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

Networks ◽  
2017 ◽  
Vol 69 (2) ◽  
pp. 189-204 ◽  
Author(s):  
Maciej Rysz ◽  
Pavlo A. Krokhmal ◽  
Eduardo L. Pasiliao
Keyword(s):  
Stochastic Optimization ◽  
Stochastic Networks ◽  
Optimization Approach ◽  
Two Stage

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

2017 ◽  
Vol 56 (7) ◽  
pp. 1823-1833 ◽  
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

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

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

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

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

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

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.

A Novel Approach for Two-Stage UAV Path Planning in Pipeline Network Inspection

2020 ◽  
Author(s):  
Rui Qiu ◽  
Yongtu Liang
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Flight Path ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Two Stage ◽  
Calculation Time ◽  
Pipeline Network ◽  
Second Stage ◽  
Computational Performance

Abstract Currently, unmanned aerial vehicle (UAV) provides the possibility of comprehensive coverage and multi-dimensional visualization of pipeline monitoring. Encouraged by industry policy, research on UAV path planning in pipeline network inspection has emerged. The difficulties of this issue lie in strict operational requirements, variable flight missions, as well as unified optimization for UAV deployment and real-time path planning. Meanwhile, the intricate structure and large scale of the pipeline network further complicate this issue. At present, there is still room to improve the practicality and applicability of the mathematical model and solution strategy. Aiming at this problem, this paper proposes a novel two-stage optimization approach for UAV path planning in pipeline network inspection. The first stage is conventional pre-flight planning, where the requirement for optimality is higher than calculation time. Therefore, a mixed integer linear programming (MILP) model is established and solved by the commercial solver to obtain the optimal UAV number, take-off location and detailed flight path. The second stage is re-planning during the flight, taking into account frequent pipeline accidents (e.g. leaks and cracks). In this stage, the flight path must be timely rescheduled to identify specific hazardous locations. Thus, the requirement for calculation time is higher than optimality and the genetic algorithm is used for solution to satisfy the timeliness of decision-making. Finally, the proposed method is applied to the UAV inspection of a branched oil and gas transmission pipeline network with 36 nodes and the results are analyzed in detail in terms of computational performance. In the first stage, compared to manpower inspection, the total cost and time of UAV inspection is decreased by 54% and 56% respectively. In the second stage, it takes less than 1 minute to obtain a suboptimal solution, verifying the applicability and superiority of the method.

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