Stanford University Department of Operations Research
Overview
Works:  89 works in 95 publications in 1 language and 158 library holdings 

Genres:  Periodicals 
Roles:  Researcher 
Classifications:  HD9502, 333.82 
Publication Timeline
.
Most widely held works by
Stanford University
Energy transition strategies : a progress report by
Stanford University(
Book
)
2 editions published in 1979 in English and held by 9 WorldCat member libraries worldwide
2 editions published in 1979 in English and held by 9 WorldCat member libraries worldwide
ETAMACRO : a model of energyeconomy interactions by
Alan Sussmann Manne(
Book
)
1 edition published in 1977 in English and held by 7 WorldCat member libraries worldwide
1 edition published in 1977 in English and held by 7 WorldCat member libraries worldwide
ETAMACRO : a progress report(
Book
)
1 edition published in 1983 in English and held by 7 WorldCat member libraries worldwide
1 edition published in 1983 in English and held by 7 WorldCat member libraries worldwide
ETAMACRO, a user's guide : interim report by
Alan Sussmann Manne(
Book
)
1 edition published in 1981 in English and held by 6 WorldCat member libraries worldwide
1 edition published in 1981 in English and held by 6 WorldCat member libraries worldwide
A compact inverse scheme applied to multicommodity network with resource constraints by
Steven F Maier(
Book
)
1 edition published in 1971 in English and held by 3 WorldCat member libraries worldwide
1 edition published in 1971 in English and held by 3 WorldCat member libraries worldwide
Multiple channel queues in heavy traffic by
Donald L Iglehart(
Book
)
1 edition published in 1969 in English and held by 3 WorldCat member libraries worldwide
1 edition published in 1969 in English and held by 3 WorldCat member libraries worldwide
Computing equilibrium compositions of ideal chemical systems by
J. H Bigelow(
Book
)
1 edition published in 1970 in English and held by 3 WorldCat member libraries worldwide
1 edition published in 1970 in English and held by 3 WorldCat member libraries worldwide
Markov maintenance models with repair by
Y Hatoyama(
Book
)
1 edition published in 1976 in English and held by 3 WorldCat member libraries worldwide
In this study discrete time finite state Markov maintenance models are investigated. In each model, a machine is assumed to be operating over time with its condition deteriorating as time goes on. The state of the machine is observed at the beginning of a period. An operating machine can be sent to a repair shop at this time, whereas a failed machine must be repaired. When a machine is being repaired, the number of time periods that it is unavailable is usually assumed to have a geometric distribution. A repaired machine becomes available in its best state. An operating cost is charged while a machine is operating, and material and labor costs are charged when it is being repaired. The objective is to find a policy which minimizes the total expected alphadiscounted cost or the longrung average cost. Special emphasis is being placed on finding sufficient conditions to assure that a control limit policy is optimal. The aforementioned model had only one machine in the system. Models with spare machines in the system are next studied. For these models a penalty cost is added when the system fails (only when all machines are inoperative). (Author)
1 edition published in 1976 in English and held by 3 WorldCat member libraries worldwide
In this study discrete time finite state Markov maintenance models are investigated. In each model, a machine is assumed to be operating over time with its condition deteriorating as time goes on. The state of the machine is observed at the beginning of a period. An operating machine can be sent to a repair shop at this time, whereas a failed machine must be repaired. When a machine is being repaired, the number of time periods that it is unavailable is usually assumed to have a geometric distribution. A repaired machine becomes available in its best state. An operating cost is charged while a machine is operating, and material and labor costs are charged when it is being repaired. The objective is to find a policy which minimizes the total expected alphadiscounted cost or the longrung average cost. Special emphasis is being placed on finding sufficient conditions to assure that a control limit policy is optimal. The aforementioned model had only one machine in the system. Models with spare machines in the system are next studied. For these models a penalty cost is added when the system fails (only when all machines are inoperative). (Author)
Drews institutionalized divvy economy by
George B Dantzig(
Book
)
1 edition published in 1973 in English and held by 2 WorldCat member libraries worldwide
1 edition published in 1973 in English and held by 2 WorldCat member libraries worldwide
Constructing a Minimal Cost Spanning Tree Subject to Resource Constraints and Flow Requirements by
Andrew W Shogan(
Book
)
2 editions published in 1981 in English and held by 1 WorldCat member library worldwide
Consider a network in which one node is a source having an infinite supply of a commodity, and every other node is a sink having a known constant demand. Furthermore, associated with each potential arc of the network are the following known constants: the cost of constructing the arc, the amount of each scarce resource consumed during the construction of the arc, and the flow capacity of the arc. Given the above known constants as well as the available supply of each of the scarce resources, the problem is to construct a minimal cost spanning tree subject to the limitations on the consumption of the scarce resources and the requirement that there exists a flow from the source satisfying the demands at the sinks without exceeding any arc capacity. This paper discusses the solution of such a problem by a branchandbound algorithm base on Lagrangean relaxation. Also included are applications of the problem, extensions to the problem, and a report on preliminary computational experience with a computer implementation of the algorithm. (Author)
