Quadratic Cost Model Research Articles

Economic generation schedule on thermal power system considering emission using grey wolves optimization

This paper delineates a computational framework to ascertain optimum thermal generation schedule using newfangled grey wolves optimization (GWO) technique corresponding to economic, emission, and combined economic emission operation. This scheduling problem is devised as a bi-objective optimization and linear interpolated price penalty model is developed based on simple analytical geometry equations which blends two conflict objectives perfectly. In order to obtain high-quality solutions within lesser executing time, the algorithm parameters are nicely replaced with system parameters that carry out global and local search process in the feasible region collaboratively. Further, an appropriate constraint handling mechanism is suitably incorporated in the algorithm that intern produces a stable convergence characteristic. The effectiveness of the proposed approach is illustrated on six unit thermal systems with due consideration of transmission line loss. The desired GWO technique reports a new feasible solution for quadratic cost model which is compared with the solution that has evolved earlier and the comparison shows that the GWO technique has outstripped other algorithms effectively.

Can rural electric cooperatives survive in a restructured US electric market? An empirical analysis

This paper examines the ability of rural electric distribution cooperatives to continue operating in their present form in a restructured electricity market. More specifically, I develop and estimate a quadratic cost model, which, unlike many of the cost functions employed in studies of this nature, conforms to all of the properties of a proper cost function. Using 1996 data, I find that these firms are not operating in a cost-minimizing fashion. This finding seems to occur because each is too small in terms of the quantity of electricity distributed. As a result, mergers between these firms could yield substantial savings and help ensure their survival in their present form in a deregulated market.

Estimating intertemporal quadratic adjustment cost models with integrated processes: a VAR approach

This paper extends the existing literature on linear quadratic adjustment cost (LQAC) models under rational expectations to the inferential issue arising when: (I) agents optimize with respect to a vector of endogenous variables; (II) the behavioral equation stemming from the agent's optimization problem are specified as 'exact' rational expectations models; (III) the stochastic processes involved are integrated of order one. We focus on estimation in e 'full-information' framework, and the key assumption is that agents compute expectations through a cointegrated VAR (CVAR) for the observable variables.

Testing the (S,s) model of inventory investment with Canadian wholesale trade data

The (S,s) model of inventories is a robust alternative to production smoothing theories. Despite the apparent usefulness of the model, exhaustive testing has been lacking in the literature. This note employs a recently published econometric methodology to test the (S,s) model and provide additional evidence from Canadian wholesale trade. The empirical findings favour (S,s) against the quadratic cost model for all the product categories investigated.

A Noniterative Algorithm for the Multiproduct Production and Work Force Planning Problem

Discrete optimal control theory is used to develop an efficient noniterative algorithm for solving the multiproduct production and work force planning problems with a quadratic cost function. The quadratic cost models allow uncertainties to be handled directly because they minimize the expected cost if unbiased expected demand forecasts are given. A real-world problem may involve as many as 200,000 variables. The noniterative algorithm makes the computations, irrespective of the number of products, not only feasible but also extremely easy and efficient.

Trade Inventories and (S,s)

The paper presents empirical tests of the (S,s) model of inventory behavior using aggregate retail trade data. Estimation and testing are based on the probability distributions of inventories derived by Caplin [1985]. The excess volatility of retailers' demand over their consumers' demand, and the forgetfulness of inventories under (S,s) are emphasized. Test results indicate that the time series properties of deliveries and sales are consistent with (S,s) and not a quadratic cost model. Finally, when autoregressions of inventories are given an (S,s) rather than a stock adjustment interpretation, traditional empirical problems such as low speeds of adjustment are explained.

A Search Decision Rule for the Aggregate Scheduling Problem

This paper proposes a search decision rule (SDR) approach to the aggregate scheduling problem. The approach is tested by converting the objective function of the classic Holt, Modigliani, Muth and Simon paint factory scheduling problem into a 20 dimension response surface which is then explored by the SDR. Conjugate gradient, variable metric and pattern search methods are tested. All three methods demonstrate the ability to consistently descend to the neighborhood of the minimum. The pattern search technique is selected for incorporation into the SDR heuristic because of its excellent performance in terms of reducing the computation time required. Production and work force decisions made up by the SDR are comparable to those made by the linear decision rule and total costs are within 0.1%. By means of the search decision rule approach it should be possible to eliminate the restrictions imposed by linear and quadratic cost models and thereby pursue a more general and realistic approach to the aggregate scheduling problem.