Data processing: generic control systems or specific application – Generic control system – apparatus or process – Optimization or adaptive control
Reexamination Certificate
2001-05-03
2004-08-24
Patel, Ramesh (Department: 2121)
Data processing: generic control systems or specific application
Generic control system, apparatus or process
Optimization or adaptive control
C700S029000, C700S032000, C700S033000, C700S099000, C700S100000, C703S002000, C703S006000
Reexamination Certificate
active
06782295
ABSTRACT:
BACKGROUND OF THE INVENTION
Many processes for handling, processing, and manufacturing discrete products consist of a series (or set) of sequential process operations (or subsystems) which are de-coupled by means of in-process storage buffers. Such a process, along with its operating rules, is referred to herein as a buffered industrial process (BIP). For example, a commercial bottling operation represents such a process, while the manufacture of such things as computer chips represents yet another example of a buffered industrial manufacturing system. Non-manufacturing systems may include such systems as mail sorting and handling, high speed systems for inventory selection and movement, and the like. In addition, subsystems within the overall system may contain one or more parallel production facilities, or “lanes,” which perform an identical function.
FIG. 1
illustrates a typical BIP having m≧2 subsystems
12
,
14
,
16
and m−1 buffers
18
,
22
,
24
in which the i
th
subsystem has n
i
≧1 lanes. Note that each pair of subsystems is separated by a single buffer (e.g., buffer
18
) that accepts the combined output of all the lanes supplying it and distributes its products to all the lanes that it supplies, i.e., a “common buffer,” as used herein.
The design of a BIP is described by its configuration and operating rules. “Configuration” means the number of lane and buffer subsystems, the number of lanes in each manufacturing subsystem, whether or not the lanes are coupled within a given subsystem, and the buffer capacities. For each subsystem, the lanes have specified failure time (“uptime”) distributions relative to particular lane speeds and repair time (“downtime”) distributions. Also, lane failure times can be represented as being either “good-as-new” (i.e., failure times reset on a lane failure) or “good-as-old” (i.e., failure times accumulate after lane failure). For purposes of the discussion, it will be assumed that buffers cannot fail, although buffer failures can be accommodated.
Other lane boundary conditions include the “maximum speed” at which a given lane can be operated, the “probability of a false lane restart” (i.e., the probability of a loss event that occurs quickly relative to the expected life of the system), and the “time required for a successful restart.” A corresponding buffer boundary condition is the “starting quantity in the buffer.” Suppose “minutes” are the desired time units of interest. Then a BIP also has a required “production limit specification” expressed as the maximum number of products that can be made or handled by the process per minute.
The operating rules for a BIP refer to the “buffer trigger levels,” “lane speeds” and “lane rules.” “Lane rules” are the rules for operating the lanes within a given subsystem; for example, whether or not lanes can be repaired on the fly (i.e., remaining lanes continue to operate after a lane failure and during repair), a repaired lane can be restarted on the fly, or the lanes must wait for a common restart after all lanes have stopped.
The “availability” of a BIP is a product-based availability defined here as the proportion of the number of products made or handled in a specified period of time relative to the potential number of products that could have been made or handled if the process had run without any lane failures during this period. An important problem is how to find configuration and operating rules (that is, BIP designs) that yield high product-based availability. The present invention is directed to this problem. In accordance with our invention, a discrete-event simulation determines the availability of a given BIP design and a genetic algorithm (GA) mutates those BIP designs having high availability until further genetic improvements cease.
The use of a GA to optimize the reliability of a system has been considered by many authors. Gen & Kim (1999) [3] present an excellent state-of-the-art survey on the use of GA-based approaches for various reliability design problems. Coit & Smith (1996) [1] use a GA to optimize the reliability of a series-parallel system. Coit & Smith (1997) [2] discuss a GA to optimize a series-parallel system in which risk profiles of both designer and user are explicitly considered. Painton & Campbell 10 (1995) [13] and Levitin & Lisnianski (1999) [10] also use a GA to optimize the reliability of a series-parallel system. Kumar, Pathak & Gupta (1995) [8] use a GA to optimize the reliability of a computer-network expansion model, while Gen & Cheng (1996) [4] use a GA to optimize the reliability of a redundant system at the subsystem level. Likewise, Ramachandran, Sivakumar & Sathiyanarayanan (1996) [14] take a genetics-based approach to redundancy optimization. While there are many real-world applications of GAs in reliability, several interesting real-world reliability applications in nuclear power plant and power system design are considered in Levitin & Lisnianski (1998) [9] and Levitin & Lisnianski (1999) [11]. However, a GA has not been used to optimize the product-based availability of a complex system, such as a BIP in accordance with our invention.
REFERENCES INCORPORATED HEREIN
[1]D. W. Coit, A. E. Smith, “Reliability optimization of series-parallel systems using a genetic algorithm”, IEEE Trans. Reliability, vol 45, June 1996, pp 254-260.
[2]D. W. Coit, A. E. Smith, “Considering risk profiles in design optimization for series-parallel systems”, 1997 Proc. Annual Reliability & Maintainability Symposium, 1997, pp 271-277.
[3]M. Gen, J. R. Kim, “GA-based reliability design: state-of-the-art survey”, Computers & Ind. Eng., vol 37, 1999, pp 151-155.
[4]M. Gen, R. Cheng, “Optimal design of system reliability using interval programming and genetic algorithms”, Computers & Ind. Eng., vol 31, 1996, pp 151-155.
[5]D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1989; Addison-Wesley.
[6]N. L. Johnson, S. Kotz, Distributions in Statistics: Continuous Univariate Distributions—January 1970; John Wiley.
[7]N. L. Johnson, S. Kotz, Distributions in Statistics: Continuous Multivariate Distributions, 1972; John Wiley.
[8]A. Kumar, R. M. Pathak, Y. P. Gupta, “Genetic-algorithm-based reliability optimization for computer network expansion”, IEEE Trans. Reliability, vol 44, March 1995, pp 63-72.
[9]G. Levitin, A. Lisnianski, “Structure optimization of power system with bridge topology”, Elec. Power Sys. Res., vol 45, 1998, pp 201-208.
[10] G. Levitin, A. Lisnianski, “Joint redundancy and maintenance optimization for multistate series-parallel systems”, Rel. Eng. & Sys. Safety, vol 64, 1999, pp 33-42.
[11] G. Levitin, A. Lisnianski, “Optimal multistage modernization of power system subject to reliability and capacity requirements”, Elec. Power Sys. Res., vol 50, 1999, pp 183-190.
[12] Z. Michalewicz, Genetic Algorithms+Data Structures=Evolution Programs, 1992; Springer-Verlag.
[13] L. Painton, J. Campbell, “Genetic algorithms in optimization of system reliability”, IEEE Trans. Reliability, vol 44, June 1995, pp 172-178.
[14] V. Ramachandran, V. Sivakumar, K. Sathiyanarayanan, “Genetics based redundancy optimization”, Microelectron. Reliab., vol 37, 1996, pp 661-663.
[15] J. E. Yang, M. J. Hwang, T. Y. Sung, Y. Jin, “Application of genetic algorithm for reliability allocation in nuclear power plants”, Rel. Eng. & Sys. Safety, vol 65, 1999, pp 229-238.
Various objects, advantages and novel features of the invention will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following or may be learned by practice of the invention. The objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appe
Berg Eric C.
Hamada Michael S.
Koehler Arthur J.
Martz, Jr. Harry F.
Liu Joshua C
Patel Ramesh
The Regents of the University of California
Wilson Ray G.
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