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This research presents a decision support methodology for the multi-criteria supplier selection and order allocation problem. The proposed approach supports purchasing managers in assembling mid-term supplier portfolios while making them aware of the trade-offs between the supplier sustainability, the purchasing costs, and the overall supply risk. First, we propose a multi-objective optimization model with three objectives: to maximize the supplier sustainability, to select the supplier portfolio with the lowest purchasing costs, and to minimize the supply risk. Our model extends existing mathematical approaches that follow the portfolio theory fathered by H. Markowitz by integrating the aspect ‘risk’ into the supplier selection problem. Secondly, since we allow for integer variables in our model—in contrast to the classical Markowitz portfolio theory—we use the ε-constraint method to visualize the efficient surface. The possibility of considering the non-dominated set of supplier portfolios is advantageous for purchasing managers as they gain a picture of the different optimal supplier portfolios and are able to analyze the trade-offs between the different purchasing goals before making a decision. Finally, we illustrate the applicability of the proposed methodology in a real-world supplier selection and order allocation case from the automotive industry. In the example case, we identify 1754 optimal supplier portfolios that may be assembled based on the eight available suppliers. Our analyses show that each optimal portfolio consists of two suppliers, with one specific supplier being included in each portfolio. Furthermore, four suppliers are not part of any optimal solution.
At present, there is no globally accepted standard for the allocation of greenhouse gas (GHG) emissions to shipments in road freight transportation. The only official international standard for emission calculation of transport operations is the European Norm EN-16258. However, even this norm still allows choosing from several alternative emission allocation schemes. This research aims to harmonize the process of GHG declarations for supply chains by identifying among all allocation units specified by EN-16258 the one that describes a shipment's contribution to GHG emissions best. For this purpose, concepts of the cooperative game theory are used. First, we develop three transport scenarios that allow studying a shipment's effect on GHG: a vehicle routing problem, a network flow model, and a mixed scenario. Our approach extends previous research projects because we take into account that shipment characteristics in terms of origin, destination, weight, and volume consume transport capacities to different degrees, impact the routing of the commercial vehicles and, thus, determine GHG. Second, we present the results of a computational study that bases on the introduced transport scenarios and that compares the allocation vectors resulting from the EN-16258 allocation rules with the Shapley value, which serves as a benchmark for a shipment's contribution to GHG. Furthermore, we show how often the EN-16258 allocation principles are in line with a set of game theoretical fairness criteria. The results indicate that the allocation unit ‘distance’ is the closest to the game theory benchmark and most often in line with game theoretical fairness criteria.
This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate ‘risk’ into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.
Virtual teams that use integrated communication technologies are ubiquitous in cross-border collaboration. This study explored media use and communication performance in multilingual virtual teams. Based on surveys from 96 virtual teams (with 578 team members), the research showed that more time spent in synchronous communication channels such as online conferences increased inclusion and satisfaction. Team members with lower language proficiency felt less included in synchronous and asynchronous collaboration, whereas team members with higher language proficiency felt less satisfied with asynchronous collaboration. Also, limited language proficiency speakers were significantly less likely to view synchronous tools as helpful for their teams to reach a mutual decision. Our data supports Media Synchronicity Theory (MST) for native and highly proficient English speakers. However, MST needs to be adjusted to account for different levels of language proficiency.
Purpose
The purpose of this paper is to investigate how professional football clubs from the English Premier League, German Bundesliga and Spanish Primera División use digital media to expand their international reach in emerging football markets (EFM) outside of Europe. Based on the EPRG framework and Rugman’s home-region hypothesis, the aim is to broaden the perspective where “sports go global” for a further understanding of actors’ international orientation in the digital sphere.
Design/methodology/approach
The study is based on data from desk research and a qualitative survey, comprising information on international digital media activities of 58 European clubs. Cluster analysis is used to identify different international orientations with regard to digital media activities.
Findings
The data provide evidence that clubs differ strongly in their orientations towards EFM. While some global players that provide digital media content in several EFM languages and attract a large share of Facebook followers from EFM exist, other clubs focus on their home region. League-specific differences become apparent.
Originality/value
This study determines the international online orientations of European football clubs by combining two previously separated research streams in football management studies: internationalisation and digital media activities. Most clubs with a strong EFM fan base choose polycentric, multi-language digital media strategies, followed by geocentric, standardised approaches. By offering a novel angle on internationalisation in professional football, this study contributes towards optimising clubs’ international online strategies for EFM, which are markets that promise high growth rates.
