices: Five species showed higher abundances in a region with passive conservation enforcement (median across species: 4.5× higher), three species showed higher abundances in a region with active conservation enforcement (median: 3.9× higher), and the remaining three species showed no abundance differences between the two regions. Furthermore, the community average of abundance was slightly higher in the region with active conservation enforcement but not definitively so (posterior mean: higher by 0.20 animals; 95% credible interval: 1.43 fewer animals, 1.86 more animals). Our integrated community modeling framework has the potential to expand the scope of inference over space, time, and levels of biological organization, but practitioners should carefully evaluate whether model assumptions are met in their systems and whether data integration is valuable for their applications