The technical efficiency of vacuum-pan sugar industry of India: An application of a stochastic frontier production function using panel data

A stochastic production frontier method is used to examine technical efficiency among Indian vacuum-pan sugar factories over a five-year period. Most factories are close to Indian best practice in terms of technical efficiency. Smaller firms and firms with access to sweeter cane are likely to be more efficient than other firms, while publicly owned firms are less efficient. There are transitory positive effects of a long crushing season on technical efficiency.     

Modeling nongray gas-phase and soot radiation in luminous turbulent nonpremixed jet flames

Much progress has been made in radiative heat transfer modeling with respect to treatment of nongray radiation from both gas-phase species and soot particles, while radiation modeling in turbulent flame simulations is still in its infancy. Aiming at reducing this gap, this paper introduces state-of-the-art models of gas-phase and soot radiation to turbulent flame simulations. The full-spectrum k-distribution method (Modest, M.F., 2003, Journal of Quantitative Spectroscopy & Radiative Transfer, 76, 69–83) is implemented into a three-dimensional unstructured CFD code for nongray radiation modeling. The mixture full-spectrum k-distributions including nongray absorbing soot particles are constructed from a narrow-band k-distribution database created for individual gas-phase species, and an efficient scheme is employed for their construction in CFD simulations. A detailed reaction mechanism including NO _x and soot kinetics is used to predict flame structure, and a detailed soot model using a method of moments is employed to determine soot particle size distributions. A spherical-harmonic P₁ approximation is invoked to solve the radiative transfer equation. An oxygen-enriched, turbulent, nonpremixed jet flame is simulated, which features large concentrations of gas-phase radiating species and soot particles. Nongray soot modeling is shown to be of greater importance than nongray gas modeling in sooty flame simulations, with gray soot models producing large errors. The nongray treatment of soot strongly influences flame temperatures in the upstream and the flame-tip region and is essential for accurate predictions of NO. The nongray treatment of gases, however, weakly influences upstream flame temperatures and, therefore, has only a small effect on NO predictions. The effect of nongray soot radiation on flame temperature is also substantial in downstream regions where the soot concentration is small. Limitations of the P₁ approximation are discussed for the jet flame configuration; the P₁ approximation yields large errors in the spatial distribution of the computed radiative heat flux for highly anisotropic radiation fields such as those in flames with localized, near-opaque soot regions.     

Optimal Operations Management and Network Planning of a District Heating System with a Combined Heat and Power Plant

District heating plants are becoming more common in European cities. These systems make it possible to furnish users with warm water while locating the production plants in the outskirts having the double benefit of lowering the impact of pollution on the center of the city and achieving better conversion performances. In order to amortize the costs throughout the year, the system often includes a combined heat and power (CHP) plant, to exploit the energy during the summer as well, when the demand for warm water decreases. A linear programming model for the optimal resource management of such a plant is presented and some results for a real case are reported. A distribution network design problem is also addressed and solved by means of mixed integer linear programming.     

Effective Information for Offline Stochastic Feedback and Optimal Control of Dynamic Systems

The impact of uncertain future events on decision making in a stochastic environment is modeled in this paper. Such modeling is presented for both feedback and optimal control problems. This research overcomes the difficulties of forecasting that arise when considering future information. In this paper, we seek to find the minimum amount of information (effective information) necessary to evaluating system performance offline or to optimally control a system. The existence of effective information is proved and a methodology for determining it is developed. It is also shown that ignoring information beyond the planning horizon leads to significant performance loss and may even lead to violating the constraints of a control problem.     

