scholarly journals LINEAR PROGRAMMING AS A DOMAIN OF MATHEMATICAL PROGRAMMING IN ECONOMIC CHALLENGES

2013 ◽  
Vol 2 (06) ◽  
pp. 16-20
Author(s):  
S.A. Sizova ◽  
◽  
V.Yu. Murdugova ◽  
Svetlana Vasilyevna Meleshko ◽  
◽  
...  
Keyword(s):  
Linear Programming ◽  
Mathematical Programming

Modeling the Economic Performance of Industrial Systems Using Mathematical Programming

2015 ◽  
Vol 809-810 ◽  
pp. 1553-1558
Author(s):  
Flavia Fechete ◽  
Anișor Nedelcu
Keyword(s):  
Linear Programming ◽  
Mathematical Programming ◽  
Economic Performance ◽  
Computer Software ◽  
Theoretical Research ◽  
Programming Models ◽  
Optimal Decision ◽  
Management Decision ◽  
Industrial System ◽  
Industrial Systems

Mathematical programming models and especially their subclass - linear programming models - plays an extremely important role, both in theory and in economic practice. Linear programming, through its results, brought a considerable contribution to improving management methods in economics and it has boosted theoretical research in modeling complex economic systems, study and interpretation of laws and economic processes. Developing and designing a model for achieving the economic performance of the industrial system allows managers making optimal decision and ensures the improvement of their activity. This paper aim is to determine an optimal manufacturing program for an industrial system, so that, by its implementation, to achieve economic performance. The manufacturing program conducted using a computer software will allow this entity to optimize their management decision process by providing information related to physical production that must be executed on each of their the products, or about the unused or overloaded capacity, in order to maximize their profits.

scholarly journals Infinite non-linear programming

1963 ◽  
Vol 3 (3) ◽  
pp. 294-300 ◽  
Author(s):  
M. A. Hanson
Keyword(s):  
Linear Programming ◽  
Mathematical Programming ◽  
Calculus Of Variations ◽  
Continuous Variable ◽  
Infinitely Divisible ◽  
Non Linear Programming ◽  
Non Linear ◽  
Mathematical Reason ◽  
Finite Spaces ◽  
Infinitely Divisible Processes

In recent years there has been extensive development in the theory and techniques of mathematical programming in finite spaces. It would be very useful in practice to extend this development to infinite spaces, in order to treat more realistically the problems that arise for example in economic situations involving infinitely divisible processes, and in particular problems involving time as a continuous variable. A more mathematical reason for seeking such generalisation is possibly that of obtaining a unification mathematical programming with other branches of mathematics concerned with extrema, such as the calculus of variations.

Optimal sampling size in multivariate forest inventories: a programming procedure

1980 ◽  
Vol 10 (4) ◽  
pp. 579-585 ◽  
Author(s):  
P. L. Marshall ◽  
J. C. Nautiyal
Keyword(s):  
Linear Programming ◽  
Mathematical Programming ◽  
Sample Size ◽  
Optimal Sampling ◽  
Fixed Costs ◽  
Optimal Sample Size ◽  
Forest Inventories ◽  
Optimal Sample ◽  
Highly Correlated ◽  
Programming Solution

Use of mathematical programming has not become very common in determining optimal sample size in multivariate forest inventories. This paper illustrates the development of a model which may be solved using linear programming to yield a sampling distribution which is close to optimal. The reduction in sample size using the programming procedure over two approximate allocation methods is shown for two examples. It is concluded that the programming solution will show significant improvements over approximate procedures despite higher fixed costs in large multivariate inventories when the variables are not highly correlated and are considered of similar importance.

Linear Programming Approaches for Multiple-Class Discriminant and Classification Analysis

2010 ◽  
Vol 1 (1) ◽  
pp. 57-80 ◽  
Author(s):  
Minghe Sun
Keyword(s):  
Linear Programming ◽  
Mathematical Programming ◽  
Ad Hoc ◽  
Programming Model ◽  
Mixed Integer ◽  
Support Vector ◽  
Discriminant Functions ◽  
Classification Analysis ◽  
Classification Errors ◽  
Multiple Class

