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Response surface methodology with stochastic constraints for expensive simulation

E Angün, J Kleijnen, D den Hertog, G Gürkan
2009 Journal of the Operational Research Society  
This paper investigates simulation-based optimization problems with a stochastic objective function, stochastic output constraints, and deterministic input constraints.  ...  The heuristic is intended for problems in which each simulation run is expensive and the computer budget is limited, so that the search needs to reach a neighborhood of the true optimum quickly.  ...  Originally, RSM was derived for problems with a single stochastic objective function. Myers and Montgomery (2002) gave the following general description for the first stage of this classic RSM.  ... 
doi:10.1057/palgrave.jors.2602614 fatcat:iu3h2mmazreafm7jwqrixwnzua

Optimization via Simulation Over Discrete Decision Variables [chapter]

Barry L. Nelson
2010 Risk and Optimization in an Uncertain World  
Continuous-decision-variable OvS, and gradient estimation to support it, has been an active research area with significant advances.  ...  Both the simulation research and software communities have been interested in optimization via simulation (OvS), by which we mean maximizing or minimizing the expected value of some output of a stochastic  ...  Stochastic Constraints For many problems it is natural to have a stochastic constraint E [C(x)] ≤ q.  ... 
doi:10.1287/educ.1100.0069 fatcat:uivi5seqira7xfn6ipx4wui6ma

Stochastic operation of home energy management systems including battery cycling

Carlos Adrian Correa-Florez, Alexis Gerossier, Andrea Michiorri, Georges Kariniotakis
2018 Applied Energy  
The complete two-stage stochastic formulation results in a Mixed-Integer Nonlinear Programming problem that is decomposed using a Competitive Swarm Optimizer to handle the calculation of the battery cycling  ...  with a portfolio of prosumers with active demand capability.  ...  Acknowledgements This work was carried out as part of the innovation project SENSIBLE (Storage ENabled SustaInable energy for BuiLdings and communitiEs -www.h2020-projectsensible.eu), which has received  ... 
doi:10.1016/j.apenergy.2018.04.130 fatcat:yyr5mwse7rf2bkhx3zp4zlwzm4

Simulation and optimization in production planning

Jack P.C. Kleijnen
1993 Decision Support Systems  
These 28 variables, however, can be reduced to one criterion variable, namely productive machine hours, which is to be maximized, and one commercial variable measuring lead times, which must satisfy a  ...  This paper reports on a practical decision support system (DSS) for production planning, developed for a Dutch company. To evaluate this DSS, a simulation model is built.  ...  Acknowledgment I benefitted from the discussions with several company employees and with B. Bettonvil (KUB/TUE) and S. Geldof (ITP-TUE/TNO).  ... 
doi:10.1016/0167-9236(93)90058-b fatcat:fgsvc2vibfhy5e2kkrlwb4kxu4

Continuous optimization via simulation using Golden Region search

Alireza Kabirian, Sigurdur Ólafsson
2011 European Journal of Operational Research  
We test these six problems with a small random noise. We also test two of the two-dimensional problems with large random noise.  ...  Linear programming methods: continuous variables with linear known and closed-form functions 2. Linear integer programming methods: discrete variables with linear, known and closed-form functions 3.  ... 
doi:10.1016/j.ejor.2010.09.002 fatcat:rwcven6pwjbnbiki3cjsynmaqu

Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes [article]

Yassine Yaakoubi, Roussos Dimitrakopoulos
2022 arXiv   pre-print
The simultaneous stochastic optimization of mining complexes (SSOMC) is a large-scale stochastic combinatorial optimization problem that simultaneously manages the extraction of materials from multiple  ...  The L2P selects the heuristic (perturbation) to be applied in a self-adaptive manner using reinforcement learning to efficiently explore which local search is best suited for a particular search point.  ...  To overcome the above limitations, the SSOMC [6, 7, 8, 9] accounts for geological uncertainty using two-stage stochastic programming models and links all components of a mining complex and can model  ... 
arXiv:2202.12866v1 fatcat:a7oa3bgy7zg6djbr4yeqqmb25a

Step decision rules for multistage stochastic programming: A heuristic approach

J. Thénié, J.-Ph. Vial
2008 Automatica  
SPSDR is then tested against two alternative methods: regular stochastic programming on a problem with 3 stages and 2 recourses; robust optimization with affinely adjustable recourses on a 12-stage model  ...  Stochastic programming with step decision rules, SPSDR, is an attempt to overcome the curse of computational complexity of multistage stochastic programming problems.  ...  In that respect, stochastic programming with LDR (SPLDR) transforms a multistage SP in an Act-and-See problem, i.e., into a two-stage problem with no recourse.  ... 
doi:10.1016/j.automatica.2008.02.001 fatcat:wf277xqekzbn7mpbsaev4jb7be

