Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/233452
Title: Approximately Optimal Computing Budget Allocation for Selection of the Best and Worst Designs
Authors: JunQi Zhang;Liang Zhang;Cheng Wang;MengChu Zhou
Year: 2017
Publisher: IEEE
Abstract: Ordinal optimization is an efficient technique to choose and rank various engineering designs that require time-consuming discrete-event simulations. Optimal computing budget allocation (OCBA) has been an important tool to enhance its efficiency such that the best design is selected in a timely fashion. It, however, fails to address the issue of selecting the best and worst designs efficiently. The need to select both rapidly given a fixed computing budget has arisen from many applications. This work develops a new OCBA-based approach for selecting both best and worst designs at the same time. Its theoretical foundation is laid. Our numerical results show that it can well outperform all the existing methods in terms of probability of correct selection and computational efficiency.
URI: http://localhost/handle/Hannan/233452
volume: 62
issue: 7
More Information: 3249,
3261
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7742364.pdf744.28 kBAdobe PDF
Title: Approximately Optimal Computing Budget Allocation for Selection of the Best and Worst Designs
Authors: JunQi Zhang;Liang Zhang;Cheng Wang;MengChu Zhou
Year: 2017
Publisher: IEEE
Abstract: Ordinal optimization is an efficient technique to choose and rank various engineering designs that require time-consuming discrete-event simulations. Optimal computing budget allocation (OCBA) has been an important tool to enhance its efficiency such that the best design is selected in a timely fashion. It, however, fails to address the issue of selecting the best and worst designs efficiently. The need to select both rapidly given a fixed computing budget has arisen from many applications. This work develops a new OCBA-based approach for selecting both best and worst designs at the same time. Its theoretical foundation is laid. Our numerical results show that it can well outperform all the existing methods in terms of probability of correct selection and computational efficiency.
URI: http://localhost/handle/Hannan/233452
volume: 62
issue: 7
More Information: 3249,
3261
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7742364.pdf744.28 kBAdobe PDF
Title: Approximately Optimal Computing Budget Allocation for Selection of the Best and Worst Designs
Authors: JunQi Zhang;Liang Zhang;Cheng Wang;MengChu Zhou
Year: 2017
Publisher: IEEE
Abstract: Ordinal optimization is an efficient technique to choose and rank various engineering designs that require time-consuming discrete-event simulations. Optimal computing budget allocation (OCBA) has been an important tool to enhance its efficiency such that the best design is selected in a timely fashion. It, however, fails to address the issue of selecting the best and worst designs efficiently. The need to select both rapidly given a fixed computing budget has arisen from many applications. This work develops a new OCBA-based approach for selecting both best and worst designs at the same time. Its theoretical foundation is laid. Our numerical results show that it can well outperform all the existing methods in terms of probability of correct selection and computational efficiency.
URI: http://localhost/handle/Hannan/233452
volume: 62
issue: 7
More Information: 3249,
3261
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7742364.pdf744.28 kBAdobe PDF