Robust optimization approach to capacitated single and multiple allocation hub location problems

2014 ◽  
Vol 35 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Fereidoon Habibzadeh Boukani ◽  
Babak Farhang Moghaddam ◽  
Mir Saman Pishvaee
Keyword(s):  
Robust Optimization ◽  
Location Problems ◽  
Optimization Approach ◽  
Hub Location ◽  
Multiple Allocation

scholarly journals A Robust Optimization Approach to the Multiple Allocation p-Center Facility Location Problem

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 588 ◽  
Author(s):  
Bo Du ◽  
Hong Zhou
Keyword(s):  
Robust Optimization ◽  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Optimization Approach ◽  
Robust Solutions ◽  
Demand Node ◽  
Uncertainty Sets ◽  
Ellipsoidal Uncertainty ◽  
Multiple Allocation

In this study, we apply a robust optimization approach to a p-center facility location problem under uncertainty. Based on a symmetric interval and a multiple allocation strategy, we use three types of uncertainty sets to formulate the robust problem: box uncertainty, ellipsoidal uncertainty, and cardinality-constrained uncertainty. The equivalent robust counterpart models can be solved to optimality using Gurobi. Comprehensive numerical experiments have been conducted by comparing the performance of the different robust models, which illustrate the pattern of robust solutions, and allocating a demand node to multiple facilities can reduce the price of robustness, and reveal that alternative models of uncertainty can provide robust solutions with different conservativeness.

scholarly journals Efficient simulated annealing based solution approaches to the competitive single and multiple allocation hub location problems

2018 ◽  
Vol 90 ◽  
pp. 173-192 ◽  
Author(s):  
Nader Ghaffarinasab ◽  
Alireza Motallebzadeh ◽  
Younis Jabarzadeh ◽  
Bahar Y. Kara
Keyword(s):  
Simulated Annealing ◽  
Location Problems ◽  
Hub Location ◽  
Multiple Allocation

Preprocessing and cutting for multiple allocation hub location problems

2004 ◽  
Vol 155 (3) ◽  
pp. 638-653 ◽  
Author(s):  
Natashia Boland ◽  
Mohan Krishnamoorthy ◽  
Andreas T. Ernst ◽  
Jamie Ebery
Keyword(s):  
Location Problems ◽  
Hub Location ◽  
Multiple Allocation

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

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