Browsing Dept. of Industrial Engineering - Ph.D. / Sc.D. by Subject "Benders decomposition"
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Item Open AccessEnergy management in plug-in hybrid electric vehicle penetrated networks(Bilkent University, 2016-04) Arslan, OkanWith the introduction of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) into the transportation system, a new line of research has emerged in the literature that reconsiders existing problems from the electrified transportation point of view. In this context, our objective is to understand the challenges that arise with the emergence of PHEV technology through a series of essays. Due to their ability to use electricity and gasoline as sources of energy with di↵erent cost structures and limitations, PHEVs stand as both a challenge and an opportunity for the existing transportation systems. They provide transportation cost reductions by utilizing less gasoline, which in turn contribute to the environmental benefits. In this context, we addressed a practically important problem: ‘finding the minimum cost path for PHEVs’. We formally present this problem, show that it is NP-Complete and propose exact and heuristic solution techniques. Using these techniques, we investigate impacts of battery characteristics, driver preferences and road network features on travel costs of a PHEV for long-distance trips. Through this analysis, the location of charging stations is identified as one of the critical factors a↵ecting the costs. In this regard, we introduce another practically important problem: ‘Hybrid charging station location’. Di↵erent than existing approaches to the charging station location problems, we also consider PHEVs when locating stations. We propose a Benders Decomposition algorithm as an exact solution methodology, and accelerate the implementation by generating nondominated cuts. Finally, we analyze the cost and emission impacts of PHEV penetration into electricity networks with widespread adoption of distributed energy resources. Approaching PHEVs from a long-distance point of view, we introduced new problems and solution approaches to the literature. Our results show that by establishing an adequate level of the intercity charging station infrastructure, wellstudied benefits of electrified transportation in urban regions can be extended to long-distance trips. Item Open AccessNonlinear mixed integer programming models and algorithms for fair and efficient large scale evacuation planning(Bilkent University, 2015-07) Bayram, VedatShelters are safe facilities that protect a population from possible damaging effects of a disaster. Traffic management during an evacuation and the decision of where to locate the shelters are of critical importance to the performance of an evacuation plan. From the evacuation management authority's point of view, the desirable goal is to minimize the total evacuation time by computing a system optimum (SO). However, evacuees may not be willing to take long routes enforced on them by a SO solution; but they may consent to taking routes with lengths not longer than the shortest path to the nearest shelter site by more than a tolerable factor. We develop a model that optimally locates shelters and assigns evacuees to the nearest shelter sites by assigning them to shortest paths, shortest and nearest with a given degree of tolerance, so that the total evacuation time is minimized. As the travel time on a road segment is often modeled as a nonlinear function of the ow on the segment, the resulting model is a nonlinear mixed integer programming model. We develop a solution method that can handle practical size problems using second order cone programming techniques. Using our model, we investigate the trade-of between efficiency and fairness. Disasters are uncertain events. Related studies and real-life practices show that a significant uncertainty regarding the evacuation demand and the impact of the disaster on the infrastructure exists. The second model we propose is a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to shelters and paths to minimize the expected total evacuation time, under uncertainty. The model considers the uncertainty in the evacuation demand and the disruption in the road network and shelter sites. We present a case study for an impending earthquake in Istanbul, Turkey. We compare the performance of the stochastic programming solutions to solutions based on single scenarios and mean values. We also propose an exact algorithm based on Benders decomposition to solve the stochastic problem. To the best of our knowledge, ours is the first algorithm that uses duality results for second order cone programming in a Benders decomposition setting. We solve practical size problems with up to 1000 scenarios in moderate CPU times. We investigate methods such as employing a multi-cut strategy, deriving pareto-optimal cuts, using a reduced primal subproblem and preemptive priority multiobjective program to enhance the proposed algorithm. Computational results confirm the efficiency of our algorithm. This research is supported by TUBITAK, The Scientific and Technological Research Council of Turkey with project number 213M434.