Users’ time preference based stochastic resource allocation in cloud spot market: cloud provider’s perspective

Cloud Computing spot markets have enabled the users to make use of the spare computing capacities of the cloud providers at a relatively cheaper price which in turn has given the providers such as Amazon and Google an opportunity to earn extra money by auctioning-off the underutilized resources. However, resource availability is a problem in the spot market owing to spot-price fluctuations. Ignoring the customer’s preference is one of the potential reasons behind this. In this paper, we propose a time preference (value of service at different points of time) based stochastic integer linear programming model to allocate the cloud resources among the cloud users with a view to maximizing the revenue of cloud providers from the spot-market

A novel approach for exploring the trade-offs between several features of students’ well-being

We propose a novel approach to explore the trade-offs between four features of students’ well-being (anxiety, motivation, sense of belonging, and bullying). On the one hand, a multiobjective interval problem is formulated by considering these distinct components of well-being as objective functions, being then instantiated with confidence intervals obtained from distinct econometric estimations. Then, the problem is solved through the use of a reference point approach that allows accounting for the decision maker's preferences by considering a set of weight vectors that can be used to express his/her preferences regarding the importance that should be given to each objective function. The results provide information on how the improvement of one objective might affect the remaining objectives. Furthermore, the student's profile corresponding to each scrutinized solution is also made available. Overall, the results claim that bullying is the most affected objective, highlighting the need to foster antibullying education policies in Spanish schools, according to PISA 2015 data. Finally, some educational polices are suggested in order to enhance students’ well-being.This research was partly supported by the Spanish Ministry of Economy and Competitiveness (project ECO2017-88883-R) co-financed by FEDER funds and by the Fundação para a Ciência e a Tecnologia (FCT) under project grant UID/Multi/00308/2019. This work was also partly supported by the Andalusian Regional Ministry of Economy, Knowledge, Business and Univer- sity (PAI group SEJ-532 and UMA18-FEDERJA-024 also supported by FEDER funding). San- dra González-Gallardo is recipient of a technical research contract within “Sistema Nacional de Garantia Juvenil y del Programa Operativo de Empleo Juvenil 2014–2020 – Fondos FEDER.

Implications of Information Structure in Control of Urban Traffic Networks

69A3551747109Caltrans 65A0674 (TO-017)First, the authors consider optimal control of traffic flow over networks using a combination of variable speed limit, ramp meter, lane-changing, and routing control. While this problem has attracted significant attention, most of the prior work has been limited to centralized or open-loop control. The authors propose to develop the foundations for a framework to design closed-loop control under given information structures. The emphasis will be on computational tractability and characterization of performance gap with respect to centralized control. Second, the authors propose to study optimal information design to influence route choice decisions of drivers in dynamic environments. Specifically, the authors adopt the framework of algorithmic persuasion, under which the system planner can exploit information asymmetry about the knowledge of the real-time state of the network to release noisy information or recommend routes to the drivers in order to optimize social objective. The study of algorithmic persuasion in the context of routing games is very recent, and more so, the existing work implicitly assumes the drivers to evaluate the incentive compliant nature of the recommendations from the system planner only asymptotically, they do not consider externality from drivers who do not participate in persuasion, and assume static traffic flow models. In this project, the authors propose to address these shortcomings to develop foundations for algorithmic persuasion in routing games. The methodological contributions will be supplemented with case studies using traffic data from the Los Angeles area, and with simulation case studies in VISSIM

Linear Programming with interval right hand sides

In this paper, we study general linear programs in which right handsides are interval numbers. This model is relevant when uncertain and inaccurate factors make di±cult the assignment of a single value to each right handside. When objective function coefficients are interval numbers in a linear program, it is used to determine optimal solutions according to classical criteria coming from decision theory (like the worst case criterion). When the feasible solutions set is uncer- tain, another approach consists in determining the worst and best optimum solutions. We study the complexity of these two optimization problems when each right handside is an interval number. Moreover, we analysis the relationship between these two problems and the classical approach coming from decision theory. We exhibit some duality relation between the worst optimum solution problem and the best optimum solution problem in the dual. This study highlights some duality property in robustness analysis.ou