Control point policy optimization using genetic algorithms

This paper describes the application of an integrated Genetic Algorithm (GA)/Discrete Event Simulation model for selecting optimum values for Critical Point Policy (CPP) hedging time and buffer size parameters. The CPP is shown to perform well, when compared with the Critical Ratio priority rule, in terms of improving service levels, particularly when subject to conditions where buffer sizes and Takt times are required to be small. The technique developed involves buffer sizes being chosen by a GA according to a constraint on the total storage space available within the system. A method is described for reducing the number of variables that the GA needs to deal with, hence, improving the efficiency of the GA optimization process. The development and application work reported also provides further understanding into how and when the CPP should be applied.     

Oral and intestinal digestion of oligosaccharides as potential sweeteners: A systematic evaluation

The utilisation of oligosaccharides by oral microorganisms and intestinal enzymes are important factors in determining their effectiveness as alternative sweeteners. In this study, classes of naturally occurring sugars were assessed using in vitro models for oral and intestinal digestibility, in order to test the influence of chemical structure on functional properties. Amongst the classes of sucrose isomers, α-glucobioses, β-glucobioses and sucrose-based oligosaccharides, structures were identified that were not utilised by the common oral bacterium Streptococcus mutans, and would therefore contribute to the non-cariogenic potential of a sweetener. Analysis of the rate and products of digestion by a rat glucosidase mixture was used to determine the relative intestinal digestibility. The results showed that oligosaccharides containing a (1 → 6)-β-Glc group, including gentiobiose and gentiobiitol, together with melezitose, a sucrose-derived oligosaccharide containing a α-Glc-(1 → 3)-Fru moiety were resistant to digestion by both S. mutans and mammalian intestinal enzymes, highlighting their potential as dietary sugar substitutes.     

Estimating percent impervious cover from Landsat-based land cover with a simple and transferable regression model

Percent impervious cover (PIC) is a widely used metric in ecological and hydrological analyses because it is highly correlated with pollutant and storm water run-off. The moderate-resolution satellite data (e.g. Landsat), that are typically used to calculate PIC, tend to overestimate PIC for all but very rural and very urban landscapes. Existing models for calibrating PIC estimates (e.g. ISAT, ETIS) are limited in that they are applicable only for specific land cover datasets and may also require population data; furthermore, these models have not been tested for performance outside of the geographic locations in which they were developed. The goal of this study was to explore simple but widely applicable regression models as tools for calibrating PIC estimates based on moderate resolution satellite data. The regression models used impervious land cover, from Landsat-based datasets, as the sole predictor of actual PIC. PIC was calculated for analysis units, ranging in size from 2.25 ha to ≥100 ha, for locations in Connecticut, Massachusetts, and Ohio in the United States. Regression models were fit for each size class of analysis unit at each study location; generalized versions of the models were created by fitting a regression to all size classes of analysis units at a given study location. Calibrated PIC estimates had root mean square error (RMSE) values that ranged from 1.5–10.7%; these values were considerably better than RMSE values for uncalibrated PIC estimates which ranged from 10.1–23.3%. For both calibrated and uncalibrated PIC, the accuracy of the estimates improved with the increasing size of the analysis units. Model regression coefficients were similar regardless of the analysis unit size, geographic location, or land cover dataset; model performance declined only slightly when applied outside the area in which it was developed. The simple regression models developed in this study had similar performance to previous calibration models (i.e. ISAT, ETIS) but are easier to apply and more widely applicable.     

Complex and dynamic population structures: synthesis, open questions, and future directions

The population structure of an evolutionary algorithm influences the dissemination and mixing of advantageous alleles, and therefore affects search performance. Much recent attention has focused on the analysis of complex population structures, characterized by heterogeneous connectivity distributions, non-trivial clustering properties, and degree–degree correlations. Here, we synthesize the results of these recent studies, discuss their limitations, and highlight several open questions regarding (1) unsolved theoretical issues and (2) the practical utility of complex population structures for evolutionary search. In addition, we will discuss an alternative complex population structure that is known to significantly influence dynamical processes, but has yet to be explored for evolutionary optimization. We then shift our attention toward dynamic population structures, which have received markedly less attention than their static counterparts. We will discuss the strengths and limitations of extant techniques and present open theoretical and experimental questions and directions for future research. In particular, we will focus on the prospects of “active linking,” wherein edges are dynamically rewired according to the genotypic or phenotypic properties of individuals, or according to the success of prior inter-individual interactions.     

The role of the methyl ester moiety in biodiesel combustion: A kinetic modeling comparison of methyl butanoate and n-butane

Growth of the biodiesel industry has motivated increased study of the combustion characteristics of its constituent molecules and building combustion modeling capability. Understanding how these characteristics differ between bio-derived and conventional diesel fuels can help in evaluating biodiesel performance. A kinetic modeling comparison of methyl butanoate and n-butane, its corresponding alkane, contrasted the combustion of methyl esters and normal alkanes, towards understanding the effect of the methyl ester moiety. Utilizing a combined n-heptane and methyl butanoate kinetic mechanism in shock tube simulations, the results predicted no region of negative temperature coefficient (NTC) behavior for methyl butanoate, compared to a well defined NTC region for n-butane. We observed that oxidation pathways associated with the methyl ester moiety inhibited NTC behavior, through increased production of hydroperoxy radicals (HO₂) instead of hydroxyl radicals (OH). In addition, we compared the evolution of carbon monoxide, carbon dioxide, ethylene and acetylene. The early formation of CO and CO₂, directly from methyl butanoate, revealed unique reaction pathways that also influenced a reduction in soot precursor formation. Overall, these results will help to understand how combustion processes change with the inclusion of oxygenated fuels, which will inform the study and design of combustion technologies.     

Bioeconomics of invasive species: using real options theory to integrate ecology,economics, and risk management

Policy makers face two countervailing incentives in invasive species management—the Pull-incentive to move quickly and the Push-incentive to wait-and-see before making irreversible investments. Real options theory is used to help understand this fundamental trade-off both in design and application. In designing policies, real options theory shows how the management of invasive species should account for the intertwined concepts of ecological risk/ecological irreversibility and economic risk/economic irreversibility. In applying policies, real options theory shows for species spreading slowly with little uncertainty, the push-incentive dominates, advocating a wait-and-see approach. In contrast, for fastspreading species, their diffusion is too fast and too unpredictable to do anything other than act immediately – the pull-incentive dominates. In addition, results indicate both the source and the magnitude of uncertainty matter, but the nature of the impact depends on the irreversibility of the policy decision highlighting the key value of flexibility in policy design and application.