Effects of Plant Fibers on the Quality of Cookies

Food, Nutrition and Institution Administratio

Interval type-2 fuzzy modelling and stochastic search for real-world inventory management

Real-world systems present a variety of challenges to the modeller, not least of which is the problem of uncertainty inherent in their operation. In this research, an interval type-2 fuzzy model is applied to a real-world problem, the goal being to discover a suitable optimisation configuration to enable a search for an inventory plan using the model. To this end, a series of simulated annealing configurations and the interval type-2 fuzzy model were used to search for appropriate inventory plans for a large-scale real-world problem. A further set of tests were conducted in which the performance of the interval type-2 fuzzy model was compared with a corresponding type-1 fuzzy model. In these tests the results were inconclusive, though, as will be discussed there are many ways in which type-2 fuzzy logic can be exploited to demonstrate its advantages over a type-1 approach. To conclude, in this research we have shown that a combination of interval type-2 fuzzy logic and simulated annealing is a logical choice for inventory management modelling and inventory plan search, and propose that the benefits that a type-2 model offers, can make it preferable to a corresponding type-1 system

Identification of microbial signatures linked to oilseed rape yield decline at the landscape scale

Background: The plant microbiome plays a vital role in determining host health and productivity. However, we lack real-world comparative understanding of the factors which shape assembly of its diverse biota, and crucially relationships between microbiota composition and plant health. Here we investigated landscape scale rhizosphere microbial assembly processes in oilseed rape (OSR), the UK’s third most cultivated crop by area and the world's third largest source of vegetable oil, which suffers from yield decline associated with the frequency it is grown in rotations. By including 37 conventional farmers’ fields with varying OSR rotation frequencies, we present an innovative approach to identify microbial signatures characteristic of microbiomes which are beneficial and harmful to the host. Results: We show that OSR yield decline is linked to rotation frequency in real-world agricultural systems. We demonstrate fundamental differences in the environmental and agronomic drivers of protist, bacterial and fungal communities between root, rhizosphere soil and bulk soil compartments. We further discovered that the assembly of fungi, but neither bacteria nor protists, was influenced by OSR rotation frequency. However, there were individual abundant bacterial OTUs that correlated with either yield or rotation frequency. A variety of fungal and protist pathogens were detected in roots and rhizosphere soil of OSR, and several increased relative abundance in root or rhizosphere compartments as OSR rotation frequency increased. Importantly, the relative abundance of the fungal pathogen Olpidium brassicae both increased with short rotations and was significantly associated with low yield. In contrast, the root endophyte Tetracladium spp. showed the reverse associations with both rotation frequency and yield to O. brassicae, suggesting that they are signatures of a microbiome which benefits the host. We also identified a variety of novel protist and fungal clades which are highly connected within the microbiome and could play a role in determining microbiome composition. Conclusions: We show that at the landscape scale, OSR crop yield is governed by interplay between complex communities of both pathogens and beneficial biota which is modulated by rotation frequency. Our comprehensive study has identified signatures of dysbiosis within the OSR microbiome, grown in real-world agricultural systems, which could be used in strategies to promote crop yield. [MediaObject not available: see fulltext.

Chemical control of Sclerotinia Blight of peanut

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Adaptive Appointment Scheduling for Patient-Centered Medical Homes

Incorporating patient-centered medical home (PCMH) principles, we develop an adaptive appointment scheduling model for a primary care setting. We propose a simulation optimization approach to sequentially schedule appointments to provide desirable schedules from the perspective of both patients and the medical practices. The objective minimizes the weighted expected cost of patient direct and indirect waiting time, physician idle time, and physician overtime. Our efficient data-driven algorithm considers patient preferences and future appointment requests, while employing overbooking to mitigate patient related uncertainties, such as no-shows and lateness. Benchmarking against myopic and optimal algorithms, computational results show that the adaptive scheduling approach provides significant value. The adaptive method provides considerable cost savings even under conditions of high patient uncertainty. In addition, the method produces high quality solutions in little time, thus providing a viable tool for practice

