Coordinated and Priority‐based Surgical Care: An Integrated Distributionally Robust Stochastic Optimization Approach

Abstract A stochastic model predictive control (SMPC) algorithm is developed to solve the problem of three-dimensional spacecraft rendezvous and docking with unbounded disturbance. In particular, we only assume that the mean and variance information of the disturbance is available. In other words, the probability density function of the disturbance distribution is not fully known. Obstacle avoidance is considered during the rendezvous phase. Line-of-sight cone, attitude control bandwidth, and thrust direction constraints are considered during the docking phase. A distributionally robust optimization based algorithm is then proposed by reformulating the SMPC problem into a convex optimization problem. Numerical examples show that the proposed method improves the existing model predictive control based strategy and the robust model predictive control based strategy in the presence of disturbance.

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Multiobjective Stochastic Optimization Approach Applied to a Hybrid Process Production–Separation in the Production of Biobutanol

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.6b04230 ◽

2017 ◽

Vol 56 (7) ◽

pp. 1823-1833 ◽

Cited By ~ 9

Author(s):

Juan José Quiroz-Ramírez ◽

Eduardo Sánchez-Ramírez ◽

Salvador Hernández ◽

Jorge Humberto Ramírez-Prado ◽

Juan Gabriel Segovia-Hernández

Keyword(s):

Stochastic Optimization ◽

Hybrid Process ◽

Optimization Approach

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Detecting resilient structures in stochastic networks: A two-stage stochastic optimization approach

Networks ◽

10.1002/net.21727 ◽

2017 ◽

Vol 69 (2) ◽

pp. 189-204 ◽

Cited By ~ 3

Author(s):

Maciej Rysz ◽

Pavlo A. Krokhmal ◽

Eduardo L. Pasiliao

Keyword(s):

Stochastic Optimization ◽

Stochastic Networks ◽

Optimization Approach ◽

Two Stage

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Optimal operation of alumina proportioning and mixing process based on stochastic optimization approach

Control Engineering Practice ◽

10.1016/j.conengprac.2021.104855 ◽

2021 ◽

Vol 113 ◽

pp. 104855

Author(s):

Yanyan Yin ◽

Lingshuang Kong ◽

Chunhua Yang ◽

Weihua Gui ◽

Fei Liu ◽

...

Keyword(s):

Stochastic Optimization ◽

Optimal Operation ◽

Optimization Approach ◽

Mixing Process

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Multiobjective Risk-Aware Path Planning in Uncertain Transient Currents: An Ensemble-Based Stochastic Optimization Approach

IEEE Journal of Oceanic Engineering ◽

10.1109/joe.2021.3063196 ◽

2021 ◽

pp. 1-17

Author(s):

Sultan Albarakati ◽

Ricardo M. Lima ◽

Thomas Theusl ◽

Ibrahim Hoteit ◽

Omar Knio

Keyword(s):

Path Planning ◽

Stochastic Optimization ◽

Optimization Approach ◽

Transient Currents

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Design of hydrogen supply chains under demand uncertainty – a case study of passenger transport in Germany

Physical Sciences Reviews ◽

10.1515/psr-2020-0052 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Anton Ochoa Bique ◽

Leonardo K. K. Maia ◽

Ignacio E. Grossmann ◽

Edwin Zondervan

Keyword(s):

Stochastic Optimization ◽

Demand Uncertainty ◽

Programming Model ◽

Cost Minimization ◽

Water Electrolysis ◽

Scenario Tree ◽

Mixed Integer ◽

Optimization Approach ◽

Strong Impact ◽

Hydrogen Supply

Abstract A strategy for the design of a hydrogen supply chain (HSC) network in Germany incorporating the uncertainty in the hydrogen demand is proposed. Based on univariate sensitivity analysis, uncertainty in hydrogen demand has a very strong impact on the overall system costs. Therefore we consider a scenario tree for a stochastic mixed integer linear programming model that incorporates the uncertainty in the hydrogen demand. The model consists of two configurations, which are analyzed and compared to each other according to production types: water electrolysis versus steam methane reforming. Each configuration has a cost minimization target. The concept of value of stochastic solution (VSS) is used to evaluate the stochastic optimization results and compare them to their deterministic counterpart. The VSS of each configuration shows significant benefits of a stochastic optimization approach for the model presented in this study, corresponding up to 26% of infrastructure investments savings.

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