Multistate series-parallel system expansion-scheduling subject to availability constraints
This paper addresses the multistage expansion problem for multistate series-parallel systems. The study period is divided into several stages. At each stage the demand distribution is predicted in the form of a cumulative demand curve. The additional elements chosen from a list of available products...
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| Published in | IEEE transactions on reliability Vol. 49; no. 1; pp. 71 - 79 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.03.2000
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-9529 1558-1721 |
| DOI | 10.1109/24.855538 |
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| Abstract | This paper addresses the multistage expansion problem for multistate series-parallel systems. The study period is divided into several stages. At each stage the demand distribution is predicted in the form of a cumulative demand curve. The additional elements chosen from a list of available products can be included into any system-component at any stage to increase the total system capacity and/or reliability. Each element is characterized by its capacity (productivity), availability, and cost. The objective is to minimize the sum of costs of the investments over the study period while satisfying reliability constraints at each stage. To solve the problem, a genetic algorithm is used as an optimization tool. The solution encoding technique allows the genetic algorithm to manipulate integer strings representing multistage expansion planes. A solution quality index comprises both reliability and cost estimations. The procedure based on the universal generating function is used for evaluating the availability of multistate series-parallel systems. An example illustrates finding the optimal expansion plan for a coal-transportation system of a power station. |
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| AbstractList | This paper addresses the multistage expansion problem for multistate series-parallel systems. The study period is divided into several stages. At each stage the demand distribution is predicted in the form of a cumulative demand curve. The additional elements chosen from a list of available products can be included into any system-component at any stage to increase the total system capacity and/or reliability. Each element is characterized by its capacity (productivity), availability, and cost. The objective is to minimize the sum of costs of the investments over the study period while satisfying reliability constraints at each stage. To solve the problem, a genetic algorithm is used as an optimization tool. The solution encoding technique allows the genetic algorithm to manipulate integer strings representing multistage expansion planes. A solution quality index comprises both reliability & cost estimations. The procedure based on the universal generating function is used for evaluating the availability of multistate series-parallel systems. An example illustrates finding the optimal expansion plan for a coal-transportation system of a power station. This paper addresses the multistage expansion problem for multistate series-parallel systems. The study period is divided into several stages. At each stage the demand distribution is predicted in the form of a cumulative demand curve. The additional elements chosen from a list of available products can be included into any system-component at any stage to increase the total system capacity and/or reliability. Each element is characterized by its capacity (productivity), availability, and cost. The objective is to minimize the sum of costs of the investments over the study period while satisfying reliability constraints at each stage. To solve the problem, a genetic algorithm is used as an optimization tool. The solution encoding technique allows the genetic algorithm to manipulate integer strings representing multistage expansion planes. A solution quality index comprises both reliability and cost estimations. The procedure based on the universal generating function is used for evaluating the availability of multistate series-parallel systems. An example illustrates finding the optimal expansion plan for a coal-transportation system of a power station. This paper addresses the multistage expansion problem for multistate series-parallel systems. The study period is divided into several stages. At each stage the demand distribution is predicted in the form of a cumulative demand curve. The additional elements chosen from a list of available products can be included into any system-component at any stage to increase the total system capacity and/or reliability. Each element is characterized by its capacity (productivity), availability, and cost. The objective is to minimize the sum of costs of the investments over the study period while satisfying reliability constraints at each stage. To solve the problem, a genetic algorithm is used as an optimization tool. The solution encoding technique allows the genetic algorithm to manipulate integer strings representing multistage expansion planes. A solution quality index comprises both reliability and cost estimations. The procedure based on the universal generating function is used for evaluating the availability of multistate series-parallel systems. An example illustrates finding the optimal expansion plan for a coal-transportation system of a power station The solution encoding technique allows the genetic algorithm to manipulate integer strings representing multistage expansion planes. |
| Author | Levitin, G. |
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| Cites_doi | 10.1016/B978-0-08-050684-5.50009-4 10.1109/24.387368 10.1109/24.510811 10.1016/0360-8352(96)00120-9 10.1002/9780470172414 10.1016/0360-8352(96)00040-X 10.1016/S0378-7796(97)01155-3 10.1109/24.722283 10.1016/S0026-2714(96)00096-0 10.1109/24.376523 |
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| References | ref13 gen (ref7) 1997 ref12 ref11 ref10 goldberg (ref6) 1989 whitley (ref16) 1988 ref1 ref17 ushakov (ref2) 1987; 25 ushakov (ref5) 1986; 24 ref8 powell (ref18) 1993 ref9 yokota (ref14) 1995; 7 ref4 ref3 sasaki (ref15) 1995; 7 |
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| SubjectTerms | Availability Constraint optimization Costs Demand Demand curves Encoding Expansion Genetic algorithms Investments Marketing Multistage Optimization Power generation Power system reliability Productivity Redundancy Strings Studies |
| Title | Multistate series-parallel system expansion-scheduling subject to availability constraints |
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