Adaptive community detection incorporating topology and content in social networks^✰

© 2018 In social network analysis, community detection is a basic step to understand the structure and function of networks. Some conventional community detection methods may have limited performance because they merely focus on the networks’ topological structure. Besides topology, content information is another significant aspect of social networks. Although some state-of-the-art methods started to combine these two aspects of information for the sake of the improvement of community partitioning, they often assume that topology and content carry similar information. In fact, for some examples of social networks, the hidden characteristics of content may unexpectedly mismatch with topology. To better cope with such situations, we introduce a novel community detection method under the framework of non-negative matrix factorization (NMF). Our proposed method integrates topology as well as content of networks and has an adaptive parameter (with two variations) to effectively control the contribution of content with respect to the identified mismatch degree. Based on the disjoint community partition result, we also introduce an additional overlapping community discovery algorithm, so that our new method can meet the application requirements of both disjoint and overlapping community detection. The case study using real social networks shows that our new method can simultaneously obtain the community structures and their corresponding semantic description, which is helpful to understand the semantics of communities. Related performance evaluations on both artificial and real networks further indicate that our method outperforms some state-of-the-art methods while exhibiting more robust behavior when the mismatch between topology and content is observed

A two-stage approach to ridesharing assignment and auction in a crowdsourcing collaborative transportation platform.

Collaborative transportation platforms have emerged as an innovative way for firms and individuals to meet their transportation needs through using services from external profit-seeking drivers. A number of collaborative transportation platforms (such as Uber, Lyft, and MyDHL) arise to facilitate such delivery requests in recent years. A particular collaborative transportation platform usually provides a two sided marketplace with one set of members (service seekers or passengers) posting tasks, and the another set of members (service providers or drivers) accepting on these tasks and providing services. As the collaborative transportation platform attracts more service seekers and providers, the number of open requests at any given time can be large. On the other hand, service providers or drivers often evaluate the first couple of pending requests in deciding which request to participate in. This kind of behavior made by the driver may have potential detrimental implications for all parties involved. First, the drivers typically end up participating in those requests that require longer driving distance for higher profit. Second, the passengers tend to overpay under a competition free environment compared to the situation where the drivers are competing with each other. Lastly, when the drivers and passengers are not satisfied with their outcomes, they may leave the platforms. Therefore the platform could lose revenues in the short term and market share in the long term. In order to address these concerns, a decision-making support procedure is needed to: (i) provide recommendations for drivers to identify the most preferable requests, (ii) offer reasonable rates to passengers without hurting driver’s profit. This dissertation proposes a mathematical modeling approach to address two aspects of the crowdsourcing ridesharing platform. One is of interest to the centralized platform management on the assignment of requests to drivers; and this is done through a multi-criterion many to many assignment optimization. The other is of interest to the decentralized individual drivers on making optimal bid for multiple assigned requests; and this is done through the use of prospect theory. To further validate our proposed collaborative transportation framework, we analyze the taxi yellow cab data collected from New York city in 2017 in both demand and supply perspective. We attempt to examine and understand the collected data to predict Uber-like ridesharing trip demands and driver supplies in order to use these information to the subsequent multi-criterion driver-to-passenger assignment model and driver\u27s prospect maximization model. Particularly regression and time series techniques are used to develop the forecasting models so that centralized module in the platform can predict the ridesharing demands and supply within certain census tracts at a given hour. There are several future research directions along the research stream in this dissertation. First, one could investigate to extend the models to the emerging concept of Physical Internet on commodity and goods transportation under the interconnected crowdsourcing platform. In other words, integrate crowdsourcing in prevalent supply chain logistics and transportation. Second, it\u27s interesting to study the effect of Uber-like crowdsourcing transportation platforms on existing traffic flows at the various levels (e.g., urban and regional)

Optimisation de laboratoires médicaux

This thesis focuses on the optimization of clinical laboratory design and operating decisions. A clinicallaboratory is an organization gathering human and machinery resources to analyze blood samples. Inthis thesis, a decision support tool including mathematical models, a heuristic algorithm and acustomized simulation model is developed to aid decision makers for the main strategic, tactical andoperational problems in clinical laboratory design and operations management. This decision supporttool follows a top-down stepwise framework starting from strategic problems and ending withoperational ones, including a recursive loop for modification and improvement. In this thesis, machineselection and facility layout are studied as the main strategic problems, analyzer configuration problemas the tactical problem, and assignment, aliquoting, and scheduling as the principal operationalproblems. In order to deal with machine selection problem for clinical laboratory, a mathematical modelis proposed which aids to select the most appropriate machines to equip the system. To tackle physicalarrangement of instruments within the laboratory area, a heuristic approach is developed. The proposedheuristic comprises the key constraints of laboratory layout design. To address the analyzerconfiguration problem which mainly deals with the assignment of chemical materials to the analyzersin clinical laboratory, a bi-objective mathematical model is developed. In addition, to determine anefficient assignment of sample tubes to the analyzers, a mathematical model with three objectives isproposed. A customized, flexible, and fine-grained simulation model is developed in FlexSim to studythe clinical laboratory designed through the outputs of developed mathematical models and layoutalgorithm. Simulation model plays a key role in the proposed framework as it is used for many purposes.The simulation model helps the designer to construct and analyze a complete clinical laboratory takinginto account all major features of the system. This simulation attribute provides the ability to scrutinizethe system behaviour and to find out whether the designed system is efficient. System performanceanalysis through simulation and resulting key performance indicators give helpful feedbacks for systemimprovement. Furthermore, simulation model can be fruitful to decide on scheduling, aliquoting andstaffing problems through the evaluation of various scenarios proposed by decision maker for each ofthese problems. To verify the validity of the proposed framework, data extracted from a real case isused. The output results seal on the applicability and the efficiency of the proposed framework as wellas competency of proposed techniques to deal with each optimization problem. To the best of ourknowledge, this thesis is one of the leading studies on the optimization of clinical laboratories.Cette thèse porte sur l'optimisation de la conception et des décisions opérationnelles des laboratoires d'analyses médicales. Dans cette thèse, un outil d'aide à la décision comprenant des modèles mathématiques, un algorithme heuristique et un modèle de simulation personnalisé est développé pour aider les décideurs à résoudre les principaux problèmes stratégiques, tactiques et opérationnels en conception et gestion des opérations des laboratoires d'analyses médicales. Dans cette thèse, la sélection des machines et la disposition des instruments sont étudiées en tant que principaux problèmes stratégiques, le problème de configuration des analyseurs en tant que problème tactique et l’affectation, l’aliquotage et l'ordonnancement en tant que principaux problèmes opérationnels. Un modèle de simulation personnalisé et flexible est développé dans FlexSim pour étudier le laboratoire d'analyse médicale conçu à l'aide des résultats de modèles mathématiques et d'un algorithme de layout développés. Le modèle de simulation aide le concepteur à construire et à analyser un laboratoire complet en tenant compte de toutes les principales caractéristiques du système. Cet attribut de simulation permet d'analyser le comportement du système et de déterminer si le système conçu est efficace. Pour vérifier la validité du cadre proposé, les données extraites d’un cas réel sont utilisées. Les résultats de sortie scellent l'applicabilité et l'efficacité du cadre proposé ainsi que la compétence des techniques proposées pour traiter chaque problème d'optimisation. À notre connaissance, cette thèse est l’une des principales études sur l’optimisation des laboratoires d'analyses médicales