Heat removal and power density distribution delivery have become two major reliability concerns in 3D stacked technology. In this paper, we propose a thermal-driven 3D floor-planner. Our contributions include: (1) a n...
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Heat removal and power density distribution delivery have become two major reliability concerns in 3D stacked technology. In this paper, we propose a thermal-driven 3D floor-planner. Our contributions include: (1) a novel multi-objective formulation to consider the thermal and performance constraints in the optimization approach; (2) an efficient mixed integer linear programming (MILP) representation of the floorplanning model; and (3) a smooth integration of the MILP model with an accurate thermal modelling of the architecture. The experimental work is conducted for two realistic many-core single-chip architectures: an homogeneous system resembling Intel's SCC, and an improved heterogeneous setup. The results show promising improvements of the mean, peak temperature and the thermal gradient, with a reduced overhead in the wire length of the system.
This paper presents the implementation of the energy storage for unit commitment and dispatch of conventional power plants. The optimization employs mixed integer linear programming. The simulations with different sce...
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This paper presents the implementation of the energy storage for unit commitment and dispatch of conventional power plants. The optimization employs mixed integer linear programming. The simulations with different scenarios have been carried out to demonstrate the effectiveness of the approach applied. The results obtained indicated that start-up energy of conventional power plants may impose the specific operation of energy storage systems. Cost benefits of energy storage implementation are also demonstrated.
By splitting unit commitment into a coupling problem and individual unit commitment subproblems, Lagrangian relaxation is very effective in decreasing the solving time of large scale problems. Consequently, unit commi...
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By splitting unit commitment into a coupling problem and individual unit commitment subproblems, Lagrangian relaxation is very effective in decreasing the solving time of large scale problems. Consequently, unit commitment subproblems should be solved equally fast. If they have been originally formulated in mixed-integerlinearprogramming, performance advantages can be expected by replacing it with dynamic programming. However, it has not been reported whether a one-to-one reformulation is feasible. We suggest approaches to choose states for dynamic programming that replicate equal solutions as well as measures to reduce memory requirements. Results of the two subproblem models differ only fractionally and for explicable reasons. Subproblem computation time has been reduced by up to 100 times at modest memory requirements.
In this study,a developed inexact community-scale energy model has been developed for planning renewable energy management systems under *** allows uncertainties presented as both probability distributions and interva...
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In this study,a developed inexact community-scale energy model has been developed for planning renewable energy management systems under *** allows uncertainties presented as both probability distributions and interval values to be incorporated with in a general optimization *** method is based on an integration of the existing possibility degree method for interval numbers,the satisfaction degree method for stochastic variables and mixed integer linear programming *** can also facilitate capacityexpansion planning for energy-production facilities within a multi-period and multi-option *** in energy management systems can be systematically reflected,thus applicability of the modeling process can be highly *** model has then been applied to a case of long-term renewable energy management planning for three *** solutions for the planning of energy management systems have been generated.
This paper addresses the scheduling problem in port of Tripoli-Lebanon for a single quay crane with multiple yard trucks, all containers that will be unloaded from the vessel are in the same bay. The objective is to r...
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This paper addresses the scheduling problem in port of Tripoli-Lebanon for a single quay crane with multiple yard trucks, all containers that will be unloaded from the vessel are in the same bay. The objective is to reduce the completion time of all containers from the vessel to their store location, we used a mixed integer linear programming and a dynamic programming algorithm to solve the problem. Finally, we have compared and validated our results on real instances from the port.
The problem of aggregating large data files from distributed databases and address the corresponding challenges involved from a network architecture perspective is considered. We model this problem as one of identifyi...
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The problem of aggregating large data files from distributed databases and address the corresponding challenges involved from a network architecture perspective is considered. We model this problem as one of identifying a time-path schedule (TPS) in a graph representation of the network. We prove that the TPS problem (TPSP) is NP-complete. We then propose a mixed integer linear programming (MILP)-based approach and three heuristics longest-file-first (LFF), disjoint-paths (DP), and most-distant-file-first (MDFF) - to solve TPSP.
We consider the reconfiguration problem in multi- fiber WDM optical networks. In a network with evolving traffic, the virtual topology may not remain optimal, leading to degradation of network performance. However, ad...
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We consider the reconfiguration problem in multi- fiber WDM optical networks. In a network with evolving traffic, the virtual topology may not remain optimal, leading to degradation of network performance. However, adapting the virtual topology to the changing traffic may lead to service disruption. This optimization problem hence captures the trade-off between network performance and number of reconfigurations applied to the virtual topology. This trade-off is considered via a multi- metrics approach. The above problem is solved through a mixed integer linear programming (MILP) formulation with a multivariate objective function. However the problem is NP-hard and such an approach is unable to solve large problem instances in a reasonable time. Therefore we propose a simulated annealing (SA) based heuristic approach for solving problems of higher complexity. We compare the performance and the computation time of solving the MILP model and the heuristic approach considering different test instances. We can find near optimal solutions for instances of medium complexity using the MILP model. The SA scheme can be used as a heuristic to arrive at near optimal solutions when the run-time of the MILP becomes practically infeasible. It also appears that the trade-off's involved in the reconfiguration problem cannot be left aside, as a little flexibility with respect to one metric allows to drastically improve the quality of the solution with respect to other metrics.
Demand response is becoming a rational option to guarantee system reliability and reduce the investment. To tackle the possible overload caused by wind power uncertainty and N-1 contingency, a stochastic mixedinteger...
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Demand response is becoming a rational option to guarantee system reliability and reduce the investment. To tackle the possible overload caused by wind power uncertainty and N-1 contingency, a stochastic mixed integer linear programming model is proposed first. In addition, extreme scenario method is utilized to guarantee the robustness of planning scheme. The proposed method is validated in the modified Garver 6-bus system with practical scenario input. The simulation results demonstrate that the demand response helps to relieve the power flow overload and reducing transmission line investment.
In this paper, we consider a variant of the transportation problem where any demand may be dropped off elsewhere than at its destination, picked up later by the same or another vehicle, and so on until it has reached ...
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In this paper, we consider a variant of the transportation problem where any demand may be dropped off elsewhere than at its destination, picked up later by the same or another vehicle, and so on until it has reached its destination. We present two mixed integer linear programming formulations based on a space-time graph. We also develop a branch-and-cut algorithm for the problem and present some computational results
Energy conservation is a critical issue in wireless multi-hop networks since the nodes are powered by batteries only. One major metric for energy conservation is to route a communication session along the routes which...
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Energy conservation is a critical issue in wireless multi-hop networks since the nodes are powered by batteries only. One major metric for energy conservation is to route a communication session along the routes which requires the lowest total energy consumption. In this paper, based on the concepts of virtual relay, we present a constraint formulation for the minimum-energy broadcast routing problem in terms of mixed integer linear programming (MIP). Moreover the model extends its analytical capability into more general scenarios in asymmetric networks, where few studies have addressed the broadcast routing problem before. The experiment results show that in a typical multi-hop network with node less than 50, optimal solution can be always solved in a timely manner, and save up to 30% power consumption compared to the most efficient heuristic algorithm BIP known so far.
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