Cut sharing for multistage stochastic linear programs with interstage dependency

Multistage stochastic programs with interstage independent random parameters have recourse functions that do not depend on the state of the system. Decomposition-based algorithms can exploit this structure by sharing cuts (outer-linearizations of the recourse function) among different scenario subproblems at the same stage. The ability to share cuts is necessary in practical implementations of algorithms that incorporate Monte Carlo sampling within the decomposition scheme. In this paper, we provide methodology for sharing cuts in decomposition algorithms for stochastic programs that satisfy certain interstage dependency models. These techniques enable sampling-based algorithms to handle a richer class of multistage problems, and may also be used to accelerate the convergence of exact decomposition algorithms. Research leading to this work was partially supported by the Department of Energy Contract DE-FG03-92ER25116-A002; the Office of Naval Research Contract N00014-89-J-1659; the National Science Foundation Grants ECS-8906260, DMS-8913089; and the Electric Power Research Institute Contract RP 8010-09, CSA-4O05335. This author's work was supported in part by the National Research Council under a Research Associateship at the Naval Postgraduate School, Monterey, California.     

A parallel inexact newton method for stochastic programs with recourse

A parallel inexact Newton method with a line search is proposed for two-stage quadratic stochastic programs with recourse. A lattice rule is used for the numerical evaluation of multi-dimensional integrals, and a parallel iterative method is used to solve the quadratic programming subproblems. Although the objective only has a locally Lipschitz gradient, global convergence and local superlinear convergence of the method are established. Furthermore, the method provides an error estimate which does not require much extra computation. The performance of the method is illustrated on a CM5 parallel computer.This work was supported by the Australian Research Council and the numerical experiments were done on the Sydney Regional Centre for Parallel Computing CM5.     

一类分支过程的收敛性

随机稳定性是各种随机模型中的至关重要的问题,随机稳定性中的关键问题是找出过程遍历,指数遍历和强遍历的准则．该文对一类重要的分支过程给出了过程指数遍历及强遍历的条件．在证明中主要应用了几种不同的比较方法,从该文的结果可以看出,这种方法是有效的,因而在其它情形中也是非常有意义的．而且所得结果的概率意义也是十分清楚的．     

Oscillator with random trichotomous mass

In addition to the case usually considered of a stochastic harmonic oscillator subject to an external random force (Brownian motion in a parabolic potential) or to a random frequency and random damping, we consider an oscillator with random mass subject to an external periodic force, where the molecules of a surrounding medium, which collide with a Brownian particle are able to adhere to the oscillator for a random time, changing thereby the oscillator mass. The fluctuations of mass are modelled as trichotomous noise. Using the Shapiro–Loginov procedure for splitting the correlators, we found the first two moments. It turns out that the second moment is a non-monotonic function of the characteristics of noise and periodic signal, and for some values of these parameters, the oscillator becomes unstable.     

Evolutionary game on a stochastic growth network

In some real complex systems the structures are difficult to map or changing over time. To explore the evolution of strategies on these complex systems, it is not realistic enough to specify their structures or topological properties in advance. In this paper, we address the evolutionary game on a stochastic growth network adopting the prisoner’s dilemma game. We introduce a growing rate q$q$ to control the ratio of network growth to strategy evolution. A large q$q$ denotes that the network grows faster than strategy evolution. Simulation results show that a fast growing rate is helpful to promote the average payoffs of both cooperators and defectors. Moreover, this parameter also significantly influences the cooperation frequency on the resulting networks. The coexisting mechanisms in this paper may provide a beneficial insight for understanding the emergence of complex topological structures and game behaviors in numerous real systems.     

Exact field-driven interface dynamics in the two-dimensional stochastic Ising model with helicoidal boundary conditions

We investigate the interface dynamics of the two-dimensional stochastic Ising model in an external field under helicoidal boundary conditions. At sufficiently low temperatures and fields, the dynamics of the interface is described by an exactly solvable high-spin asymmetric quantum Hamiltonian that is the infinitesimal generator of the zero range process. Generally, the critical dynamics of the interface fluctuations is in the Kardar–Parisi–Zhang universality class of critical behavior. We remark that a whole family of RSOS interface models similar to the Ising interface model investigated here can be described by exactly solvable restricted high-spin quantum XXZ$XXZ$-type Hamiltonians.