Efficient solution generation for multiple objective linear programming based on extreme ray generation method

In this paper we consider solution generation method for multiple objective linear programming problems. The set of efficient or Pareto optimal solutions for the problems can be regarded as global information in multiple objective decision making situation. In the past three decades as solution generation techniques various conventional algorithms based on simplex-like approach with heavy computational burden were developed. Therefore, the development of novel and useful directions in efficient solution generation method have been desired. The purpose of this paper is to develop theoretical results and computational techniques of the efficient solution generation method based on extreme ray generation method that sequentially generates efficient points and rays by adding inequality constraints of the polyhedral feasible region.     

Multigrid applications to three-dimensional elliptic coordinate generation

The multigrid algorithm was applied to solve the coupled set of elliptic quasilinear partial differential equations associated with three-dimensional coordinate generation. The results indicate that the multigrid scheme is more than twice as fast as conventional relaxation schemes on moderate-size grids. Convergence factors of order 0.90 per work unit were achieved on 36,000-point grids. The paper covers the form of transformation, develops the set of generation equations, and gives details on the multigrid approach used. Included are a development of the full-approximation storage scheme, details of the smoothing-rate analysis, and a section devoted to rational programming techniques applicable to the multigrid algorithm.     

Comparison of techniques for assessing effects of loading uncertainty upon a long term phosphorous model

This paper compares the feasibility of applying three stochastic techniques to a linear water quality model. The Monte Carlo, first order analysis, and generation of moment equation techniques are applied to a long term phosphorus model of Lake Washington. The effect of uncertainty of the phosphorus loading term on simulated phosphorus levels is analysed. The simulated concentrations of phosphorus in the water column are very responsive to uncertainty in annual phosphorus loading, but the sediment concentrations are relatively insensitive. All three stochastic techniques produced identical results, but the level of preparatory and computational effort required varies considerably. The Monte Carlo technique requires the most computation time of the three stochastic techniques examined. The first order analysis and generation of moment equation techniques are shown to be precise and efficient methods of stochastic analysis. In this application they required less than one-thousandth the computer time of the Monte Carlo technique     

A generic view of Dantzig-Wolfe decomposition in mixed integer programming

The Dantzig-Wolfe reformulation principle is presented based on the concept of generating sets. The use of generating sets allows for an easy extension to mixed integer programming. Moreover, it provides a unifying framework for viewing various column generation practices, such as relaxing or tightening the column generation subproblem and introducing stabilization techniques.     

Effect of variable viscosity and viscous dissipation on the Hagen‐Poiseuille flow and entropy generation

In this article, the steady‐state flow of a Hagen‐Poiseuille modelin a circular pipe is considered and entropy generation due tofluid friction and heat transfer is examined. Because of variationin fluid viscosity, the entropy generation in the flow varies. Inhis model, Arrhenius law is applied for temperature equation‐dependent viscosity, and the influence of viscosity parameters on the entropy generation number and distribution of temperature and velocity is investigated. The governing momentum and energy equations, which are coupled due to the dissipative term in the energy equation, were solved by analytical techniques. The solutions of equations via perturbation method and homotopy perturbation method are obtained and then compared with those of numerical solutions. It is found that the fluid viscosity influences considerably the temperature distribution in the fluid close to the pipe wall, and increasing pipe wall temperature enhances the rate of entropy generation. © 2009 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 27: 529–540, 2011     

Solving generation expansion planning problems with environmental constraints by a bundle method

We discuss the energy generation expansion planning with environmental constraints, formulated as a nonsmooth convex constrained optimization problem. To solve such problems, methods suitable for constrained nonsmooth optimization need to be employed. We describe a recently developed approach, which applies the usual unconstrained bundle techniques to a dynamically changing ??improvement function??. Numerical results for the generation expansion planning are reported.     

Constraint programming-based column generation

This paper surveys recent applications and advances of the constraint programming-based column generation framework, where the master subproblem is solved by traditional OR techniques, while the pricing subproblem is solved by constraint programming (CP). This framework has been introduced to solve crew assignment problems, where complex regulations make the pricing subproblem demanding for traditional techniques, and then it has been applied to other contexts. The main benefits of using CP are the expressiveness of its modeling language and the flexibility of its solvers. Recently, the CP-based column generation framework has been applied to many other problems, ranging from classical combinatorial problems such as graph coloring and two dimensional bin packing, to application oriented problems, such as airline planning and resource allocation in wireless ad hoc networks.      

Simple Harmonic Generation: an Exact Method of Solution

The general solution for simple harmonic generation is obtained by inverse scattering techniques. The time evolution of the scattering data is found to be much more complicated than in all other cases which are solvable by inverse scattering. It is also pointed out that the instability of the real solutions of simple harmonic generation is related to the singular nature of the scattering data about ζ = 0.     

