R-MGSPLINE: Retrospective multi-gradient search for multi-objective simulation optimization on integer lattices. Applegate, E. A. & Hunter, S. R. In Mustafee, N., Bae, K. G., Lazarova-Molnar, S., Rabe, M., Szabo, C., Haas, P., & Son, Y., editors, *Proceedings of the 2019 Winter Simulation Conference*, pages 3516–3527, Piscataway, NJ, 2019. IEEE.

Paper doi abstract bibtex 7 downloads

Paper doi abstract bibtex 7 downloads

We introduce the Retrospective Multi-Gradient Search with Piecewise Linear Interpolation and Neighborhood Enumeration (R-MGSPLINE) algorithm for finding a local efficient point when solving a multi-objective simulation optimization problem on an integer lattice. In this nonlinear optimization problem, all objectives can only be observed with stochastic error, the decision variables are integer-valued, and a local solution is called the efficient set. R-MGSPLINE uses a retrospective approximation (RA) framework to repeatedly call the MGSPLINE sample-path solver at a sequence of increasing sample sizes, using the solution from the previous RA iteration as a warm start for the current RA iteration. The MGSPLINE algorithm performs a line search along a common descent direction constructed from pseudo-gradients of each objective, followed by a neighborhood enumeration for certification. Numerical experiments show that R-MGSPLINE converges to a local weakly efficient point.

@inproceedings{2019apphunWSC, Year = {2019}, Author = {E. A. Applegate and S. R. Hunter}, Title = {{R-MGSPLINE}: {R}etrospective multi-gradient search for multi-objective simulation optimization on integer lattices}, Booktitle = {Proceedings of the 2019 Winter Simulation Conference}, Editor = {N. Mustafee and {K.-H.} G. Bae and S. {Lazarova-Molnar} and M. Rabe and C. Szabo and P. Haas and {Y.-J.} Son}, Publisher = {IEEE}, Address = {Piscataway, NJ}, doi = {10.1109/WSC40007.2019.9004719}, pages = {3516--3527}, url_Paper = {http://web.ics.purdue.edu/~hunter63/PAPERS/2019apphunWSC.pdf}, abstract = {We introduce the Retrospective Multi-Gradient Search with Piecewise Linear Interpolation and Neighborhood Enumeration (R-MGSPLINE) algorithm for finding a local efficient point when solving a multi-objective simulation optimization problem on an integer lattice. In this nonlinear optimization problem, all objectives can only be observed with stochastic error, the decision variables are integer-valued, and a local solution is called the efficient set. R-MGSPLINE uses a retrospective approximation (RA) framework to repeatedly call the MGSPLINE sample-path solver at a sequence of increasing sample sizes, using the solution from the previous RA iteration as a warm start for the current RA iteration. The MGSPLINE algorithm performs a line search along a common descent direction constructed from pseudo-gradients of each objective, followed by a neighborhood enumeration for certification. Numerical experiments show that R-MGSPLINE converges to a local weakly efficient point.}, keywords = {simulation optimization > multi-objective > integer-ordered}}

