TY - JOUR
T1 - Efficiently solving the redundancy allocation problem using tabu search
AU - Kulturel-Konak, Sadan
AU - Smith, Alice E.
AU - Coit, David W.
N1 - Funding Information:
Alice E. Smith is Philpott-WestPoint Stevens Professor and Chair of Industrial and Systems at Auburn University. Previous to this position, she was on the faculty of the Department of Industrial Engineering at the University of Pittsburgh, which she joined in 1991 after 10 years of industrial experience with Southwestern Bell Corporation. She has degrees in engineering and business from Rice University, Saint Louis University and University of Missouri—Rolla. Her research has been funded by the National Institute of Standards (NIST), Lockheed Martin, Adtranz NA, the Ben Franklin Technology Center of Western Pennsylvania, Daimler-Chrysler, and the National Science Foundation (NSF), from which she was awarded a CAREER grant in 1995 and an ADVANCE Leadership grant in 2001. She served in an editorial capacity for IIE Transactions, INFORMS Journal on Computing and IEEE Transactions on Evolutionary Computation and has authored over 100 refereed publications. She is a senior member of IIE, IEEE and SWE, a member of Tau Beta Pi, INFORMS and ASEE, and a Registered Professional Engineer in Industrial Engineering in Alabama and Pennsylvania. She currently serves as Senior Vice President (Academic) of IIE.
Funding Information:
This research was conducted while Sadan Kulturel-Konak was a Ph.D. candidate in the Industrial and Systems Engineering Department at Auburn University, AL. She would like to thank Dr. Abdullah Konak for valuable discussions during the research. David W. Coit’s contributions were supported by NSF CAREER grant DMII-987416.
PY - 2003/6
Y1 - 2003/6
N2 - A tabu search meta-heuristic has been developed and successfully demonstrated to provide solutions to the system reliability optimization problem of redundancy allocation. Tabu search is particularly well-suited to this problem and it offers distinct advantages compared to alternative optimization methods. While there are many forms of the problem, the redundancy allocation problem generally involves the selection of components and redundancy levels to maximize system reliability given various system-level constraints. This is a common and extensively studied problem involving system design, reliability engineering and operations research. It is becoming increasingly important to develop efficient solutions to this reliability optimization problem because many telecommunications (and other) systems are becoming more complex, yet with short development schedules and very stringent reliability requirements. Tabu search can be applied to a more diverse problem domain compared to mathematical programming methods, yet offers the potential of greater efficiency compared to population-based search methodologies, such as genetic algorithms. The tabu search is demonstrated on numerous variations of three different problems and compared to integer programming and genetic algorithm solutions. The results demonstrate the benefits of tabu search for solving this type of problem.
AB - A tabu search meta-heuristic has been developed and successfully demonstrated to provide solutions to the system reliability optimization problem of redundancy allocation. Tabu search is particularly well-suited to this problem and it offers distinct advantages compared to alternative optimization methods. While there are many forms of the problem, the redundancy allocation problem generally involves the selection of components and redundancy levels to maximize system reliability given various system-level constraints. This is a common and extensively studied problem involving system design, reliability engineering and operations research. It is becoming increasingly important to develop efficient solutions to this reliability optimization problem because many telecommunications (and other) systems are becoming more complex, yet with short development schedules and very stringent reliability requirements. Tabu search can be applied to a more diverse problem domain compared to mathematical programming methods, yet offers the potential of greater efficiency compared to population-based search methodologies, such as genetic algorithms. The tabu search is demonstrated on numerous variations of three different problems and compared to integer programming and genetic algorithm solutions. The results demonstrate the benefits of tabu search for solving this type of problem.
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U2 - 10.1080/07408170304422
DO - 10.1080/07408170304422
M3 - Article
AN - SCOPUS:0038475612
SN - 0740-817X
VL - 35
SP - 515
EP - 526
JO - IIE Transactions (Institute of Industrial Engineers)
JF - IIE Transactions (Institute of Industrial Engineers)
IS - 6
ER -