Data processing: financial – business practice – management – or co – Automated electrical financial or business practice or... – Health care management
Reexamination Certificate
1999-07-06
2001-04-17
Trammell, James P. (Department: 2161)
Data processing: financial, business practice, management, or co
Automated electrical financial or business practice or...
Health care management
C700S099000
Reexamination Certificate
active
06219649
ABSTRACT:
BACKGROUND TECHNICAL FIELD
This invention relates to methods and systems for allocating resources in an uncertain environment.
By reference the following documents, submitted to the Sunnyvale Center for Innovation, Invention and Ideas (SCI
3
) under the US Patent and Trademark Office's Document Disclosure Program, are hereby included:
Title
Number
Date
Method of Allocating Resources in a
S00463
July 9, 1997
Stochastic Environment
Method of Allocating Resources in a
S00730
March 16, 1998
Stochastic Environment — Further
Considerations
Method of Allocating Resources in a
S00743
April 10, 1998
Stochastic Environment — Further
Considerations II
Method of Allocating Resources in a
S00764
May 11, 1998
Stochastic Environment — Further
Considerations III
Method of Allocating Resources in a
S00814
July 24, 1998
Stochastic Environment — Further
Considerations IV
Methods and Apparatus for Allocating
S00901
December 14, 1998
Resources in an Uncertain Environment —
PPA Draft I
Methods and Apparatus for Allocating
S00905
December 18, 1998
Resources in an Uncertain Environment —
PPA Draft II
Methods and Apparatus for Allocating
S00914
January 6, 1999
Resources in an Uncertain Environment —
PPA Draft III
Copending patent application Ser. No. 09/070,130, filed on Apr. 29, 1998, is incorporated herein and termed PRPA (Prior Relevant Patent Application). PRPA discloses and discusses several methods for allocating resources.
BACKGROUND OF PRIOR ART
Almost all organizations and individuals are constantly allocating material, financial, and human resources. Clearly, how best to allocate such resources is of prime importance.
Innumerable methods have been developed to allocate resources, but they usually ignore uncertainty: uncertainty as to whether the resources will be available; uncertainty as to whether the resources will accomplish what is expected; uncertainty as to whether the intended ends prove worthwhile. Arguably, as the increasingly competitive world-market develops, as technological advancements continue, and as civilization becomes ever more complex, uncertainty becomes increasingly the most important consideration for all resource allocations.
Known objective methods for allocating resources in the face of uncertainty can be classified as Detailed-calculation, stochastic programming, scenario analysis, and Financial-calculus. (The terms “Detailed-calculation”, “Financial-calculus”, “Simple-scenario analysis”, and “Convergent-scenario analysis” are being coined here to help categorize prior-art.) (These known objective methods for allocating resources are almost always implemented with the assistance of a computer.)
In Detailed-calculation, probabilistic results of different resource allocations are determined, and then an overall best allocation is selected. The first historic instance of Detailed-calculation, which led to the development of probability theory, was the determination of gambling-bet payoffs to identify the best bets. A modem example of Detailed-calculation is U.S. Pat. No. 5,262,956, issued to DeLeeuw and assigned to Inovec, Inc., where yields for different timber cuts are probabilistically calculated, and the cut with the best probabilistic value is selected. The problem with DeLeeuw's method, and this is a frequent problem with all Detailed-calculation, is its requirement to enumerate and evaluate a list of possible resource allocations. Frequently, because of the enormous number of possibilities, such enumeration and valuation is practically impossible.
Sometimes to allocate resources using Detailed-calculation, a computer simulation is used to evaluate:
z
dc
=E
(
f
dc
(
x
dc
)) (1.0)
where vector x
dc
is a resource allocation plan, the function f
dc
evaluates the allocation in the presence of random, probabilistic, and stochastic events or effects, and E is the mathematical expectation operator. With such simulation capabilities, alternative resource allocations can be evaluated and, of those evaluated, the best identified. Though there are methods to optimize the function, such methods often require significant amounts of computer time and hence are frequently impractical. (See Michael C. Fu's article “Optimization via Simulation: A Review,”
Annals of Operations Research Vol.
53 (1994), p. 199-247 and Georg Ch. Pflug's book
Optimization of stochastic Models: The Interface between Simulation and Optimization,
Kluwer Academic Publishers, Boston, 1996.) (Generally known approximation solution techniques for optimizing equation 1.0 include genetic algorithms and response surface methods.)
A further problem with Detailed-calculation is the difficulty of handling multiple-stage allocations. In such situations, allocations are made in stages and between stages, random variables are realized (become manifest or assume definitive values). A standard solution approach to such multiple-stage Detailed-calculation resource allocations is dynamic programming where, beginning with the last stage, Detailed-calculation is used to contingently optimize last-stage allocations; these contingent last-stage allocations are then used by Detailed-calculation to contingently optimize the next-to-the-last-stage allocations, and so forth. Because dynamic programming builds upon Detailed-calculation, the problems of Detailed-calculation are exacerbated. Further, dynamic programming is frequently difficult to apply.
Stochastic programming is the specialty in operations research/management science (OR/MS) that focuses on extending deterministic optimization techniques (e.g., linear programming, non-linear programming, etc.) to consider uncertainty. The general solution approach is to construct and solve an optimization model that incorporates all the possibilities of what could happen. Unless the resulting optimization model is a linear programming model, the usual problem with such an approach is that the resulting optimization problem is too big to be solved; and aside from size considerations, is frequently unsolvable by known solution means. Creating a linear programming model, on the other hand, frequently requires accepting serious distortions and simplifications. Usually, using more than two stages in a stochastic programming problem is impractical, because the above-mentioned computational problems are seriously aggravated. Assumptions, simplifications, and multi-processor-computer techniques used in special stochastic programming situations fail to serve as a general stochastic-programming solution method.
In Simple-scenario analysis, future possible scenarios are created. The allocations for each are optimized, and then, based upon scenario probabilities, a weighted-average allocation is determined. Sometimes the scenarios and allocations are analyzed and, as a consequence, the weights adjusted. The fundamental problem with this method is that it does not consider how the resulting allocation performs against the scenarios, nor does it make any genuine attempt to develop an allocation that, overall, performs best against all individual scenarios. Related to this fundamental problem is the assumption that optimality occurs at a point central to individual scenario optimizations; in other words, that it is necessarily desirable to hedge allocations. Such hedging could, for example, lead to sub-optimality when, and if, the PRPA uses Simple-scenario analysis for allocating resources: because of economies of scale, it could be preferable to allocate large resource quantities to only a few uses, rather than allocate small quantities to many uses. Another practical example concerns allocating military warheads, where hedging can be counter-productive.
Also related to the fundamental problem of scenario analysis is its inability to accommodate utility functions in general, and von Neumann-Morgenstern (VNM) utility functions in particular. Arguably, according to economic theory, utility functions should be used for all allocations when uncertainty is present. Loosely, a utility function maps outcomes to “happiness.” The VNM utility function, in par
Hamman & Benn
Hayes John W.
Trammell James P.
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