New linear programming approaches are proposed as nonparametric procedures for multiple-class discriminant and classification analysis. A new MSD model minimizing the sum of the classification errors is formulated to construct discriminant functions. This model has desirable properties because it is versatile and is immune to the pathologies of some of the earlier mathematical programming models for two-class classification. It is also purely systematic and algorithmic and no user ad hoc and trial judgment is required. Furthermore, it can be used as the basis to develop other models, such as a multiple-class support vector machine and a mixed integer programming model, for discrimination and classification. A MMD model minimizing the maximum of the classification errors, although with very limited use, is also studied. These models may also be considered as generalizations of mathematical programming formulations for two-class classification. By the same approach, other mathematical programming formulations for two-class classification can be easily generalized to multiple-class formulations. Results on standard as well as randomly generated test datasets show that the MSD model is very effective in generating powerful discriminant functions.

Linear Programming Approaches for Multiple-Class Discriminant and Classification Analysis

2012 ◽  
pp. 291-314 ◽  
Author(s):  
Minghe Sun
Keyword(s):  
Linear Programming ◽  
Mathematical Programming ◽  
Ad Hoc ◽  
Programming Model ◽  
Mixed Integer ◽  
Support Vector ◽  
Discriminant Functions ◽  
Classification Analysis ◽  
Classification Errors ◽  
Multiple Class

New linear programming approaches are proposed as nonparametric procedures for multiple-class discriminant and classification analysis. A new MSD model minimizing the sum of the classification errors is formulated to construct discriminant functions. This model has desirable properties because it is versatile and is immune to the pathologies of some of the earlier mathematical programming models for two-class classification. It is also purely systematic and algorithmic and no user ad hoc and trial judgment is required. Furthermore, it can be used as the basis to develop other models, such as a multiple-class support vector machine and a mixed integer programming model, for discrimination and classification. A MMD model minimizing the maximum of the classification errors, although with very limited use, is also studied. These models may also be considered as generalizations of mathematical programming formulations for two-class classification. By the same approach, other mathematical programming formulations for two-class classification can be easily generalized to multiple-class formulations. Results on standard as well as randomly generated test datasets show that the MSD model is very effective in generating powerful discriminant functions.

scholarly journals A Mathematical Programming Model for Vegetable Rotations

1985 ◽  
Vol 17 (1) ◽  
pp. 169-176 ◽  
Author(s):  
Wesley N. Musser ◽  
Vickie J. Alexander ◽  
Bernard V. Tew ◽  
Doyle A. Smittle
Keyword(s):  
Linear Programming ◽  
Mathematical Programming ◽  
Crop Production ◽  
Programming Model ◽  
Crop Rotations ◽  
Programming Models ◽  
Vegetable Production ◽  
Model Design ◽  
Mathematical Programming Model ◽  
Large Numbers

AbstractRotations have historically been used to alleviate pest problems in crop production. This paper considers methods of modeling rotations in linear programming models for Southeastern vegetable production. In such models, entering each possible crop rotation as a separate activity can be burdensome because of the large numbers of possible rotational alternatives. Conventional methodology for double crop rotations reduces the number of activities but must be adapted to accommodate triple crop rotational requirements in vegetable production. This paper demonstrates these methods both for a simple example and an empirical problem with numerous rotation alternatives. While the methods presented in this paper may have computational disadvantages compared to entering each rotation as a separate activity, they do have advantages in model design and data management.

Input Efficiency Measures: A Generalized, Encompassing Formulation

2020 ◽  
Vol 68 (6) ◽  
pp. 1836-1849
Author(s):  
Walter Briec ◽  
Laurent Cavaignac ◽  
Kristiaan Kerstens
Keyword(s):  
Linear Programming ◽  
Mathematical Programming ◽  
Special Focus ◽  
Efficiency Measure ◽  
Efficiency Measures ◽  
Input Efficiency

In the article “Input Efficiency Measures: A Generalized, Encompassing Formulation” we develop a generalized, encompassing formulation unifying four traditional input efficiency measures: radial, Färe-Lovell, asymmetric Färe, and multiplicative Färe-Lovell. This is basically motivated by the fact that observations on production need not be situated near the efficient subset, but could also be positioned close to the isoquant or even the boundary of the technology. This new generalized Färe-Lovell input efficiency measure shares its axiomatic properties with the original Färe-Lovell input efficiency measure. In addition, we can derive new dual interpretations for this generalized Färe-Lovell input efficiency measure. Finally, we derive mathematical programming formulations, with a special focus on cases where linear programming applies.

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