Models for Optimum Decision Making

2020 ◽  
2006 ◽  
Vol 34 (3) ◽  
pp. 401-414 ◽  
Author(s):  
Harald L. Battermann ◽  
Udo Broll ◽  
Jack E. Wahl

1970 ◽  
Vol 10 (2) ◽  
pp. 232-263 ◽  
Author(s):  
Azizur Rahman Khan

The concept of capital-intensity, denned as the ratio of capital to labour, has been used widely in both theoretical and applied problems of planning. These ratios have often been used in forecasting, e.g., in measuring the possible expansion of employment that would be generated from a given investment programme and in estimating the rate of investment that would be required in achieving a given employment target. More importantly, these ratios have been recommended for use in optimum decision-making. The usual argument is that in a labour-surplus backward economy with scarcity of capital, it is costless or nearly so to use labour which produces little at the margin. Thus, a given amount of capital, the scarce resource, should be combined with as much labour, the abundant resource, as possible. Minimizing capital-intensity has, thus, emerged as an investment criterion: from alternative sectors those with lowest capital-labour ratios should be expanded and from alternative technical blueprints for each sector the projects with the lowest capital-intensities should be chosen. The corollary in trade theory has been to identify comparative advantage with labour-intensity for the labour-abundant economies.


1976 ◽  
Vol 6 (3) ◽  
pp. 60-62
Author(s):  
Rick Hesse ◽  
Steve Altman

Author(s):  
Mona Abdolrazaghi ◽  
Sherif Hassanien

Abstract Pipeline operators sometimes face challenges in finding an optimum decision while executing both capital and operational projects. Utility- and risk-based decision-making approaches could create a framework for devising an optimum solution, especially in cases involving multi-parameter decision making. From a utility function perspective, an optimum solution would be the one with the least amount of cost and the highest benefit. In other words, an optimum solution could be achieved by maximizing a utility function. Accordingly, as an optimization problem, constraints related to safety, risk, and reliability for a given scenario could limit or dictate the space of feasible solutions. Addressing the problem of arriving at an optimum solution for a multi-parameter decision is the core topic of this paper. Moreover, optimized utility functions — decision making analyses based on probabilistic risk assessment — are discussed. In addition, the paper introduces a conceptual approach that has been utilized for infrastructure decision making: the life quality index (LQI) approach.


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