2 editions published in 1981 in English and held by 1 WorldCat member library worldwide
Consider a network in which one node is a source having an infinite supply of a commodity, and every other node is a sink having a known constant demand. Furthermore, associated with each potential arc of the network are the following known constants: the cost of constructing the arc, the amount of each scarce resource consumed during the construction of the arc, and the flow capacity of the arc. Given the above known constants as well as the available supply of each of the scarce resources, the problem is to construct a minimal cost spanning tree subject to the limitations on the consumption of the scarce resources and the requirement that there exists a flow from the source satisfying the demands at the sinks without exceeding any arc capacity. This paper discusses the solution of such a problem by a branchandbound algorithm base on Lagrangean relaxation. Also included are applications of the problem, extensions to the problem, and a report on preliminary computational experience with a computer implementation of the algorithm. (Author)
New approaches to linear and nonlinear programming. Progress report, January 1, 1988December 31, 1988(
)
1 edition published in 1988 in English and held by 0 WorldCat member libraries worldwide
This report describes technical progress during the past twelve months on DOE Contract DEFG87ER25030 and requests support for the third year. The project involves study of the theoretical properties and computational performance of techniques that solve linear and nonlinear programs by means of nonlinear transformations. The group at the Systems Optimization Laboratory (SOL) were the first to recognize the connection between Karmarkar's projective method and the logarithmic barrier method. It is now generally recognized that essentially all interiorpoint methods for linear programming inspired by Karmarkar's method are closely related to application of Newton's method to a sequence of barrier functions. Each barrier function is defined from the objective function and a barrier term that is infinite along the boundary of the feasible region. As the weight on the barrier term is reduced to zero, the solution of the subproblem becomes closer to the solution of the original problem
1 edition published in 1988 in English and held by 0 WorldCat member libraries worldwide
This report describes technical progress during the past twelve months on DOE Contract DEFG87ER25030 and requests support for the third year. The project involves study of the theoretical properties and computational performance of techniques that solve linear and nonlinear programs by means of nonlinear transformations. The group at the Systems Optimization Laboratory (SOL) were the first to recognize the connection between Karmarkar's projective method and the logarithmic barrier method. It is now generally recognized that essentially all interiorpoint methods for linear programming inspired by Karmarkar's method are closely related to application of Newton's method to a sequence of barrier functions. Each barrier function is defined from the objective function and a barrier term that is infinite along the boundary of the feasible region. As the weight on the barrier term is reduced to zero, the solution of the subproblem becomes closer to the solution of the original problem
New approaches to linear and nonlinear programming(
)
1 edition published in 1988 in English and held by 0 WorldCat member libraries worldwide
This report describes technical progress during the past twelve months on DOE Contract DEFG87ER25030 and requests support for the third year. The project involves study of the theoretical properties and computational performance of techniques that solve linear and nonlinear programs by means of nonlinear transformations. The group at the Systems Optimization Laboratory (SOL) were the first to recognize the connection between Karmarkar's projective method and the logarithmic barrier method. It is now generally recognized that essentially all interiorpoint methods for linear programming inspired by Karmarkar's method are closely related to application of Newton's method to a sequence of barrier functions. Each barrier function is defined from the objective function and a barrier term that is infinite along the boundary of the feasible region. As the weight on the barrier term is reduced to zero, the solution of the subproblem becomes closer to the solution of the original problem
1 edition published in 1988 in English and held by 0 WorldCat member libraries worldwide
This report describes technical progress during the past twelve months on DOE Contract DEFG87ER25030 and requests support for the third year. The project involves study of the theoretical properties and computational performance of techniques that solve linear and nonlinear programs by means of nonlinear transformations. The group at the Systems Optimization Laboratory (SOL) were the first to recognize the connection between Karmarkar's projective method and the logarithmic barrier method. It is now generally recognized that essentially all interiorpoint methods for linear programming inspired by Karmarkar's method are closely related to application of Newton's method to a sequence of barrier functions. Each barrier function is defined from the objective function and a barrier term that is infinite along the boundary of the feasible region. As the weight on the barrier term is reduced to zero, the solution of the subproblem becomes closer to the solution of the original problem