In modern medicine, Clinical Practice Guidelines (CPGs) are well-established resources for the appropriate treatment of diseases. Evidence-based CPGs contain recommendations which are based on the state of the art and which have been achieved by consensus of several experts. Nevertheless, there is a potential for problems in translating guideline documents into specific actions for physicians. Therefore we propose to formalize the treatment process in an understandable representation as UML activities together with a domain expert. This formalization serves as a basis for the transfer of knowledge into a model, in this case PROforma, which directly allows execution in an interactive assistance software. The results of this work are part of an ongoing research project on the treatment of colon cancer based on the corresponding evidence-based CPG.
Probabilistic Estimation of Human Interaction Needs in Context of a Robotic Assistance in Geriatrics
(2019)
The key purpose of assistance robots is to help people coping with work-related or everyday tasks. To ensure an intuitive and effective support by an assistance robot, its expectation conform behavior is essential. In particular, when using assistance robots in geriatrics to assist elderly patients, special attention to the human-robot interaction should be paid. In order to help elderly patients maintain their independence and abilities as much as possible, the robot should only intervene when its support is needed. Therefore, the continuous estimation of the patient’s need for interaction is of particular importance. For enabling suitable models to estimate this need, we elaborate the use of Bayesian Networks. The analysis of our results seems promising, yielding a robust and practical approach.
With the digital transformation of companies, ever larger amounts of data are generated and available for analysis. Process mining techniques can be used to extract and analyze process models from these data. Related techniques have quickly developed into an important field with constantly increasing investments in recent years. Thus, the automated analysis of processes has gained an important role in many companies. In this context, graphs have been shown to be an intuitive representation of how the gathered processes are carried out using the aforementioned techniques. For the analysis of these so-called control flow graphs, we investigate the use of convolution neural networks, which are specially designed for graphs: graph convolution networks (GCNs). In our contribution, GCNs are used to perform a regression task based on individual control flows of a process in which farmers apply for specific governmental payments. The approach achieved promising results on this publicly available data set.
This thesis investigates the measurement and prediction of machinery noise in timber-frame buildings. To quantify the structure-borne sound power input from multi-point sources, simplified approaches were assessed that reduce the required data for out-of-plane force excitation. This identified approaches that give estimates within ±5 dB from 20 Hz to 2000 Hz. To investigative the importance of out-of-plane moment excitation, inverse methods were used to determine the power input; these were affected by noise but processing was used to overcome this shortcoming. A series of experimental investigations were carried out on a timber-frame structure undergoing mechanical point excitation. The driving-point mobility showed orthotropic plate characteristics at low frequencies, ribbed-plate characteristics in a narrow frequency band and infinite plate characteristics in mid- and high-frequency ranges. The moment mobility above or in-between studs was similar to infinite beam or plate theory with interpolation between these theories where necessary. The experimental work indicated the potential to use Statistical Energy Analysis (SEA) to predict sound transmission. The first experimental finding was that above the mass-spring-mass resonance frequency, the vibrational response of the wall leaves was uncorrelated. The second was a significant decrease in vibration across the wall from the excitation point, with structural intensity showing a decrease in net power flow across successive timber studs. The third was that tongue and groove connections between chipboard sheets significantly reduce the vibration transmission above 500 Hz. This led to different SEA models being used to model a timber-frame wall undergoing mechanical point excitation. A 41-subsystem model was found to be necessary to reproduce the measured vibration levels on both leaves within 10 dB. As there is a significant decrease in vibration with distance in the mid- and high-frequency range, the region close to the excitation point is particularly important and the SEA model has better accuracy in this region. An alternative engineering approach to the prediction of machinery noise in timber-frame buildings was introduced and validated that used measured transmission functions between the injected power and the spatial-average sound pressure level in a receiving room. A field survey and case studies indicate this is a feasible and practical approach.
Providing a subset of previously studied items as retrieval cues can both impair and improve memory for the remaining items. Here, we investigated such part-list cuing effects in younger and older adults’ episodic recall, using listwise directed forgetting to manipulate study context access at test. When context access was maintained, part-list cuing impaired recall regardless of age. In contrast, when context access was impaired, part-list cuing improved recall in younger but not in older adults. The results are consistent with the proposal that older adults show intact inhibition and blocking of competing information, but reduced capability for episodic context reactivation.