Monotonic bounds in multistage mixed-integer stochastic programming

2016 ◽  
Vol 13 (3) ◽  
pp. 423-457 ◽  
Author(s):  
Francesca Maggioni ◽  
Elisabetta Allevi ◽  
Marida Bertocchi
Keyword(s):  
Stochastic Programming ◽  
Mixed Integer ◽  
Integer Stochastic Programming

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1392 ◽  
Author(s):  
Iram Parvez ◽  
JianJian Shen ◽  
Mehran Khan ◽  
Chuntian Cheng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Stochastic Programming ◽  
Short Interval ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Fuzzy Logics ◽  
Spinning Reserve ◽  
Lagrange Relaxation ◽  
Generation Scheduling

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

scholarly journals Bounds on risk-averse mixed-integer multi-stage stochastic programming problems with mean-CVaR

2018 ◽  
Vol 266 (2) ◽  
pp. 595-608 ◽  
Author(s):  
Ali İrfan Mahmutoğulları ◽  
Özlem Çavuş ◽  
M. Selim Aktürk
Keyword(s):  
Stochastic Programming ◽  
Mixed Integer ◽  
Risk Averse ◽  
Multi Stage

scholarly journals A Lexicographic Approach to Postdisaster Relief Logistics Planning Considering Fill Rates and Costs under Uncertainty

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Yajie Liu ◽  
Bo Guo
Keyword(s):  
Stochastic Programming ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Solution Algorithms ◽  
Evacuation Plans ◽  
Proposed Model ◽  
Transportation Assets ◽  
Stochastic Programming Model

Predicting the occurrences of earthquakes is difficult, but because they often bring huge catastrophes, it is necessary to launch relief logistics campaigns soon after they occur. This paper proposes a stochastic optimization model for post-disaster relief logistics to guide the strategic planning with respect to the locations of temporary facilities, the mobilization levels of relief supplies, and the deployment of transportation assets with uncertainty on demands. In addition, delivery plans for relief supplies and evacuation plans for critical population have been developed for each scenario. Two objectives are featured in the proposed model: maximizing the expected minimal fill rate of affected areas, where the mismatching distribution among correlated relief demands is penalized, and minimizing the expected total cost. An approximate lexicographic approach is here used to transform the bi-objective stochastic programming model into a sequence of single objective stochastic programming models, and scenario-decomposition-based heuristic algorithms are furthermore developed to solve these transformed models. The feasibility of the proposed bi-objective stochastic model has been demonstrated empirically, and the effectiveness of the developed solution algorithms has also been evaluated and compared to that of commercial mixed-integer optimization software.

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