New approaches to linear and nonlinear programming(
)
1 edition published in 1990 in English and held by 0 WorldCat member libraries worldwide
During the last twelve months, research has concentrated on barrier function methods for linear programming (LP) and quadratic programming (QP). Some groundwork for the application of barrier methods to nonlinearly constrained problems has also begun. In our previous progress report we drew attention to the difficulty of developing robust implementations of barrier methods for LP. We have continued to refine both the primal algorithm and the dual algorithm. We still do not claim that the barrier algorithms are as robust as the simplex method; however, the dual algorithm has solved all the problems in our extensive test set. We have also gained some experience with using the algorithms to solve aircrew scheduling problems
1 edition published in 1990 in English and held by 0 WorldCat member libraries worldwide
During the last twelve months, research has concentrated on barrier function methods for linear programming (LP) and quadratic programming (QP). Some groundwork for the application of barrier methods to nonlinearly constrained problems has also begun. In our previous progress report we drew attention to the difficulty of developing robust implementations of barrier methods for LP. We have continued to refine both the primal algorithm and the dual algorithm. We still do not claim that the barrier algorithms are as robust as the simplex method; however, the dual algorithm has solved all the problems in our extensive test set. We have also gained some experience with using the algorithms to solve aircrew scheduling problems
Solving linear programs under uncertainty, using decomposition, importance sampling and parallel processors. Progress report(
)
1 edition published in 1994 in English and held by 0 WorldCat member libraries worldwide
Planning under uncertainty is a fundamental problem of decision science where solution could advance man's ability to plan, schedule, design, and control complex situations. Goal is to develop efficient methods for solving an important class of planning problems, namely linear programs whose parameters (coefficients, right hand sides) are not known with certainty. The research concentrated on theoretical tasks of decomposition and importance sampling techniques, implementation, and software development issues and on applications. Research is continuing on use of parallel processors for solving stochastic programs
1 edition published in 1994 in English and held by 0 WorldCat member libraries worldwide
Planning under uncertainty is a fundamental problem of decision science where solution could advance man's ability to plan, schedule, design, and control complex situations. Goal is to develop efficient methods for solving an important class of planning problems, namely linear programs whose parameters (coefficients, right hand sides) are not known with certainty. The research concentrated on theoretical tasks of decomposition and importance sampling techniques, implementation, and software development issues and on applications. Research is continuing on use of parallel processors for solving stochastic programs
New approaches to linear and nonlinear programming. Final technical report, January 1, 1987December 31, 1989(
)
1 edition published in 1990 in English and held by 0 WorldCat member libraries worldwide
During the last twelve months, research has concentrated on barrier function methods for linear programming (LP) and quadratic programming (QP). Some groundwork for the application of barrier methods to nonlinearly constrained problems has also begun. In our previous progress report we drew attention to the difficulty of developing robust implementations of barrier methods for LP. We have continued to refine both the primal algorithm and the dual algorithm. We still do not claim that the barrier algorithms are as robust as the simplex method; however, the dual algorithm has solved all the problems in our extensive test set. We have also gained some experience with using the algorithms to solve aircrew scheduling problems
1 edition published in 1990 in English and held by 0 WorldCat member libraries worldwide
During the last twelve months, research has concentrated on barrier function methods for linear programming (LP) and quadratic programming (QP). Some groundwork for the application of barrier methods to nonlinearly constrained problems has also begun. In our previous progress report we drew attention to the difficulty of developing robust implementations of barrier methods for LP. We have continued to refine both the primal algorithm and the dual algorithm. We still do not claim that the barrier algorithms are as robust as the simplex method; however, the dual algorithm has solved all the problems in our extensive test set. We have also gained some experience with using the algorithms to solve aircrew scheduling problems
New approaches to linear and nonlinear programming(
)
1 edition published in 1993 in English and held by 0 WorldCat member libraries worldwide
This program involves study of theoretical properties and computational performance of algorithms that solve linear and nonlinear programs. Emphasis is placed on algorithms to solve large problems, especially in the energy area. E.g., the safe, efficient distribution of electricity and the identification of the state of an electrical network are both largescale nonlinearly constrained optimization problems. Other applications include optimal trajectory calculations and optimal structural design
