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Author Barrera, J.; Homem-De-Mello, T.; Moreno, E.; Pagnoncelli, B.K.; Canessa, G. pdf  doi
openurl 
  Title Chance-constrained problems and rare events: an importance sampling approach Type
  Year 2016 Publication Mathematical Programming Abbreviated Journal Math. Program.  
  Volume 157 Issue 1 Pages 153-189  
  Keywords Chance-constrained programming; Sample average approximation; Importance sampling; Rare-event simulation  
  Abstract We study chance-constrained problems in which the constraints involve the probability of a rare event. We discuss the relevance of such problems and show that the existing sampling-based algorithms cannot be applied directly in this case, since they require an impractical number of samples to yield reasonable solutions. We argue that importance sampling (IS) techniques, combined with a Sample Average Approximation (SAA) approach, can be effectively used in such situations, provided that variance can be reduced uniformly with respect to the decision variables. We give sufficient conditions to obtain such uniform variance reduction, and prove asymptotic convergence of the combined SAA-IS approach. As it often happens with IS techniques, the practical performance of the proposed approach relies on exploiting the structure of the problem under study; in our case, we work with a telecommunications problem with Bernoulli input distributions, and show how variance can be reduced uniformly over a suitable approximation of the feasibility set by choosing proper parameters for the IS distributions. Although some of the results are specific to this problem, we are able to draw general insights that can be useful for other classes of problems. We present numerical results to illustrate our findings.  
  Address [Barrera, Javiera; Moreno, Eduardo] Univ Adolfo Ibanez, Fac Sci & Engn, Santiago, Chile, Email: javiera.barrera@uai.cl  
  Corporate Author Thesis  
  Publisher Springer Heidelberg Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0025-5610 ISBN Medium  
  Area Expedition (up) Conference  
  Notes WOS:000375568400007 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 613  
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Author Canessa, G.; Gallego, J.A.; Ntaimo, L.; Pagnoncelli, B.K. pdf  doi
openurl 
  Title An algorithm for binary linear chance-constrained problems using IIS Type
  Year 2019 Publication Computational Optimization And Applications Abbreviated Journal Comput. Optim. Appl.  
  Volume 72 Issue 3 Pages 589-608  
  Keywords Chance-constrained programming; Infeasible irreducible subsystems; Integer programming  
  Abstract We propose an algorithm based on infeasible irreducible subsystems to solve binary linear chance-constrained problems with random technology matrix. By leveraging on the problem structure we are able to generate good quality upper bounds to the optimal value early in the algorithm, and the discrete domain is used to guide us efficiently in the search of solutions. We apply our methodology to individual and joint binary linear chance-constrained problems, demonstrating the ability of our approach to solve those problems. Extensive numerical experiments show that, in some cases, the number of nodes explored by our algorithm is drastically reduced when compared to a commercial solver.  
  Address [Canessa, Gianpiero] Univ Adolfo Ibanez, Diagonal Torres 2640, Santiago 7941169, Chile, Email: gianpiero.canessa@edu.uai.cl  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0926-6003 ISBN Medium  
  Area Expedition (up) Conference  
  Notes WOS:000463792400003 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 996  
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Author Barrera, J.; Carrasco, R.A.; Mondschein, S.; Canessa, G.; Rojas-Zalazar, D. doi  openurl
  Title Operating room scheduling under waiting time constraints: the Chilean GES plan Type
  Year 2020 Publication Annals Of Operations Research Abbreviated Journal Ann. Oper. Res.  
  Volume 286 Issue 1-2 Pages 501-527  
  Keywords Scheduling; Operating theater; Operating room scheduling  
  Abstract In 2000, Chile introduced profound health reforms to achieve a more equitable and fairer system (GES plan). The reforms established a maximum waiting time between diagnosis and treatment for a set of diseases, described as an opportunity guarantee within the reform. If the maximum waiting time is exceeded, the patient is referred to another (private) facility and receives a voucher to cover the additional expenses. This voucher is paid by the health provider that had to do the procedure, which generally is a public hospital. In general, this reform has improved the service for patients with GES pathologies at the expense of patients with non-GES pathologies. These new conditions create a complicated planning scenario for hospitals, in which the hospital's OR Manager must balance the fulfillment of these opportunity guarantees and the timely service of patients not covered by the guarantee. With the collaboration of the Instituto de Neurocirugia, in Santiago, Chile, we developed a mathematical model based on stochastic dynamic programming to schedule surgeries in order to minimize the cost of referrals to the private sector. Given the large size of the state space, we developed an heuristic to compute good solutions in reasonable time and analyzed its performance. Our experimental results, with both simulated and real data, show that our algorithm performs close to optimum and improves upon the current practice. When we compared the results of our heuristic against those obtained by the hospital's OR manager in a simulation setting with real data, we reduced the overtime from occurring 21% of the time to zero, and the non-GES average waiting list's length from 71 to 58 patients, without worsening the average throughput.  
  Address [Barrera, Javiera; Carrasco, Rodrigo A.; Mondschein, Susana; Canessa, Gianpiero] Univ Adolfo Ibanez, Fac Engn & Sci, Santiago, Chile, Email: javiera.barrera@uai.cl;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0254-5330 ISBN Medium  
  Area Expedition (up) Conference  
  Notes WOS:000511564300021 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1104  
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Author Canessa, G.; Moreno, E.; Pagnoncelli, B.K. doi  openurl
  Title The risk-averse ultimate pit problem Type
  Year 2021 Publication Optimization And Engineering Abbreviated Journal Optim. Eng.  
  Volume Early access Issue Pages 24 pp  
  Keywords Ultimate pit; Mining; Risk-averse optimization; Integer programming  
  Abstract In this work, we consider a risk-averse ultimate pit problem where the grade of the mineral is uncertain. We derive conditions under which we can generate a set of nested pits by varying the risk level instead of using revenue factors. We propose two properties that we believe are desirable for the problem: risk nestedness, which means the pits generated for different risk aversion levels should be contained in one another, and additive consistency, which states that preferences in terms of order of extraction should not change if independent sectors of the mine are added as precedences. We show that only an entropic risk measure satisfies these properties and propose a two-stage stochastic programming formulation of the problem, including an efficient approximation scheme to solve it. We illustrate our approach in a small self-constructed example, and apply our approximation scheme to a real-world section of the Andina mine, in Chile.  
  Address [Canessa, Gianpiero] Univ Adolfo Ibanez, PhD Program Ind Engn & Operat Res, Diagonal Las Torres 2640, Santiago 7941169, Chile, Email: canessa@kth.se  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1389-4420 ISBN Medium  
  Area Expedition (up) Conference  
  Notes WOS:000557140000001 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1219  
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