Bi-objective simulation optimization on integer lattices using the epsilon-constraint method in a retrospective approximation framework. Cooper, K., Hunter, S. R., & Nagaraj, K. *INFORMS Journal on Computing*, 32(4):1080–1100, 2020.

Paper doi abstract bibtex

Paper doi abstract bibtex

We consider multi-objective simulation optimization (MOSO) problems on integer lattices, that is, nonlinear optimization problems in which multiple simultaneous objective functions can only be observed with stochastic error, e.g., as output from a Monte Carlo simulation model. The solution to a MOSO problem is the efficient set, which is the set of all feasible decision points that map to non-dominated points in the objective space. For problems with two objectives, we propose the R-PERLE algorithm, which stands for Retrospective Partitioned Epsilon-constraint with Relaxed Local Enumeration. R-PERLE is designed for simulation efficiency and provably converges to a local efficient set under appropriate regularity conditions. It uses a retrospective approximation (RA) framework and solves each resulting bi-objective sample-path problem only to an error tolerance commensurate with the sampling error. R-PERLE uses the sub-algorithm RLE to certify it has found a sample-path approximate local efficient set. We also propose R-MinRLE, which is a provably-convergent benchmark algorithm for problems with two or more objectives. R-PERLE performs favorably relative to R-MinRLE and the current state of the art, MO-COMPASS, in our numerical experiments. This work points to a family of RA algorithms for MOSO on integer lattices that employ RLE to certify sample-path approximate local efficient sets, and for which we provide the convergence guarantees.

@article{2020coohunnag, Year = {2020}, Author = {K. Cooper and S. R. Hunter and K. Nagaraj}, Title = {Bi-objective simulation optimization on integer lattices using the epsilon-constraint method in a retrospective approximation framework}, journal = {INFORMS Journal on Computing}, volume = {32}, number = {4}, pages = {1080--1100}, doi = {10.1287/ijoc.2019.0918}, url_Paper = {http://web.ics.purdue.edu/~hunter63/PAPERS/pre2019coohunnag.pdf}, abstract = {We consider multi-objective simulation optimization (MOSO) problems on integer lattices, that is, nonlinear optimization problems in which multiple simultaneous objective functions can only be observed with stochastic error, e.g., as output from a Monte Carlo simulation model. The solution to a MOSO problem is the efficient set, which is the set of all feasible decision points that map to non-dominated points in the objective space. For problems with two objectives, we propose the R-PERLE algorithm, which stands for Retrospective Partitioned Epsilon-constraint with Relaxed Local Enumeration. R-PERLE is designed for simulation efficiency and provably converges to a local efficient set under appropriate regularity conditions. It uses a retrospective approximation (RA) framework and solves each resulting bi-objective sample-path problem only to an error tolerance commensurate with the sampling error. R-PERLE uses the sub-algorithm RLE to certify it has found a sample-path approximate local efficient set. We also propose R-MinRLE, which is a provably-convergent benchmark algorithm for problems with two or more objectives. R-PERLE performs favorably relative to R-MinRLE and the current state of the art, MO-COMPASS, in our numerical experiments. This work points to a family of RA algorithms for MOSO on integer lattices that employ RLE to certify sample-path approximate local efficient sets, and for which we provide the convergence guarantees.}, keywords = {simulation optimization > multi-objective > integer-ordered}}