New models for operations planning under risk and uncertainty

The presence of uncertainty complicates planning decisions in all industries and sectors. In an operations context, failure to acknowledge and incorporate the unknowns in the decision-making process may lead to undesirable outcomes including reduced profitability and customer dissatisfaction. This dissertation considers three problems that incorporate risk and uncertainty in production scheduling, surgical scheduling, and supply chain risk management. The first problem involves scheduling products in a parallel, non-identical machine environment subject to sequence dependent setup costs, sequence dependent setup times, where production waste and processing time of a product depend on feasible machine assignments. A new metric for schedule quality is introduced that considers the tradeoff between the risk of imperfect production and overall production time requirements. We develop a mathematical model and two solution approaches that determine schedules that are superior to schedules found using more traditional scheduling measures with respect to waste and overtime costs. The second problem presents a novel, scenario-based model for incorporating the inherent uncertainty of the operating theater (OT) environment into elective operation scheduling decisions. Specifically, the model allows the time requirements of elective operations to be uncertain and explicitly accounts for the service of urgent patients by requiring opportunities (i.e., break-in-moments) to perform urgent operations before a specified duration elapses. We develop a two step-solution procedure that demonstrates that the scenario-based approach is effective at capturing the uncertainty in the OT environment. The third problem examines mitigation strategies with respect to supply and demand uncertainties in a supply chain. In addition to typical mitigation strategies, such as inventory holding and sourcing from multiple suppliers, we investigate alternate strategies, namely concurrent sourcing and downward substitution. We develop an analytical model for a single period, two-product setting. Analysis of first order optimality conditions reveals several insights into environment characteristics that influence the optimal actions of the manufacturer. Most notably, in contrast to works considering infinite capacity, we find that cost is not the sole driver for strategy selection. Each problem considered is a significant extension of previous works in the respective area that is motivated by industry experience, empirical research, or interaction with practitioners. Mathematical models are presented for each problem and used to develop efficient solution approaches, and provide managerial insights. The modeling techniques and solution approaches developed are applicable to problem domains beyond those considered. (Published By University of Alabama Libraries

Multi-perspective system-wide analyses of adaptive traffic signal control systems using microsimulation and contemporary data sources

The primary function of traffic signals is to assign the right of way to vehicular and pedestrian traffic at intersections. Effective traffic signal system reduces congestion, increases intersection capacity, and improves other traffic related performance measures such as safety and mobility. To ensure these goals are met, traffic signals require updated timings to maintain proper operation. These updated signal timings impact not only traffic performance, but overall transportation system efficiency. Because traditional signal timing plans may not accommodate variable and unpredictable traffic demands, a more proactive approach is necessary to ensure properly timed and maintained traffic signals. Adaptive traffic control systems (ATCS) continually collect data and optimize signal timing on a real time basis thereby reducing the aforementioned drawbacks of traditional signal retiming. Understanding and characterizing how these systems are working is important to transportation engineers, and evaluating these systems can provide useful insights. The objective of this dissertation is to develop evaluation methodologies (both operational and economical) for adaptive traffic signal control that go beyond the traditional assessments that use traffic measures of effectiveness (MOEs). Case studies are conducted for Sydney Coordinated Adaptive Traffic System (SCATS) implementations in Alabama, which are useful in objective evaluations of ATCS (in general) for both their current and future operational environments by using microsimulation techniques and/or field data from contemporary data sources. The study contains detailed comparative analyses of traffic operations of the study corridors for existing peak hour traffic conditions under the previous time-of-day (TOD) plan and similar peak hour conditions after SCATS implementation. Although simulation analysis using VISSIM traffic microsimulation software is the primary methodological technique used for evaluating comparative performances, arterial data from other sources (Bluetooth MAC Address Matching and crowdsourced travel data) are also used to perform the evaluations, which is a novel application for this context. While past studies have considered either the arterial or its side-streets performances in their evaluations, this work explored a system-wide approach looking at the composite performance of both dimensions together. Finally, for transportation agencies which operate within budget constraints, it is important to know the real worth of attaining the benefits from ATCS implementations. The last chapter of this dissertation extends the evaluation methodology to include benefit-cost analysis (BCA) by evaluating the ATCS performance for both current and future traffic conditions. This information will be helpful for transportation agencies, planners, and practitioners to understand and justify their ATCS investment and also serve as a guideline for their future ITS projects. (Published By University of Alabama Libraries