作者：
Anand, POpen Univ
Fac Social Sci Econ Discipline Milton Keynes MK7 6AA Bucks England
The paper contributes to the use of social choice and welfare theory in health economics by developing and applying the integration of claims framework to health-care rationing. Related to Sen's critique of neo-cl...
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The paper contributes to the use of social choice and welfare theory in health economics by developing and applying the integration of claims framework to health-care rationing. Related to Sen's critique of neo-classical welfare economics, the integration of claims framework recognises three primitive sources of claim: consequences, deontology and procedures. A taxonomy is presented with the aid of which it is shown that social welfare functions reflecting these claims individually or together, can be specified. Some of the resulting social choice rules can be regarded as generalisations of health-maximisation and all have normative justifications, though the justifications may not be universally acceptable. The paper shows how non-linear programming can be used to operationalise such choice rules and illustrates their differential impacts on the optimal provision of health-care. Following discussion of relations to the capabilities framework and the context in which rationing occurs, the paper concludes that the integration of claims provides a viable framework for modelling health-care rationing that is technically rigorous, general and tractable, as well as being consistent with relevant moral considerations and citizen preferences. (C) 2003 Elsevier Science B.V. All rights reserved.
We study the probabilistic programming problems with multi-choice parameters proposed by Acharya and Biswal (2011) that was published in Opsearch. We point out that the multi-choice parameters can be simplified to avo...
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We study the probabilistic programming problems with multi-choice parameters proposed by Acharya and Biswal (2011) that was published in Opsearch. We point out that the multi-choice parameters can be simplified to avoid the complicated solution procedure proposed by Acharya and Biswal (2011). We use the same numerical example of Acharya and Biswal (2011) to demonstrate that our simplification is effective to derive better minimum.
Most existing linearprogramming (LP) models have optimization objectives that are very different from Fisher's linear discriminant function (FLDF). An LP technique that adapts to FLDF to solve the two-group class...
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Most existing linearprogramming (LP) models have optimization objectives that are very different from Fisher's linear discriminant function (FLDF). An LP technique that adapts to FLDF to solve the two-group classification problem is desirable, as FLDF is one of the most popular classification rules. Therefore, this paper introduces a piecewise linearprogramming (PLP-p) approach that has an optimization objective very similar to that of FLDF to solve the two-group classification problem in discriminant analysis. Moreover, the paper compares the classificatory performance between FLDF and the new PLP-p model, and shows that the results from both approaches are as good as each other when applied to three published data sets. However, the new PLP-p is more flexible than FLDF in terms of adding different types of constraints and weighting individual observations. The results of a simulation. experiment confirm the value of our proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.
We discuss computational enhancements for the low-rank semidefinite programming algorithm, including the extension to block semidefinite programs (SDPs), an exact linesearch procedure, and a dynamic rank reduction sch...
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We discuss computational enhancements for the low-rank semidefinite programming algorithm, including the extension to block semidefinite programs (SDPs), an exact linesearch procedure, and a dynamic rank reduction scheme. A truncated-Newton method is also introduced, and several preconditioning strategies are proposed. Numerical experiments illustrating these enhancements are provided on a wide class of test problems. In particular, the truncated-Newton variant is able to achieve high accuracy in modest amounts of time on maximum-cut-type SDPs.
We study optimization techniques for makespan minimizing workforce assignment problems wherein human learning is explicitly modeled. The key challenge in solving these problems is that the learning functions that map ...
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We study optimization techniques for makespan minimizing workforce assignment problems wherein human learning is explicitly modeled. The key challenge in solving these problems is that the learning functions that map experience to worker productivity are usually nonlinear. This paper presents a set of techniques that enable the solution of much larger instances of such problems than seen in the literature to date. The first technique is an exact linear reformulation for the general makespan minimizing workforce assignment models with learning. Next, we introduce a computationally efficient means for generating an initial feasible solution (which our computational experiments indicate is often near optimal). Finally, we present methods for strengthening the formulation with cover inequalities and a lower bound on the objective function value of the optimal solution. With an extensive computational study we demonstrate the value of these techniques and that large instances can be solved much faster than have previously been solved in the literature. To focus the paper on the presented methodology, we solve a makespan minimizing workforce assignment problem that has few complicating constraints. However, the techniques can be adapted to speed up the solution of most any makespan minimizing workforce assignment problem. (C) 2016 Elsevier Ltd. All rights reserved.
