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Author Acuna, J.A.; Cantarino, D.; Martinez, R.; Zayas-Castro, J.L.
Title A two-stage stochastic game model for elective surgical capacity planning and investment Type
Year 2024 Publication Socio-Economic Planning Sciences Abbreviated Journal Socio-Econ. Plan. Sci.
Volume 91 Issue Pages 101786
Keywords Waiting lists; Elective surgeries; Game theory; Stochastic programming; Fairness; Health care access
Abstract Waiting for elective procedures has become a major health concern in both rich and poor countries. The inadequate balance between the demand for and the supply of health services negatively affects the quality of life, mortality, and government appraisal. This study presents the first mathematical framework shedding light on how much, when, and where to invest in health capacity to end waiting lists for elective surgeries. We model the healthcare system as a two-stage stochastic capacity expansion problem where government investment decisions are represented as a non-symmetric Nash bargaining solution. In particular, the model assesses the capacity requirements, optimal allocation, and corresponding financial investment per hospital, region, specialty, and year. We use the proposed approach to target Chile's elective surgical waiting lists (2021- 2031), considering patients' priorities, 10 regional health services, 24 hospitals, and 10 surgical specialties. We generate uncertain future demand scenarios using historical data (2012-2021) and 100 autoregressive integrated moving average prediction models. The results indicate that USD 3,331.677 million is necessary to end the waiting lists by 2031 and that the Nash approach provides a fair resource distribution with a 6% efficiency loss. Additionally, a smaller budget (USD 2,000 million) was identified as necessary to end the waiting lists in a longer planning horizon. Further analysis revealed the impact of investment in patient transfer and a decline in investment yield.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0038-0121 ISBN Medium
Area Expedition Conference
Notes WOS:001161461600001 Approved
Call Number UAI @ alexi.delcanto @ Serial 1945
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Author Bergen, M.; Munoz, F.D.
Title Quantifying the effects of uncertain climate and environmental policies on investments and carbon emissions: A case study of Chile Type
Year 2018 Publication Energy Economics Abbreviated Journal Energy Econ.
Volume 75 Issue Pages 261-273
Keywords Uncertainty; Climate policies; Transmission and generation planning; Carbon emissions; Stochastic programming; Equilibrium
Abstract In this article we quantify the effect of uncertainty of climate and environmental policies on transmission and generation investments, as well as on CO2 emissions in Chile. We use a two-stage stochastic planning model with recourse or corrective investment options to find optimal portfolios of infrastructure both under perfect information and uncertainty. Under a series of assumptions, this model is equivalent to the equilibrium of a much more complicated bi-level market model, where a transmission planner chooses investments first and generation firms invest afterwards. We find that optimal investment strategies present important differences depending on the policy scenario. By changing our assumption of how agents will react to this uncertainty we compute bounds on the cost that this uncertainty imposes on the system, which we estimate ranges between 3.2% and 5.7% of the minimum expected system cost of $57.6B depending on whether agents will consider or not uncertainty when choosing investments. We also find that, if agents choose investments using a stochastic planning model, uncertain climate policies can result in nearly 18% more CO2 emissions than the equilibrium levels observed under perfect information. Our results highlight the importance of credible and stable long-term regulations for investors in the electric power industry if the goal is to achieve climate and environmental targets in the most cost-effective manner and to minimize the risk of asset stranding. (C) 2018 Elsevier B.V. All rights reserved.
Address [Bergen, Matias] Politecn Torino, Turin, Italy, Email: mebergen@uc.cl;
Corporate Author Thesis
Publisher Elsevier Science Bv Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0140-9883 ISBN Medium
Area Expedition Conference
Notes WOS:000449891600019 Approved
Call Number UAI @ eduardo.moreno @ Serial 930
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Author Lagos, G.; Espinoza, D.; Moreno, E.; Vielma, J.P.
Title Restricted risk measures and robust optimization Type
Year 2015 Publication European Journal Of Operational Research Abbreviated Journal Eur. J. Oper. Res.