1 edition published in 1993 in English and held by 0 WorldCat member libraries worldwide
This program involves study of theoretical properties and computational performance of algorithms that solve linear and nonlinear programs. Emphasis is placed on algorithms to solve large problems, especially in the energy area. E.g., the safe, efficient distribution of electricity and the identification of the state of an electrical network are both largescale nonlinearly constrained optimization problems. Other applications include optimal trajectory calculations and optimal structural design
New approaches to linear and nonlinear programming. Progress report, April 15, 1992January 31, 1993(
)
1 edition published in 1993 in English and held by 0 WorldCat member libraries worldwide
This program involves study of theoretical properties and computational performance of algorithms that solve linear and nonlinear programs. Emphasis is placed on algorithms to solve large problems, especially in the energy area. E.g., the safe, efficient distribution of electricity and the identification of the state of an electrical network are both largescale nonlinearly constrained optimization problems. Other applications include optimal trajectory calculations and optimal structural design
1 edition published in 1993 in English and held by 0 WorldCat member libraries worldwide
This program involves study of theoretical properties and computational performance of algorithms that solve linear and nonlinear programs. Emphasis is placed on algorithms to solve large problems, especially in the energy area. E.g., the safe, efficient distribution of electricity and the identification of the state of an electrical network are both largescale nonlinearly constrained optimization problems. Other applications include optimal trajectory calculations and optimal structural design
New approaches to linear and nonlinear programming. Progress report, April 15, 1993February 28, 1994(
)
1 edition published in 1994 in English and held by 0 WorldCat member libraries worldwide
The project involves study of theoretical properties and computational performance of algorithms that solve linear and nonlinear programs, with emphasis on solving large problems, which is important in the energy area. For example, the safe and efficient distribution of electricity and identification of the state of an electrical network are largescale nonlinearly constrained optimization problems. Other application areas involved include optimal trajectory calculations and optimal structural design
1 edition published in 1994 in English and held by 0 WorldCat member libraries worldwide
The project involves study of theoretical properties and computational performance of algorithms that solve linear and nonlinear programs, with emphasis on solving large problems, which is important in the energy area. For example, the safe and efficient distribution of electricity and identification of the state of an electrical network are largescale nonlinearly constrained optimization problems. Other application areas involved include optimal trajectory calculations and optimal structural design
Probabilistic lower bound for two stage stochastic programs by Stanford University(
)
1 edition published in 1995 in English and held by 0 WorldCat member libraries worldwide
In the framework of Benders decomposition for twostage stochastic linear programs, the authors estimate the coefficients and righthand sides of the cutting planes using Monte Carlo sampling. The authors present a new theory for estimating a lower bound for the optimal objective value and they compare (using various test problems whose true optimal value is known) the predicted versus the observed rate of coverage of the optimal objective by the lower bound confidence interval
1 edition published in 1995 in English and held by 0 WorldCat member libraries worldwide
In the framework of Benders decomposition for twostage stochastic linear programs, the authors estimate the coefficients and righthand sides of the cutting planes using Monte Carlo sampling. The authors present a new theory for estimating a lower bound for the optimal objective value and they compare (using various test problems whose true optimal value is known) the predicted versus the observed rate of coverage of the optimal objective by the lower bound confidence interval
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Related Identities
 United States Office of Naval Research
 Stanford University Department of Statistics
 United States Department of Energy Oakland Operations Office Researcher
 United States Department of Energy Office of Scientific and Technical Information Distributor
 United States Department of Energy Sponsor
 Manne, Alan Sussmann Author
 Electric Power Research Institute
 Stanford University Applied Mathematics and Statistics Laboratory
 Stanford University Applied Mathematics and Statistics Laboratory
 National Science Foundation (U.S.)
Associated Subjects
Breeder reactors Chemical equilibriumMathematics Economic history Economics, Mathematical EconomicsMathematical models Electric power Electric utilities Energy developmentEconomic aspects Energy industriesEconomic aspects Energy policy Linear programming MachineryMaintenance and repairMathematical models MacroeconomicsMathematical models Markov processes Network analysis (Planning) Operations research Power resourcesEconomic aspects Power resourcesMathematical models Power resourcesSupply and demand Queuing theory Traffic engineeringMathematical models Traffic flowMathematical models United States
Alternative Names
Stanford University. Program in Operations Research
Department of Operations Research
Stanford University. Dept. of Operations Research
Languages