Maximization Of Objective Function Research Articles

A novel edge detection method based on the maximizing objective function

This paper proposes a novel edge detection method for both gray level images and color images. The 3 × 3 mask in the image is considered and two pixel sets S 0 and S 1 in the mask are used to define an objective function. The values of the objective function corresponding to four directions determine the edge intensity and edge direction of each pixel in the mask. After all pixels in the image have been processed, the edge map and direction map are generated. Then we apply the non-maxima suppression method to the edge map and the direction map to extract the edge points. The proposed method can detect the edge successfully, while double edges, thick edges, and speckles can be avoided.

Store Location in Shopping Centers: Theory and Estimates

This paper develops a formal theory of store location within shopping centers based on bid rent theory. The bid rent model is fully specified and solved with the objective function of profit maximization in the presence of comparative, multipurpose and impulse shopping behavior. Several hypotheses result about the optimal relationships between store types, sizes, rents, sales, and distances from the mall center. The hypotheses are tested and confirmed using data from a sample of 689 leases in eight regional and super-regional shopping centers, suggesting that a bid rent explanation is consistent with observed location patterns in malls.

Urban planning for low-income groups with developed optimization models

This article presents a methodology and new technique for residential land planning for low-income groups. Several unit design modules for the residential area are suggested and a cost optimization model for obtaining the optimum physical design of one module satisfying the geometric, density, and other constraints has been formulated as a non-linear programming problem. The solution came through a relatively simplified Focus Search and Monte-Carlo Simulation techniques. A second model for the optimal design of the dwelling-layout has been developed with an objective function of maximization of lot dimensions and satisfying some constraints, most important of which is the affordability for low-income groups. The optimum design, based on this model, came by using the relatively simple computer-based Focus Search technique. A practical application of the two models for Syria is illustrated by an example about obtaining a residential neighborhood from several planning modules combined together. A case study revealed how a planner, can achieve directly the desired feasible and optimal design solutions, depending on these new planning techniques.

Genomic Signal Enhancement by Clustering**The project supported in part by the Special Funds for Major National Basic Research Projects, National Natural Science Foundation of China and Research Project 248 of Beijing

Weight matrix models for signal sequence motif are simple. A main limitation of the models is the assumption of independence between positions. Signal enhancement is achieved by taking the total likelihood as the objective function for maximization to cluster sequences into groups with different patterns. As an example, the initial and terminal signals for translation in rice genome are examined.

Modeling the consumer benefit in the optimal power flow

The addition of a model of the consumer into the traditional optimal power flow (OPF) algorithm that minimizes supplier costs is investigated. The development of such a model is based on the solution of the OPF using an objective function for maximization of social welfare. A traditional OPF algorithm can be modified to solve the social welfare maximization problem by including price-dependent load models. This modification to the traditional OPF is intuitive and very simple. This modified OPF formulation facilitates simulation of spot markets for both real and reactive power. The algorithm is effective on systems of hundreds of buses, but small examples to compare the results to the traditional OPF are also insightful. The impact of price-dependent loads on systems with transmission congestion, increased fuel costs, and voltage problems can be studied.

Computing lower bounds by destructive improvement: An application to resource-constrained project scheduling

In this paper, two meta-strategies for computing lower bounds (for minimization problems) are described. Constructive (direct) methods directly calculate a bound value by relaxing a problem and solving this relaxation. Destructive improvement techniques restrict a problem by setting a maximal objective function value F and try to contradict (destruct) the feasibility of this reduced problem. In case of success, F or even F + 1 is a valid lower bound value. The fundamental properties and differences of both meta-strategies are explained by applying them to the well-known resource-constrained project scheduling problem (RCPSP). For this problem, only a few constructive bound arguments are available in the literature. We present a number of extensions and new methods as well as techniques for reducing problem data which can be exploited within the destructive improvement framework. Comprehensive numerical experiments show that the new constructive bound arguments clearly provide better bounds than the former ones and that further really significant improvements are obtained through an appropriate application of destructive improvement.

Tourism and advertising: Evidence from Virginia beach

In recent years, the City of Virginia Beach, Virginia, has been spending more than two million dollars per year to promote tourism for the city. However, very little is known about the effects of such advertising on overnight visitors. On the basis of a survey of 1,200 overnight visitors during the summer of 1991, this study estimates the effect on tourism and the subsequent fiscal effect of tourism advertising expenditures. The study also calculates a rate of return from advertising from the perspective of a governmental revenue maximization objective function. This paper develops a travel cost model of the impact of advertising on overnight visitation to the city. The model provides, ceteris paribus, that advertising expenditures of 2.4 million dollars in 1991 resulted in an estimated 600,000 additional visitors. The paper concludes that if the City's objective is to maximize public profit, then it should expand its advertising budget.