Volume 241 Issue 3 Pages 771-782
Keywords Risk management; Stochastic programming; Uncertainty modeling
Abstract In this paper we consider characterizations of the robust uncertainty sets associated with coherent and distortion risk measures. In this context we show that if we are willing to enforce the coherent or distortion axioms only on random variables that are affine or linear functions of the vector of random parameters, we may consider some new variants of the uncertainty sets determined by the classical characterizations. We also show that in the finite probability case these variants are simple transformations of the classical sets. Finally we present results of computational experiments that suggest that the risk measures associated with these new uncertainty sets can help mitigate estimation errors of the Conditional Value-at-Risk. (C) 2014 Elsevier B.V. All rights reserved.
Address [Lagos, Guido] Georgia Inst Technol, H Milton Stewart Sch Ind & Syst Engn, Atlanta, GA 30332 USA, Email: glagos@gatech.edu;
Corporate Author Thesis
Publisher Elsevier Science Bv Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0377-2217 ISBN Medium
Area Expedition Conference
Notes WOS:000347605100018 Approved
Call Number UAI @ eduardo.moreno @ Serial 438
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Author Munoz, F.D.; Hobbs, B.F.; Watson, J.P.
Title New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints Type
Year 2016 Publication European Journal Of Operational Research Abbreviated Journal Eur. J. Oper. Res.
Volume 248 Issue 3 Pages 888-898
Keywords OR in energy; Stochastic programming; Benders decomposition
Abstract We propose a novel two-phase bounding and decomposition approach to compute optimal and near-optimal solutions to large-scale mixed-integer investment planning problems that have to consider a large number of operating subproblems, each of which is a convex optimization. Our motivating application is the planning of power transmission and generation in which policy constraints are designed to incentivize high amounts of intermittent generation in electric power systems. The bounding phase exploits Jensen's inequality to define a lower bound, which we extend to stochastic programs that use expected-value constraints to enforce policy objectives. The decomposition phase, in which the bounds are tightened, improves upon the standard Benders' algorithm by accelerating the convergence of the bounds. The lower bound is tightened by using a Jensen's inequality-based approach to introduce an auxiliary lower bound into the Benders master problem. Upper bounds for both phases are computed using a sub-sampling approach executed on a parallel computer system. Numerical results show that only the bounding phase is necessary if loose optimality gaps are acceptable. However, the decomposition phase is required to attain optimality gaps. Use of both phases performs better, in terms of convergence speed, than attempting to solve the problem using just the bounding phase or regular Benders decomposition separately. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.
Address [Munoz, F. D.] Univ Adolfo Ibanez, Fac Sci & Engn, Santiago, Chile, Email: fdmunoz@uai.cl;
Corporate Author Thesis
Publisher Elsevier Science Bv Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0377-2217 ISBN Medium
Area Expedition Conference
Notes WOS:000364603700013 Approved
Call Number UAI @ eduardo.moreno @ Serial 535
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Author Munoz, F.D.; van der Weijde, A.H.; Hobbs, B.F.; Watson, J.P.
Title Does risk aversion affect transmission and generation planning? A Western North America case study Type
Year 2017 Publication Energy Economics Abbreviated Journal Energy Econ.
Volume 64 Issue Pages 213-225
Keywords Risk aversion; Stochastic programming; Transmission and generation planning; Investment
Abstract We investigate the effects of risk aversion on optimal transmission and generation expansion planning in a competitive and complete market. To do so, we formulate a stochastic model that minimizes a weighted average of expected transmission and generation costs and their conditional value at risk (CVaR). We show that the solution of this optimization problem is equivalent to the solution of a perfectly competitive risk averse Stackelberg equilibrium, in which a risk-averse transmission planner maximizes welfare after which risk-averse generators maximize profits. This model is then applied to a 240-bus representation of the Western Electricity Coordinating Council, in which we examine the impact of risk aversion on levels and spatial patterns of generation and transmission investment. Although the impact of risk aversion remains small at an aggregate level, state-level impacts on generation and transmission investment can be significant, which emphasizes the importance of explicit consideration of risk aversion in planning models. (C) 2017 Elsevier B.V. All rights reserved.
Address [Munoz, Francisco D.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Diagonal Las Torres 2640, Santiago, Chile, Email: fdmunoz@uai.cl
Corporate Author Thesis
Publisher Elsevier Science Bv Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0140-9883 ISBN Medium
Area Expedition Conference
Notes WOS:000404704900020 Approved
Call Number UAI @ eduardo.moreno @ Serial 746
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Author Ramirez-Pico, C.; Ljubic, I.; Moreno, E.