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E. A.","Hunter, S. R."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","year":"2019","author":[{"firstnames":["E.","A."],"propositions":[],"lastnames":["Applegate"],"suffixes":[]},{"firstnames":["S.","R."],"propositions":[],"lastnames":["Hunter"],"suffixes":[]}],"title":"R-MGSPLINE: Retrospective multi-gradient search for multi-objective simulation optimization on integer lattices","booktitle":"Proceedings of the 2019 Winter Simulation Conference","editor":[{"firstnames":["N."],"propositions":[],"lastnames":["Mustafee"],"suffixes":[]},{"firstnames":["K.-H.","G."],"propositions":[],"lastnames":["Bae"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Lazarova-Molnar"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Rabe"],"suffixes":[]},{"firstnames":["C."],"propositions":[],"lastnames":["Szabo"],"suffixes":[]},{"firstnames":["P."],"propositions":[],"lastnames":["Haas"],"suffixes":[]},{"firstnames":["Y.-J."],"propositions":[],"lastnames":["Son"],"suffixes":[]}],"publisher":"IEEE","address":"Piscataway, NJ","doi":"10.1109/WSC40007.2019.9004719","pages":"3516–3527","url_paper":"http://web.ics.purdue.edu/~hunter63/PAPERS/2019apphunWSC.pdf","abstract":"We introduce the Retrospective Multi-Gradient Search with Piecewise Linear Interpolation and Neighborhood Enumeration (R-MGSPLINE) algorithm for finding a local efficient point when solving a multi-objective simulation optimization problem on an integer lattice. In this nonlinear optimization problem, all objectives can only be observed with stochastic error, the decision variables are integer-valued, and a local solution is called the efficient set. R-MGSPLINE uses a retrospective approximation (RA) framework to repeatedly call the MGSPLINE sample-path solver at a sequence of increasing sample sizes, using the solution from the previous RA iteration as a warm start for the current RA iteration. The MGSPLINE algorithm performs a line search along a common descent direction constructed from pseudo-gradients of each objective, followed by a neighborhood enumeration for certification. Numerical experiments show that R-MGSPLINE converges to a local weakly efficient point.","keywords":"simulation optimization > multi-objective > integer-ordered","bibtex":"@inproceedings{2019apphunWSC,\n\tYear = {2019},\n\tAuthor = {E. A. Applegate and S. R. Hunter},\n\tTitle = {{R-MGSPLINE}: {R}etrospective multi-gradient search for multi-objective simulation optimization on integer lattices},\n\tBooktitle = {Proceedings of the 2019 Winter Simulation Conference},\n\tEditor = {N. Mustafee and {K.-H.} G. Bae and S. {Lazarova-Molnar} and M. Rabe and C. Szabo and P. Haas and {Y.-J.} Son},\n\tPublisher = {IEEE},\n Address = {Piscataway, NJ},\n\tdoi = {10.1109/WSC40007.2019.9004719},\n\tpages = {3516--3527},\n\turl_Paper = {http://web.ics.purdue.edu/~hunter63/PAPERS/2019apphunWSC.pdf},\n\tabstract = {We introduce the Retrospective Multi-Gradient Search with Piecewise Linear Interpolation and Neighborhood Enumeration (R-MGSPLINE) algorithm for finding a local efficient point when solving a multi-objective simulation optimization problem on an integer lattice. In this nonlinear optimization problem, all objectives can only be observed with stochastic error, the decision variables are integer-valued, and a local solution is called the efficient set. R-MGSPLINE uses a retrospective approximation (RA) framework to repeatedly call the MGSPLINE sample-path solver at a sequence of increasing sample sizes, using the solution from the previous RA iteration as a warm start for the current RA iteration. The MGSPLINE algorithm performs a line search along a common descent direction constructed from pseudo-gradients of each objective, followed by a neighborhood enumeration for certification. Numerical experiments show that R-MGSPLINE converges to a local weakly efficient point.},\n\tkeywords = {simulation optimization > multi-objective > integer-ordered}}\n\n","author_short":["Applegate, E. A.","Hunter, S. R."],"editor_short":["Mustafee, N.","Bae, K. G.","Lazarova-Molnar, S.","Rabe, M.","Szabo, C.","Haas, P.","Son, Y."],"key":"2019apphunWSC","id":"2019apphunWSC","bibbaseid":"applegate-hunter-rmgsplineretrospectivemultigradientsearchformultiobjectivesimulationoptimizationonintegerlattices-2019","role":"author","urls":{" paper":"http://web.ics.purdue.edu/~hunter63/PAPERS/2019apphunWSC.pdf"},"keyword":["simulation optimization > multi-objective > integer-ordered"],"metadata":{"authorlinks":{"hunter, s":"https://web.ics.purdue.edu/~hunter63/"}},"downloads":7,"html":""},"bibtype":"inproceedings","biburl":"https://web.ics.purdue.edu/~hunter63/PAPERS/srhunterweb.bib","creationDate":"2019-06-18T12:14:50.838Z","downloads":7,"keywords":["simulation optimization > multi-objective > integer-ordered"],"search_terms":["mgspline","retrospective","multi","gradient","search","multi","objective","simulation","optimization","integer","lattices","applegate","hunter"],"title":"R-MGSPLINE: Retrospective multi-gradient search for multi-objective simulation optimization on integer lattices","year":2019,"dataSources":["PkcXzWbdqPvM6bmCx"]}