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K.","Hunter, S. R.","Nagaraj, K."],"bibdata":{"bibtype":"article","type":"article","year":"2020","author":[{"firstnames":["K."],"propositions":[],"lastnames":["Cooper"],"suffixes":[]},{"firstnames":["S.","R."],"propositions":[],"lastnames":["Hunter"],"suffixes":[]},{"firstnames":["K."],"propositions":[],"lastnames":["Nagaraj"],"suffixes":[]}],"title":"Bi-objective simulation optimization on integer lattices using the epsilon-constraint method in a retrospective approximation framework","journal":"INFORMS Journal on Computing","volume":"32","number":"4","pages":"1080–1100","doi":"10.1287/ijoc.2019.0918","url_paper":"http://web.ics.purdue.edu/~hunter63/PAPERS/pre2019coohunnag.pdf","abstract":"We consider multi-objective simulation optimization (MOSO) problems on integer lattices, that is, nonlinear optimization problems in which multiple simultaneous objective functions can only be observed with stochastic error, e.g., as output from a Monte Carlo simulation model. The solution to a MOSO problem is the efficient set, which is the set of all feasible decision points that map to non-dominated points in the objective space. For problems with two objectives, we propose the R-PERLE algorithm, which stands for Retrospective Partitioned Epsilon-constraint with Relaxed Local Enumeration. R-PERLE is designed for simulation efficiency and provably converges to a local efficient set under appropriate regularity conditions. It uses a retrospective approximation (RA) framework and solves each resulting bi-objective sample-path problem only to an error tolerance commensurate with the sampling error. R-PERLE uses the sub-algorithm RLE to certify it has found a sample-path approximate local efficient set. We also propose R-MinRLE, which is a provably-convergent benchmark algorithm for problems with two or more objectives. R-PERLE performs favorably relative to R-MinRLE and the current state of the art, MO-COMPASS, in our numerical experiments. This work points to a family of RA algorithms for MOSO on integer lattices that employ RLE to certify sample-path approximate local efficient sets, and for which we provide the convergence guarantees.","keywords":"simulation optimization > multi-objective > integer-ordered","bibtex":"@article{2020coohunnag,\n\tYear = {2020},\n\tAuthor = {K. Cooper and S. R. Hunter and K. Nagaraj},\n\tTitle = {Bi-objective simulation optimization on integer lattices using the epsilon-constraint method in a retrospective approximation framework},\n\tjournal = {INFORMS Journal on Computing},\n\tvolume = {32},\n\tnumber = {4},\n\tpages = {1080--1100},\n\tdoi = {10.1287/ijoc.2019.0918},\n\turl_Paper = {http://web.ics.purdue.edu/~hunter63/PAPERS/pre2019coohunnag.pdf},\n\tabstract = {We consider multi-objective simulation optimization (MOSO) problems on integer lattices, that is, nonlinear optimization problems in which multiple simultaneous objective functions can only be observed with stochastic error, e.g., as output from a Monte Carlo simulation model. The solution to a MOSO problem is the efficient set, which is the set of all feasible decision points that map to non-dominated points in the objective space. For problems with two objectives, we propose the R-PERLE algorithm, which stands for Retrospective Partitioned Epsilon-constraint with Relaxed Local Enumeration. R-PERLE is designed for simulation efficiency and provably converges to a local efficient set under appropriate regularity conditions. It uses a retrospective approximation (RA) framework and solves each resulting bi-objective sample-path problem only to an error tolerance commensurate with the sampling error. R-PERLE uses the sub-algorithm RLE to certify it has found a sample-path approximate local efficient set. We also propose R-MinRLE, which is a provably-convergent benchmark algorithm for problems with two or more objectives. R-PERLE performs favorably relative to R-MinRLE and the current state of the art, MO-COMPASS, in our numerical experiments. This work points to a family of RA algorithms for MOSO on integer lattices that employ RLE to certify sample-path approximate local efficient sets, and for which we provide the convergence guarantees.},\n\tkeywords = {simulation optimization > multi-objective > integer-ordered}}\n\n","author_short":["Cooper, K.","Hunter, S. R.","Nagaraj, K."],"key":"2020coohunnag","id":"2020coohunnag","bibbaseid":"cooper-hunter-nagaraj-biobjectivesimulationoptimizationonintegerlatticesusingtheepsilonconstraintmethodinaretrospectiveapproximationframework-2020","role":"author","urls":{" paper":"http://web.ics.purdue.edu/~hunter63/PAPERS/pre2019coohunnag.pdf"},"keyword":["simulation optimization > multi-objective > integer-ordered"],"metadata":{"authorlinks":{"hunter, s":"https://web.ics.purdue.edu/~hunter63/"}}},"bibtype":"article","biburl":"http://web.ics.purdue.edu/~hunter63/PAPERS/srhunterweb.bib","creationDate":"2020-01-06T21:54:05.565Z","downloads":36,"keywords":["simulation optimization > multi-objective > integer-ordered"],"search_terms":["objective","simulation","optimization","integer","lattices","using","epsilon","constraint","method","retrospective","approximation","framework","cooper","hunter","nagaraj"],"title":"Bi-objective simulation optimization on integer lattices using the epsilon-constraint method in a retrospective approximation framework","year":2020,"dataSources":["ZEwmdExPMCtzAbo22","PkcXzWbdqPvM6bmCx"]}