Wind farm layout optimization is one of the challenging problems in the field of renewable energy. In the present study, a new nonlinear mathematical model for layout of wind turbines under multiple wake effects is pr...
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Wind farm layout optimization is one of the challenging problems in the field of renewable energy. In the present study, a new nonlinear mathematical model for layout of wind turbines under multiple wake effects is proposed considering two objective functions separately: maximization of total power production and minimization of cost per power. To incorporate multiple wake effects into the proposed model, Jensen's wake decay model is employed. It was proven that the proposed model has totally unimodularity property and according to this property, relaxation of binary decision variables related with the wind turbine locations makes the model relatively simple to solve. Computational study reveals that results of total power production and cost of power obtained from the proposed model outperform that of the previous studies in the literature on a set of example cases and therefore, can be used to layout more productive wind farms. (C) 2018 Elsevier Ltd. All rights reserved.
Concave objective functions which are both piecewise linear and separable are often encountered in a wide variety of management science problems. Provided the constraints are linear, problems of this kind are normally...
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Concave objective functions which are both piecewise linear and separable are often encountered in a wide variety of management science problems. Provided the constraints are linear, problems of this kind are normally forced into a linearprogramming mould and solved using the simplex method. This paper takes another look at the associated linear programs and shows that they have special structural features which are not exploited by the simplex algorithm. It suggests that their variables can be divided into special ordered sets which can then be used to guide the pivoting strategies of the simplex algorithm with a resultant reduction in basis changes.
Regularity conditions or constraint qualifications play an important role in mathematical programming. In this article we present a relaxed version of the constant rank constraint qualification (CRCQ) which is weaker ...
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Regularity conditions or constraint qualifications play an important role in mathematical programming. In this article we present a relaxed version of the constant rank constraint qualification (CRCQ) which is weaker than the original CRCQ for mixed-constrained non-linear programming problems and is still a regularity condition. The main aim of this article is to show that the relaxed CRCQ (and, consequently, CRCQ too) implies the R-regularity (in other terms the error bound property) of a system of inequalities and equalities. In the same way we prove that the constant positive linear dependence (CPLD) condition also implies R-regularity.
We assume a monopolistic market for a non-durable non-renewable resource such as crude oil, phosphates or fossil water. Stating the problem of obtaining optimal policies on extraction and pricing of the resource as a ...
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We assume a monopolistic market for a non-durable non-renewable resource such as crude oil, phosphates or fossil water. Stating the problem of obtaining optimal policies on extraction and pricing of the resource as a non-linear program allows general conclusions to be drawn under diverse assumptions about the demand curve, discount rates and length of the planning horizon. We compare the results with some common beliefs about the pace of exhaustion of this kind of resources.
A primal-dual interior point algorithm for solving general nonlinearprogramming problems is presented. The algorithm solves the perturbed optimality conditions by applying a quasi-Newton method, where the Hessian of ...
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A primal-dual interior point algorithm for solving general nonlinearprogramming problems is presented. The algorithm solves the perturbed optimality conditions by applying a quasi-Newton method, where the Hessian of the Lagrangian is replaced by a positive definite approximation. An approximation of Fletcher's exact and differentiable merit function together with line-search procedures are incorporated into the algorithm. The line-search procedures are used to modify the length of the step so that the value of the merit function is always reduced. Different step-sizes are used for the primal and dual variables. The search directions are ensured to be descent for the merit function, which is thus used to guide the algorithm to an optimum solution of the constrained optimisation problem. The monotonic decrease of the merit Function at each iteration, ensures the global convergence of the algorithm. Finally, preliminary numerical results demonstrate the efficient performance of the algorithm for a variety of problems.
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