Title Benders Adaptive-Cuts Method for Two-Stage Stochastic Programs Type
Year 2023 Publication Transportation Science Abbreviated Journal Transp. Sci.
Volume Early Access Issue Pages
Keywords two-stage stochastic programming; Benders decomposition; network flow; conditional value-at-risk; facility location
Abstract Benders decomposition is one of the most applied methods to solve two-stage stochastic problems (TSSP) with a large number of scenarios. The main idea behind the Benders decomposition is to solve a large problem by replacing the values of the second stage subproblems with individual variables and progressively forcing those variables to reach the optimal value of the subproblems, dynamically inserting additional valid constraints, known as Benders cuts. Most traditional implementations add a cut for each scenario (multicut) or a single cut that includes all scenarios. In this paper, we present a novel Benders adaptive-cuts method, where the Benders cuts are aggregated according to a partition of the scenarios, which is dynamically refined using the linear program-dual information of the subproblems. This scenario aggregation/disaggregation is based on the Generalized Adaptive Partitioning Method (GAPM), which has been successfully applied to TSSPs. We formalize this hybridization of Benders decomposition and the GAPM by providing sufficient conditions under which an optimal solution of the deterministic equivalent can be obtained in a finite number of iterations. Our new method can be interpreted as a compromise between the Benders single-cuts and multicuts methods, drawing on the advantages of both sides, by rendering the initial iterations faster (as for the single-cuts Benders) and ensuring the overall faster convergence (as for the multicuts Benders). Computational experiments on three TSSPs [the Stochastic Electricity Planning, Stochastic Multi Commodity Flow, and conditional value-at-risk (CVaR) Facility Location] validate these statements, showing that the new method outperforms the other implementations of Benders methods, as well as other standard methods for solving TSSPs, in particular when the number of scenarios is very large. Moreover, our study demonstrates that the method is not only effective for the risk-neutral decision makers, but also that it can be used in combination with the risk-averse CVaR objective.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0041-1655 ISBN Medium
Area Expedition Conference
Notes WOS:001011928100001 Approved
Call Number UAI @ alexi.delcanto @ Serial 1814
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Author Reus, L.; Belbeze, M.; Feddersen, H.; Rubio, E.
Title Extraction Planning Under Capacity Uncertainty at the Chuquicamata Underground Mine Type
Year 2018 Publication Interfaces Abbreviated Journal Interfaces
Volume 48 Issue 6 Pages 543-555
Keywords underground mine extraction scheduling; operational uncertainty management; stochastic programming applications; long-term mine planning
Abstract We propose an extraction schedule for the Chuquicamata underground copper mine in Chile. The schedule maximizes profits while adhering to all operational and geomechanical requirements involved in proper removal of the material. We include extraction capacity uncertainties due to failure in equipment, specifically to the overland conveyor, which we find to be the most critical component in the extraction process. First we present the extraction plan based on a deterministic model, which does not assume uncertainty in the extraction capacity and represents the solution that the mine can implement without using the results of this study. Then we extend this model to a stochastic setting by generating different scenarios for capacity values in subsequent periods. We construct a multistage model that handles economic downside risk arising from this uncertainty by penalizing plans that deviate from an ex ante profit target in one or more scenarios. Simulation results show that a stochastic-based solution can achieve the same expected profits as the deterministic-based solution. However, the earnings of the stochastic-based solution average 5% more for scenarios in which earnings are below the 10th percentile. If we choose a target 2% below the expected profit obtained by the deterministic-based solution, this average increases from 5% to 9%.
Address [Reus, Lorenzo; Belbeze, Mathias; Feddersen, Hans] Adolfo Ibanez Univ, Dept Engn & Sci, Santiago 7910000, Chile, Email: lorenzo.reus@uai.cl;
Corporate Author Thesis
Publisher Informs Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0092-2102 ISBN Medium
Area Expedition Conference
Notes WOS:000454513500005 Approved
Call Number UAI @ eduardo.moreno @ Serial 967
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