This paper presents a three-stage optimization algorithm for solving two-stage robust decision making problems under uncertainty with min-max regret objective. The structure of the first stage problem is a general mixed integer (binary) linear programming model with a specific model of uncertainty that can occur in any of the parameters, and the second stage problem is a linear programming model. Each uncertain parameter can take its value from a finite set of real numbers with unknown probability distribution independently of other parameters' settings. This structure of parametric uncertainty is referred to in this paper as the full-factorial scenario design of data uncertainty. The proposed algorithm is shown to be efficient for solving large-scale min-max regret robust optimization problems with this structure. The algorithm coordinates three mathematical programming formulations to solve the overall optimization problem. The main contributions of this paper are the theoretical development of the three-stage optimization algorithm, and improving its computational performance through model transformation, decomposition, and pre-processing techniques based on analysis of the problem structure. The proposed algorithm is applied to solve a number of robust facility location problems under this structure of parametric uncertainty.All results illustrate significant improvement in computation time of the proposed algorithm over existing approaches.
In this paper, we describe a dynamic network optimization-based solution framework that effectively integrates the management of complex information with traffic strategies and logistics support. This framework is composed of a transportation management function, a logistics support and capacity refinement function, and a validation function. The transportation management function is central to this solution framework and is mainly composed of four modules; a Rough-
Cut Capacity Plan (RCCP), a Detailed Capacity Plan (DCP), a Restricted Evacuation Plan (REP) and an Enforced Evacuation Plan (EEP). This research has been motivated by the challenges experienced during the hurricane Rita evacuation of the City ofHouston. This tool is expected to provide an intelligent systematic methodology that can be used by transportation planners and emergency agencies to improve traffic management during hurricane evacuation, enhance public safety, and mitigate the overall economic impact of evacuation.
We conclude that a carefully planned, comprehensive, appropriately enforced protocol is necessary to reduce the rate of thromboembolic events. Practical safety measures and technical recommendations are presented that strongly encourage the use of thromboprophylaxis during the pre-, intra-, and postoperative phases of aesthetic surgical procedures. We feel that DVT and PE prevention should involve a partnership between patient and surgeon.
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