A dynamic model of soil salinity and drainage generation in irrigated agriculture: A framework for policy analysis

This paper presents a dynamic model of irrigated agriculture that accounts for drainage generation and salinity accumulation. Critical model relationships involving crop production, soil salinity, and irrigation drainage are based on newly estimated functions derived from lysimeter field tests. The model allocates land and water inputs over time based on an intertemporal profit maximization objective function and soil salinity accumulation process. The model is applied to conditions in the San Joaquin Valley of California, where environmental degradation from irrigation drainage has become a policy issue. Findings indicate that in the absence of regulation, drainage volumes increase over time before reaching a steady state as increased quantities of water are allocated to leaching soil salts. The model is used to evaluate alternative drainage abatement scenarios involving drainage quotas and taxes, water supply quotas and taxes, and irrigation technology subsidies. In our example, direct drainage policies are more cost‐effective in reducing drainage than policies operating indirectly through surface water use, although differences in cost efficiency are relatively small. In some cases, efforts to control drainage may result in increased soil salinity accumulation, with implications for long‐term cropland productivity. While policy adjustments may alter the direction and duration of convergence to a steady state, findings suggest that a dynamic model specification may not be necessary due to rapid convergence to a comon steady state under selected scenarios.

Oil Resource Exploitaton and Economic Growth in Nigeria: An Economy-Wide Optimization Study

Revenue generated from crude oil exports has been an important source of foreign exchange required for development in most net oil exporting developing countries. As a result, development planning in these nations require a thorough understanding of the linkages that usually exist between the oil sector and the rest of the economy. We used a macro-economic/optimization model to study the impacts of the developments in the oil market on the Nigerian economy. The modelling technique involve the building of a two sector macroeconomic model (oil and non-oil sectors) which is then employed together with a social welfare maximization objective function to formulate an optimal control problem for the economy. The need to have a balance of payment situation during the planning period was incorporated as a constraint in the formulation. The model was used to evaluate the optimal path of real imports during the decade of the 1970 in Nigeria, as well as the path of the macroeconomy in the future.

A Methodology for Estimating Integrated Forecasting/Decision Model Parameters Using Linear Programming

AbstractA linear programming algorithm is used to estimate the parameters of a wheat storage decision model. This approach allows objective functions other than minimization of error squared to be used. It is demonstrated that by using a profit maximization objective function, an improved wheat storage decision model can be developed.

Uncertain Input Prices, Profit Risk, and the Rate-of-Return Regulated Firm: Comment

Scott (1983) considers the effects of input price uncertainty on the selection of inputs by regulated firms. Specifically, he asks the question: How will the combination of input price uncertainty and continuous rate-of-return regulation affect the firm's selection of inputs? (p. 337). In attempting to answer this question, he considers a firm value maximization objective function based upon the capital asset pricing model and characterizes the regulatory constraint by proposing that the firm faces a constraint on the expected value of accounting profits. His conclusion is that when the effect of uncertain input prices on profit risk is considered, the continuously regulated firm has the incentive to use relatively less capital than when input prices are known with certainty. In this comment, it will be argued, firstly, that the specification of the regulatory constraint is inconsistent with the idea of modelling continuous regulation. Secondly, it will be pointed out that, even if the regulatory constraint formulation is accepted, the results are incorrect and, in plausible circumstances, the opposite of Scott's (1983) results will be found. This error is caused by a misspecification of the variables defined within the capital asset pricing model. Thirdly, it is asserted that a comparison between the regulated firm under certainty is not the only valid comparison. Presumably, in attempting to evaluate the effect of uncertainty on the Averch-Johnson effect, it would be more appropriate to compare the regulated firm under uncertainty with the unregulated firm under uncertainty. Such a comparison suggests that the effect of

An operational optimization procedure for production scheduling

The methodology takes a sufficiently long time horizon and breaks the problem into two subproblems. The first subproblem is the long range planning model and the second the short run production scheduling model. The long range model is essentially a resource constrained model and has a linear programming formulation with a profit maximization objective function. The long range plan fixes the discretionary marketing variables, such as the selection of product line, and the timing and extent of promotional sales. It estimates manpower requirements and establishes the raw material procurement plans. Lagrange multipliers obtained in the long range model are then used in the short run production scheduling model. The scheduling algorithm, having a Lagrangian function for an objective, is the solution to an unconstrained maximization problem. This then reduces to one of sequential allocation of production facilities to products. The algorithm is being applied on a problem with five production lines, 126 products, 26 time periods and 32 raw material constraints.

Computer-aided design of parametric amplifiers

A computer-aided design approach is developed for the analysis and design of single-tuned parametric amplifiers. The voltage gain- bandwidth product (√ <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> ̅ <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i> ) is used as a maximizing objective function for the optimum design. An 18-GHz single-tuned amplifier using a single-packaged varactor diode is designed as an illustration. The experimental amplifier exhibited a large √ <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> ̅ <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i> of 2400 MHz and a low noise temperature of 180°K at room temperature ambient.