Interior point method for long-term generation scheduling of large-scale hydrothermal systems

This paper presents an interior point method for the long-term generation scheduling of large-scale hydrothermal systems. The problem is formulated as a nonlinear programming one due to the nonlinear representation of hydropower production and thermal fuel cost functions. Sparsity exploitation techniques and an heuristic procedure for computing the interior point method search directions have been developed. Numerical tests in case studies with systems of different dimensions and inflow scenarios have been carried out in order to evaluate the proposed method. Three systems were tested, with the largest being the Brazilian hydropower system with 74 hydro plants distributed in several cascades. Results show that the proposed method is an efficient and robust tool for solving the long-term generation scheduling problem.     

A column generation algorithm for nonlinear programming

An algorithm using column generation and penalty function techniques is presented. A linear program with a uniformly bounded number of columns, similar to the restricted master in generalized programming, is used to reduce the number of constraints included in forming a penalty function. The penalty function is used as a Lagrangian in an unconstrained subproblem.This work was supported in part by National Science Foundation Grant GS-3032.     

On the Generation of Markov Decision Processes

Comparisons of the performance of solution algorithms for Markov decision processes rely heavily on problem generators to provide sizeable sets of test problems. Existing generation techniques allow little control over the properties of the test problems and often result in problems which are not typical of real-world examples. This paper identifies the properties of Markov decision processes which affect the performance of solution algorithms, and also describes a new problem generation technique which allows all of these properties to be controlled.     

Integrating Operations Research in Constraint Programming

This paper presents Constraint Programming as a natural formalism for modelling problems, and as a flexible platform for solving them. CP has a range of techniques for handling constraints including several forms of propagation and tailored algorithms for global constraints. It also allows linear programming to be combined with propagation and novel and varied search techniques which can be easily expressed in CP. The paper describes how CP can be used to exploit linear programming within different kinds of hybrid algorithm. In particular it can enhance techniques such as Lagrangian relaxation, Benders decomposition and column generation.     

逆Walsh序p值Walsh变换的演化生成及快速算法

本文利用二分演化思想和矩阵分解技术 ,重点讨论逆Walsh序三值Walsh变换的演化生成和快速算法 ,并将之推广至p值情形 .     

Integrating operations research in constraint programming

This paper presents constraint programming (CP) as a natural formalism for modelling problems, and as a flexible platform for solving them. CP has a range of techniques for handling constraints including several forms of propagation and tailored algorithms for global constraints. It also allows linear programming to be combined with propagation and novel and varied search techniques which can be easily expressed in CP. The paper describes how CP can be used to exploit linear programming within different kinds of hybrid algorithm. In particular it can enhance techniques such as Lagrangian relaxation, Benders decomposition and column generation.     

Evaluation and enhancement of dynamic performance of a microgrid including PV and wind generation systems

Large participation of renewable energy sources with rapidly varying inputs can affect the performance of a microgrid system. This article develops a detailed non-linear and small-signal dynamic model of a microgrid that includes photovoltaic (PV), wind and conventional small-scale generation along with their power electronics interfaces and the filters. The role of the extent of generation mix of the distributed generators (DGs) for satisfactory operation of the system has been investigated through small-signal dynamic model. It was observed that there exist critical values of PV and wind power above which the microgrid performance degrades. The enhancement of performance of the microgrid through various control inputs have been evaluated through decomposition techniques. Simulation studies showed that an energy storage system-based central supervisory controller and also control of PV system can improve transient performance when violations of proper generation mix of the DGs occur.     

Hybrid one-dimensional reversible cellular automata are regular

It is shown that the set of hybrid one-dimensional reversible cellular automata (CA) with the periodic boundary condition is a regular set. This has several important consequences. For example, it allows checking whether a given CA is reversible and the random generation of a reversible CA from the uniform distribution, both using time polynomial in the size of the CA. Unfortunately, the constant term in the resulting random generation algorithm is much too large to be of practical use. We show that for the less general case of null boundary (NB) CA, this constant can be reduced drastically, hence facilitating a practical algorithm for uniform random generation. Our techniques are further applied asymptotically to count the number of reversible NBCA.     

Sensitivity of sample values not generated by inversion

The perturbation generation rule of Suri (Ref. 1) is extended to cases where random variables are not generated by inversion. Conditions are given for the use of other methods. Infinitesimal perturbation analysis is shown to be compatible with common random number techniques in cases where finite-difference approximations are not.This work was supported by the National Science Foundation, Grants Nos. ECS-82-13680 and CDR-85-001-08.     

Combining expert judgment: On the performance of trimmed mean vote aggregation procedures in the presence of strategic voting

Analytic group decision techniques for selecting a subset of alternatives range between multicriteria decision analysis techniques such as multiattribute utility theory and the analytic hierarchy process to voting techniques where each member of the decision group submits a ranking of the alternatives, and these individual rankings are then aggregated into an overall ranking. The obvious advantage of voting is that it bypasses the rather intensive data generation requirements of multicriteria techniques. In this paper we compare the performance of trimmed mean rank-order aggregation procedures in the case where a subset of the individuals in the group charged with the decision vote strategically. We employ a Monte Carlo simulation experiment on a specific decision instance and find that trimmed mean aggregation compares favorably with other procedures.