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Mathematical Programming Approach (mathematical + programming_approach)

Distribution by Scientific Domains

Chemistry	40%
Business, Economics, Finance and Accounting	40%

Selected Abstracts

A Mathematical Programming Approach for Procurement Using Activity Based Costing

JOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 1-2 2000
Zeger Degraeve
Activity Based Costing and Management are important topics in today's management accounting literature. While there has been much attention paid in the Activity Based Costing literature to customer profitability analysis, process improvement and product design, there has been far less notice taken of purchasing. In this paper we develop an Activity Based Costing approach for the determination of procurement strategies. Vendor selection using an Activity Based Costing approach is choosing the combination of suppliers for a given product group that minimizes the total costs associated with the purchasing strategy. To this end we develop a mathematical programming model where decisions involve the selection of vendors and the determination of order quantities. The system computes the total cost of ownership, thereby increasing the objectivity in the selection process and giving the opportunity for various kinds of sensitivity analysis. [source]

Food consumption impacts of adherence to dietary norms in the United States: a quantitative assessment

AGRICULTURAL ECONOMICS, Issue 2-3 2007
C. S. Srinivasan
Dietary norms; Dietary adjustment; Food consumption impacts; Quadratic programming Abstract Promotion of adherence to healthy-eating norms has become an important element of nutrition policy in the United States and other developed countries. We assess the potential consumption impacts of adherence to a set of recommended dietary norms in the United States using a mathematical programming approach. We find that adherence to recommended dietary norms would involve significant changes in diets, with large reductions in the consumption of fats and oils along with large increases in the consumption of fruits, vegetables, and cereals. Compliance with norms recommended by the World Health Organization for energy derived from sugar would involve sharp reductions in sugar intakes. We also analyze how dietary adjustments required vary across demographic groups. Most socio-demographic characteristics appear to have relatively little influence on the pattern of adjustment required to comply with norms. Income levels have little effect on required dietary adjustments. Education is the only characteristic to have a significant influence on the magnitude of adjustments required. The least educated rather than the poorest have to bear the highest burden of adjustment. Our analysis suggests that fiscal measures like nutrient-based taxes may not be as regressive as commonly believed. Dissemination of healthy-eating norms to the less educated will be a key challenge for nutrition policy. [source]

A property-based optimization of direct recycle networks and wastewater treatment processes

AICHE JOURNAL, Issue 9 2009
José María Ponce-Ortega
Abstract This article presents a mathematical programming approach to optimize direct recycle-reuse networks together with wastewater treatment processes in order to satisfy a given set of environmental regulations. A disjunctive programming formulation is developed to optimize the recycle/reuse of process streams to units and the performance of wastewater treatment units. In addition to composition-based constraints, the formulation also incorporates in-plant property constraints as well as properties impacting the environment toxicity, ThOD, pH, color, and odor. The MINLP model is used to minimize the total annual cost of the system, which includes the cost for the fresh sources, the piping cost for the process integration and the waste stream treatment cost. An example problem is used to show the application of the proposed model. The results show that the simultaneous optimization of a recycle network and waste treatment process yields significant savings with respect to a commonly-used sequential optimization strategy. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source]

A mathematical programming approach for improving the robustness of least sum of absolute deviations regression

NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 4 2006
Avi Giloni
Abstract This paper discusses a novel application of mathematical programming techniques to a regression problem. While least squares regression techniques have been used for a long time, it is known that their robustness properties are not desirable. Specifically, the estimators are known to be too sensitive to data contamination. In this paper we examine regressions based on Least-sum of Absolute Deviations (LAD) and show that the robustness of the estimator can be improved significantly through a judicious choice of weights. The problem of finding optimum weights is formulated as a nonlinear mixed integer program, which is too difficult to solve exactly in general. We demonstrate that our problem is equivalent to a mathematical program with a single functional constraint resembling the knapsack problem and then solve it for a special case. We then generalize this solution to general regression designs. Furthermore, we provide an efficient algorithm to solve the general nonlinear, mixed integer programming problem when the number of predictors is small. We show the efficacy of the weighted LAD estimator using numerical examples. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006 [source]

Optimization of a Process Synthesis Superstructure Using an Ant Colony Algorithm

CHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 3 2008
B. Raeesi
Abstract The optimization of chemical syntheses based on superstructure modeling is a perfect way for achieving the optimal plant design. However, the combinatorial optimization problem arising from this method is very difficult to solve, particularly for the entire plant. Relevant literature has focused on the use of mathematical programming approaches. Some research has also been conducted based on meta-heuristic algorithms. In this paper, two approaches are presented to optimize process synthesis superstructure. Firstly, mathematical formulation of a superstructure model is presented. Then, an ant colony algorithm is proposed for solving this nonlinear combinatorial problem. In order to ensure that all the constraints are satisfied, an adaptive, feasible bound for each variable is defined to limit the search space. Adaptation of these bounds is executed by the suggested bound updating rule. Finally, the capability of the proposed algorithm is compared with the conventional Branch and Bound method by a case study. [source]