Evaluation of a robot learning and planning via extreme value theory

F. Celeste, F. Dambreville, J.P. Le Cadre
2007 2007 10th International Conference on Information Fusion  
Here this evaluation procedure is applied for the problem of optimizing the navigation of a mobile robot in a known environment.  ...  This paper presents a methodology for the evaluation of a path planning algorithm based on a learning approach.  ...  Unlike the other local random search algorithm such as simulated annealing which used the assumption of local neighborhood hypothesis, the CE method tries to solve the problem globally.  ... 
doi:10.1109/icif.2007.4408187 dblp:conf/fusion/CelesteDC07 fatcat:jxzsywza6be4bdos2cl6xjgjua

Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search [article]

Luigi Acerbi, Wei Ji Ma
2017 arXiv   pre-print
First, we present a novel hybrid BO algorithm, Bayesian adaptive direct search (BADS), that achieves competitive performance with an affordable computational overhead for the running time of typical models  ...  Here we explore whether BO can be applied as a general tool for model fitting.  ...  version of this manuscript; John Wixted and colleagues for allowing us to reuse their data for the CCN17 'word recognition memory' problem set; and three anonymous reviewers for useful feedback.  ... 
arXiv:1705.04405v2 fatcat:3smxarrglzbode7v73k4quaste

A computer package for modeling and simulating regionalized count variables

Xavier Emery, Jaime Hernández
2010 Computers & Geosciences  
.  Code available from server at 1 Computer programs are provided for parameter inference and simulation, and an application to a forestry dataset is presented.  ...  In this paper, we propose to model the point distribution by a Cox process, i.e., a Poisson point process with a random regionalized intensity.  ...  Conclusions One motivation of this work was to present a stochastic model for representing discrete regionalized variables associated with count observations.  ... 
doi:10.1016/j.cageo.2009.04.013 fatcat:ytdk7u65nzf43hed4pgmzejx74

The Distributive Effects of Land Titleon Labor Supply: Evidence From Brazil

Mauricio Moura, Caio Piza, Marcos Poplawski-Ribeiro
2011 IMF Working Papers  
The estimates suggest that the impact of land-titling on labor supply is heterogeneous and greater for those households with fewer hours worked before the program.  ...  The role of legal ownership security is isolated by comparing the effect that being part of, or excluded from, a land title program in a unique quasi-experiment in two similar communities in the Brazilian  ...  The Data A two-stage survey focusing on property rights originates the dataset.  ... 
doi:10.5089/9781455259366.001 fatcat:xm7eycrfybhajnk765bqrusuxm

Quantile-Optimal Treatment Regimes

Lan Wang, Yu Zhou, Rui Song, Ben Sherwood
2017 Journal of the American Statistical Association  
We propose an alternative formulation of the estimator as a solution of an optimization problem with an estimated nuisance parameter.  ...  Thus, our results ll an important theoretical gap for a general class of policy search methods in the literature. The paper investigates both static and dynamic treatment regimes.  ...  Example 2 (two-stage DTR).  ... 
doi:10.1080/01621459.2017.1330204 pmid:30416233 pmcid:PMC6223317 fatcat:o3tuxtmmr5bfpmbfmocsjafa6q

Fuzzy Simheuristics: Solving Optimization Problems under Stochastic and Uncertainty Scenarios

Diego Oliva, Pedro Copado, Salvador Hinojosa, Javier Panadero, Daniel Riera, Angel A. Juan
2020 Mathematics  
Simheuristics combine metaheuristics with simulation in order to solve the optimization problems with stochastic elements.  ...  In order to illustrate the potential of fuzzy simheuristics, we consider the team orienteering problem (TOP) under an uncertainty scenario, and perform a series of computational experiments.  ...  Acknowledgments: This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T), and the Erasmus+ Program (2019-I-ES01-KA103  ... 
doi:10.3390/math8122240 fatcat:4mgobabuxfcjtnr2opbza4yb3q

Large Neighborhood Search for Energy Aware Meeting Scheduling in Smart Buildings [chapter]

Boon Ping Lim, Menkes van den Briel, Sylvie Thiébaux, Russell Bent, Scott Backhaus
2015 Lecture Notes in Computer Science  
We extend this work and develop an approach that scales to larger problems by combining mixed integer programming (MIP) with large neighborhood search (LNS).  ...  This approach is far more effective than solving the complete problem as a MIP problem.  ...  Acknowledgments Thanks to Pascal Van Hentenryck for pointing us to optimization problems in the smart buildings space and to Philip Kilby for helpful discussions on LNS.  ... 
doi:10.1007/978-3-319-18008-3_17 fatcat:isyrarmosjcmli25ngnu6aarwa

Feature Article: Optimization for simulation: Theory vs. Practice

Michael C. Fu
2002 INFORMS journal on computing  
A tutorial exposition that summarizes the approaches found in the research literature is included, as well as a discussion contrasting these approaches with the algorithms implemented in commercial software  ...  The main thesis of this article, however, is that there is a disconnect between research in simulation optimization-which has addressed the stochastic nature of discrete-event simulation by concentrating  ...  The author thanks the two referees, the Area Editor, and the Feature Article Editor for their comments that have led to an improved exposition.  ... 
doi:10.1287/ijoc.14.3.192.113 fatcat:ntus2mrdhjbptc77jf2zjmgmcu
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