Adaptive Sampling Line Search for Simulation Optimization
| dc.contributor.author | Ragavan, Prasanna Kumar | en |
| dc.contributor.committeechair | Pasupathy, Raghu | en |
| dc.contributor.committeechair | Taaffe, Michael R. | en |
| dc.contributor.committeemember | Bish, Douglas R. | en |
| dc.contributor.committeemember | Wernz, Christian | en |
| dc.contributor.department | Industrial and Systems Engineering | en |
| dc.date.accessioned | 2018-08-31T06:00:32Z | en |
| dc.date.available | 2018-08-31T06:00:32Z | en |
| dc.date.issued | 2017-03-08 | en |
| dc.description.abstract | This thesis is concerned with the development of algorithms for simulation optimization (SO), a special case of stochastic optimization where the objective function can only be evaluated through noisy observations from a simulation. Deterministic techniques, when directly applied to simulation optimization problems fail to converge due to their inability to handle randomness thus requiring sophisticated algorithms. However, many existing algorithms dedicated for simulation optimization often show poor performance on implementation as they require extensive parameter tuning. To overcome these shortfalls with existing SO algorithms, we develop ADALINE, a line search based algorithm that eliminates the need for any user defined parameters. ADALINE is designed to identify a local minimum on continuous and integer ordered feasible sets. ADALINE on a continuous feasible set mimics deterministic line search algorithms, while it iterates between a line search and an enumeration procedure on integer ordered feasible sets in its quest to identify a local minimum. ADALINE improves upon many of the existing SO algorithms by determining the sample size adaptively as a trade-off between the error due to estimation and the optimization error, that is, the algorithm expends simulation effort proportional to the quality of the incumbent solution. We also show that ADALINE converges ``almost surely'' to the set of local minima. Finally, our numerical results suggest that ADALINE converges to a local minimum faster, outperforming other advanced SO algorithms that utilize variable sampling strategies. To demonstrate the performance of our algorithm on a practical problem, we apply ADALINE in solving a surgery rescheduling problem. In the rescheduling problem, the objective is to minimize the cost of disruptions to an existing schedule shared between multiple surgical specialties while accommodating semi-urgent surgeries that require expedited intervention. The disruptions to the schedule are determined using a threshold based heuristic and ADALINE identifies the best threshold levels for various surgical specialties that minimizes the expected total cost of disruption. A comparison of the solutions obtained using a Sample Average Approximation (SAA) approach, and ADALINE is provided. We find that the adaptive sampling strategy in ADALINE identifies a better solution quickly than SAA. | en |
| dc.description.abstractgeneral | This thesis is concerned with the development of algorithms for simulation optimization (SO), where the objective function does not have an analytical form, and can only be estimated through noisy observations from a simulation. Deterministic techniques, when directly applied to simulation optimization problems fail to converge due to their inability to handle randomness thus requiring sophisticated algorithms. However, many existing algorithms dedicated for simulation optimization often show poor performance on implementation as they require extensive parameter tuning. To overcome these shortfalls with existing SO algorithms, we develop ADALINE, a line search based algorithm that minimizes the need for user defined parameter. ADALINE is designed to identify a local minimum on continuous and integer ordered feasible sets. ADALINE on continuous feasible sets mimics deterministic line search algorithms, while it iterates between a line search and an enumeration procedure on integer ordered feasible sets in its quest to identify a local minimum. ADALINE improves upon many of the existing SO algorithms by determining the sample size adaptively as a trade-off between the error due to estimation and the optimization error, that is, the algorithm expends simulation effort proportional to the quality of the incumbent solution. Finally, our numerical results suggest that ADALINE converges to a local minimum faster than the best available SO algorithm for the purpose. To demonstrate the performance of our algorithm on a practical problem, we apply ADALINE in solving a surgery rescheduling problem. In the rescheduling problem, the objective is to minimize the cost of disruptions to an existing schedule shared between multiple surgical specialties while accommodating semi-urgent surgeries that require expedited intervention. The disruptions to the schedule are determined using a threshold based heuristic and ADALINE identifies the best threshold levels for various surgical specialties that minimizes the expected total cost of disruption. A comparison of the solutions obtained using traditional optimization techniques, and ADALINE is provided. We find that the adaptive sampling strategy in ADALINE identifies a better solution more quickly than traditional optimization. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:10023 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/84938 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Simulation Optimization | en |
| dc.subject | Adaptive Sampling | en |
| dc.subject | Line Search | en |
| dc.subject | Discrete Simulation Optimization | en |
| dc.subject | ADALINE | en |
| dc.title | Adaptive Sampling Line Search for Simulation Optimization | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Industrial and Systems Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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