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To enable an intelligent traffic light system (ITLS) to consider the interactions between the signal controls and the traffic flow distribution resulting from the selfish-routing behaviors of travelers, a dynamic origin-destination (O-D) demand estimation model and a dynamic combined traffic assignment and signal control (CTA-SC) model are needed. However, the ITLS may collect inaccurate and incomplete...
Stackelberg Security Games (SSG) have been widely applied for solving real-world security problems -- with a significant research emphasis on modeling attackers' behaviors to handle their bounded rationality. However, access to real-world data (used for learning an accurate behavioral model) is often limited, leading to uncertainty in attacker's behaviors while modeling. This paper therefore focuses...
In this paper, based on the robust optimization techniques in Bertsimas and Sim[8], we propose a computationally tractable robust mean absolute deviation portfolio model. The purpose is to consider parameter uncertainty by controlling the impact of estimation errors on the portfolio strategy performance. The remarkable characteristic of the new method is that the robust optimization model retains...
Design of embedded systems involves a number of architecture decisions which have a significant impact on its quality. Due to the complexity of today's systems and the large design options that need to be considered, making these decisions is beyond the capabilities of human comprehension and makes the architectural design a challenging task. Several tools and frameworks have been developed, which...
Amazon Elastic Compute Cloud (EC2) provides a cloud computing service by renting out computational resources to customers (i.e., cloud users). The customers can dynamically provision virtual servers (i.e., computing instances) in EC2, and then the customers are charged by Amazon on a pay-per-use basis. EC2 offers three options to provision virtual servers, i.e., on-demand, reservation, and spot options...
Cloud providers can offer cloud consumers two plans to provision resources, namely reservation and on-demand plans. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, this resource provisioning is challenging due to the uncertainty. For example, consumers' demand and providers' resource prices can be fluctuated. Moreover, inefficiency of resource provisioning...
To minimize the total workload delayed in the terminal yard, an integrated model of yard space allocation and crane scheduling is proposed. To deal with the uncertainty in pick-up process of import containers, a robust optimization model is suggested to simultaneously determine the storage locations of import containers and the routes of yard crane movements. A Lagrange relaxation algorithm and a...
Traffic in communication networks fluctuates heavily over time. Thus, to avoid capacity bottlenecks, operators highly overestimate the traffic volume during network planning. In this paper we consider telecommunication network design under traffic uncertainty, adapting the robust optimization approach of [11]. We present three different mathematical formulations for this problem, provide valid inequalities,...
In this paper, we are interested in obtaining robust solutions to the Wounded Transfer Problem based on an absolute robustness criterion, in the background of large-scale emergencies such as typhoons, floods, earthquakes, droughts and so on. We firstly describe the related methodology, and then introduce a min-max robust multi-point transportation model with multi-type vehicles to minimize the maximum...
Model predictive control or model-based prediction control is one of the most powerful tools to robust plant control. The controller, particularly in vital and sensitive processes, requires a robust optimization method of cost function such as Quadratic Programming which is very time consuming. Packet Scheduling for data transmission networks is a useful way to achieve better QoS and bandwidth optimization...
The paper considers the application of Robust Optimization (RO) technique to model predictive control (MPC). Robust Optimization has received considerable attention recently as a result of the well accepted and proven interior-point method. The robust optimization has been investigated and applied to various application areas. It is of interest to see the application of robust optimization method...
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