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SCIP: solving constraint integer programs
, 2009
"... Constraint integer programming (CIP) is a novel paradigm which integrates constraint programming (CP), mixed integer programming (MIP), and satisfiability (SAT) modeling and solving techniques. In this paper we discuss the software framework and solver SCIP (Solving Constraint Integer Programs), wh ..."
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Constraint integer programming (CIP) is a novel paradigm which integrates constraint programming (CP), mixed integer programming (MIP), and satisfiability (SAT) modeling and solving techniques. In this paper we discuss the software framework and solver SCIP (Solving Constraint Integer Programs), which is free for academic and noncommercial use and can be downloaded in source code. This paper gives an overview of the main design concepts of SCIP and how it can be used to solve constraint integer programs. To illustrate the performance and flexibility of SCIP, we apply it to two different problem classes. First, we consider mixed integer programming and show by computational experiments that SCIP is almost competitive to specialized commercial MIP solvers, even though SCIP supports the more general constraint integer programming paradigm. We develop new ingredients that improve current MIP solving technology. As a second application, we employ SCIP to solve chip design verification problems as they arise in the logic design of integrated circuits. This application goes far beyond traditional MIP solving, as it includes several highly nonlinear constraints, which can be handled nicely within the constraint integer programming framework. We show anecdotally how the different solving techniques from MIP, CP, and SAT work together inside SCIP to deal with such constraint classes. Finally, experimental results show that our approach outperforms current stateoftheart techniques for proving the validity of properties on circuits containing arithmetic.
Supervisory hybrid model predictive control for voltage stability of power networks
 in: Proceedings of the IEEE American Control Conference
, 2008
"... Abstract — Emergency voltage control problems in electric power networks have stimulated the interest for the implementation of online optimal control techniques. Briefly stated, voltage instability stems from the attempt of load dynamics to restore power consumption beyond the capability of the tr ..."
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Abstract — Emergency voltage control problems in electric power networks have stimulated the interest for the implementation of online optimal control techniques. Briefly stated, voltage instability stems from the attempt of load dynamics to restore power consumption beyond the capability of the transmission and generation system. Typically, this situation occurs after the outage of one or more components in the network, such that the system cannot satisfy the load demand with the given inputs at a physically sustainable voltage profile. For a particular network, a supervisory control strategy based on model predictive control is proposed, which provides at discrete time steps inputs and setpoints to lowerlayer primary controllers based on the predicted behavior of a model featuring hybrid dynamics of the loads and the generation system.
Faster IntegerFeasibility in MixedInteger Linear Programs by Branching to Force Change
 ACCEPTED FOR PUBLICATION IN COMPUTERS AND OPERATIONS RESEARCH
, 2010
"... Branching in mixedinteger (or integer) linear programming requires choosing both the branching variable and the branching direction. This paper develops a number of new methods for making those two decisions either independently or together with the goal of reaching the first integerfeasible solut ..."
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Cited by 5 (1 self)
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Branching in mixedinteger (or integer) linear programming requires choosing both the branching variable and the branching direction. This paper develops a number of new methods for making those two decisions either independently or together with the goal of reaching the first integerfeasible solution quickly. These new methods are based on estimating the probability of satisfying a constraint at the child node given a variable/direction pair. The surprising result is that the first integerfeasible solution is usually found much more quickly when the variable/direction pair with the smallest probability of satisfying the constraint is chosen. This is because this selection forces change in many candidate variables simultaneously, leading to an integerfeasible solution sooner. Extensive empirical results are given.
Fast Model Predictive Control for Urban Road Networks via MILP
"... Fast model predictive control for urban road networks via MILP ∗ ..."
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Fast model predictive control for urban road networks via MILP ∗
MILP Software
"... This article concerns software for solving a general Mixed Integer Linear Program (MILP) in the form min{c T x: Ax ≥ b, x ≥ 0, xj ∈ Z ∀j ∈ I}. (1) The algorithmic approach relies on the iterative solution, through generalpurpose ..."
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This article concerns software for solving a general Mixed Integer Linear Program (MILP) in the form min{c T x: Ax ≥ b, x ≥ 0, xj ∈ Z ∀j ∈ I}. (1) The algorithmic approach relies on the iterative solution, through generalpurpose
THE ELECTRIC VEHICLE CHARGING STATION LOCATION PROBLEM: A PARKINGBASED ASSIGNMENT METHOD FOR SEATTLE
"... Access to electric vehicle (EV) charging stations will impact EV adoption rates, use decisions, electrified mile shares, petroleum demand, and power consumption across times of day. This work uses parking information from over 30,000 personaltrip records in the Puget Sound Regional Council’s 2006 h ..."
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Access to electric vehicle (EV) charging stations will impact EV adoption rates, use decisions, electrified mile shares, petroleum demand, and power consumption across times of day. This work uses parking information from over 30,000 personaltrip records in the Puget Sound Regional Council’s 2006 household travel survey to determine public (nonresidential) parking locations and durations. Regression equations predict parking demand variables (total vehiclehours per zone/neighborhood and parkedtime per vehicletrip) as a function of site accessibility, local jobs and population densities, trip attributes, and other variables available in most regions and travel surveys. Several of these variables are key inputs for a mixed integer programming problem, developed here for optimal EVchargingstation location assignments. The algorithm minimizes EV users ’ station access costs while penalizing unmet demand. This useful specification was used to determine top locations for installing a constrained number of charging stations within 10 miles of Seattle’s downtown, showing how charging location schemes ’ access costs respond to parking demand and station locations. The models developed here are generalizable to data sets available for most any region, and can be used to make more informed decisions on station locations around the world.
A Mapping Framework Based on Packing for Design Space Exploration of Heterogeneous MPSoCs ∗
"... The computational demand of signal processing algorithms is rising continuously. Heterogeneous embedded multiprocessor systemsonchips are one solution to satisfy this demand. But to be able to take advantage of these systems, new strategies are required to map applications to such a system and to ..."
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The computational demand of signal processing algorithms is rising continuously. Heterogeneous embedded multiprocessor systemsonchips are one solution to satisfy this demand. But to be able to take advantage of these systems, new strategies are required to map applications to such a system and to evaluate the systems performance at a very early design stage. We will present a framework for static, analytical, bottomup temporal and spatial mapping of applications to MPSoCs based on packing. This mapping framework allows easy performance evaluation and design space exploration of heterogeneous systems on chip. 1
DaytoDay Route Choice Control in Traffic Networks with TimeVarying Demand Profiles
"... Daytoday route choice control in traffic networks with timevarying demand profiles ∗ ..."
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Daytoday route choice control in traffic networks with timevarying demand profiles ∗
DYNAMIC RAILWAY NETWORK MANAGEMENT USING SWITCHING MAXPLUSLINEAR MODELS
"... Dynamic railway network management using switching maxpluslinear models ∗ T.J.J. van den Boom and B. De Schutter If you want to cite this report, please use the following reference instead: T.J.J. van den Boom and B. De Schutter, “Dynamic railway network management using switching maxpluslinear ..."
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Dynamic railway network management using switching maxpluslinear models ∗ T.J.J. van den Boom and B. De Schutter If you want to cite this report, please use the following reference instead: T.J.J. van den Boom and B. De Schutter, “Dynamic railway network management using switching maxpluslinear models, ” Proceedings of the 11th IFAC