An approach for computing AS/R systems travel times in a class-based storage configuration

This study provides an approach to compute the travel time for AS/R systems in a class-based storage environment. A regression analysis is completed in order to define the importance of the key predictors taken into account and to propose a formulation of travel times. The results show the reliability of the model and allow to evaluate the travel time through the identification of a complete list of predictors. The proposed approach supports managers in theex-ante definition of travel times for a warehouse. A correct evaluation of travel times enables a better monitoring of the performance of warehouse operations and can support practitioners in the choice of the configuration not only in terms of kind of cycle, but also from a policy assignment perspective. From a theoretical point of view, this work can be considered as an attempt to refine the existing methods to compute travel times

An approach for computing AS/R systems travel times in a class-based storage configuration

This study provides an approach to compute the travel time for AS/R systems in a class-based storage environment. A regression analysis is completed in order to define the importance of the key predictors taken into account and to propose a formulation of travel times. The results show the reliability of the model and allow to evaluate the travel time through the identification of a complete list of predictors. The proposed approach supports managers in theex-ante definition of travel times for a warehouse. A correct evaluation of travel times enables a better monitoring of the performance of warehouse operations and can support practitioners in the choice of the configuration not only in terms of kind of cycle, but also from a policy assignment perspective. From a theoretical point of view, this work can be considered as an attempt to refine the existing methods to compute travel times

A Critical Examination on the Ability of BIM-based Software to Provide a More Accurate Building Energy Rating (BER) for New Dwellings in Ireland When Compared Against the DEAP Software

In this paper, a simulation-based approach was taken to perform sensitivity anal-ysis on building energy consumption datasets. The aim of the analysis was to assess the capa-bility of BIM based software for BER certification of new housing units in Ireland. The simu-lations involved the creation of three distinct model houses, repeated in three different software packages. The two BIM software packages chosen for assessment were Autodesk Revit for model construction and energy analysis through the Autodesk Insight plugin, and IES VE for model construction and energy simulation. The Dwelling Energy Assessment Procedure (DEAP) software, approved by the Sustainable Energy Authority of Ireland (SEAI) for BER certification in Ireland, was the third software package analysed in the research. The modelling approach was to select distinctive parameters within the three initial houses and to alter them in such a way as to create model iterations that would have different energy performance characteristics. This process was implemented to create 26 distinct models through the DEAP and Autodesk Revit software, and 46 model houses within IES VE. The study found that the most influential parameters for building energy performance are related to building location, occupancy patterns, and space heating schedules. These are pa-rameters that are not currently assessed in the DEAP methodology and are therefore not ed-itable within the DEAP software but can be modelled and assessed within both BIM software suites. Other influential parameters found within the study relate to overall building size, and coverage of primary heating zones. The study found that weather station data plays a key factor in overall energy performance. The DEAP software was found to be extremely limited with standardized weather data used and actual building location not accounted for, with an annual mean external temperature used for space and water heating simulations. The BIM software tools were both capable of utilizing any obtainable weather station data to simulate localized conditions, utilizing the annual vari-ances recorded in regional weather stations. The DEAP software was found to lack the necessary capabilities to model the shape and form of a building, hampering its analysis of deep or shallow floor plans which have an impact on solar gains and daylighting analysis. Both BIM packages were found be capable of designing to any conceivable shape and design

Home-bias in online fundraising: an analysis of international reward-based crowdfunding

Home Bias is the recognized tendency of individuals to choose geographically proximate interaction partners. In business finance, Home Bias is to the detriment of both investors and entrepreneurs because it promotes an uneven distribution of capital and contributes to the Global Finance Gap. The aim of this thesis is to examine the existence of Home Bias in the emerging financing channel of reward-based crowdfunding. Crowdfunding, in general, is different from traditional financing because it shifts the entire fundraising process to a digital space on the internet. Moreover, it introduces new community-based trust mechanisms and eliminates some of the distance-related costs. The focus of this thesis lies on reward-based crowdfunding, which is currently the most popular, unrestricted and, therefore, most international form of crowdfunding. To assess whether international reward-based crowdfunding is prone to Home Bias, this thesis employs a Negative Binomial regression model that examines the relationship between the count of crowdfunding project backers and their respective distance to entrepreneurs. The model builds on an aggregate data sample of 1,118,654 project-specific country-to-country investment observations (from 211,695 projects) that occurred on Kickstarter platform between 2009 and 2020, making it the largest and most up to date crowdfunding study. Although large sample or “Big Data” models provide many advantages (e.g., higher representativeness), and have been commonly used in the crowdfunding literature, they however introduce some caveats that have been mostly ignored by previous research. One main issue that might distort results in Big Data models is that they are capable to identify marginally small patterns in the data that, although statistically significant in terms of p-values, might have little relevance in practice. Therefore, this thesis goes beyond the traditional analysis of statistical significance and devotes great attention to the assessment of different marginal effect sizes to identify the practical relevance of findings. The thesis also investigates the effect of additional variables that may have potential effect on the count of backers namely GDP per capita of backers and entrepreneurs, project category, third-party endorsements, herding behaviour and Covid-19 pandemic. The results suggest that although geographical distance appears to have a statistically significant negative influence on the count of backers, its practical effect is very small. This indicates that Home Bias has a comparably small relevance in international reward-based crowdfunding and that entrepreneurs should not overestimate its impact when planning their crowdfunding campaigns. Moreover, neither individual wealth of backers nor entrepreneurs, project category or global economic crises seem to affect the success of crowdfunding campaigns in a practically relevant manner. However, herding behaviour and third-party endorsements do seem to have a statistically and practically relevant influence on the count of backers and, therefore, should be considered in the planning of crowdfunding campaigns. The overall findings of this thesis suggest that some of the prior research in crowdfunding might have overestimated the practical relevance of certain influencing factors (e.g., geographical distance and individual wealth), perhaps by focusing too much on statistical significance while ignoring the capability of Big Data models to identify marginally small and practically irrelevant patterns in the data