Large-scale optimization techniques for nonnegative image restorations

2002 ◽  
Author(s):  
Marielba Rojas ◽  
Trond Steihaug
Keyword(s):  
Large Scale ◽  
Optimization Techniques ◽  
Large Scale Optimization ◽  
Scale Optimization

Applying Large-Scale Optimization to Evaluate Pavement Maintenance Alternatives for Low-Volume Roads using Genetic Algorithms

2018 ◽  
Vol 2672 (52) ◽  
pp. 205-215 ◽  
Author(s):  
Marwan Hafez ◽  
Khaled Ksaibati ◽  
Rebecca A. Atadero
Keyword(s):  
Genetic Algorithms ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Pavement Performance ◽  
Large Scale Optimization ◽  
Maintenance Policy ◽  
Low Volume Roads ◽  
Low Volume ◽  
Scale Optimization

Over the last decade, significant progress has been made to customize the maintenance policies of low-volume roads (LVRs) to local needs and available resources. Low-cost treatments and surface repairs are extensively employed to reduce annual maintenance costs. Colorado Department of Transportation (CDOT) uses chip seals and thin overlays as the available treatment options applied to LVRs. However, the effectiveness of these treatments differs depending on the existing condition of pavements. Some surface treatments and light rehabilitations provide only short-term effectiveness. Multi-year optimization techniques can support decision makers with a set of optimal maintenance activities to achieve specific pavement performance targets. This study applies large-scale optimization to compare the current CDOT maintenance policy with an alternative strategy recommended for low-volume paved roads in Colorado. Genetic algorithms were applied in the optimization models because they are capable of resolving the computational complexity of optimization problems in a timely fashion. The optimized maintenance alternatives were comprehensively investigated for a LVR network in Colorado over a specific planning horizon. The specific optimization constraints and limitations prevailing in LVRs are addressed and introduced in the problem formulation of the optimization process. The results of both performance and cost analysis emphasize the effectiveness of the proposed maintenance strategy compared with the existing one. The alternative policy provides much more benefit-cost saving while preserving the overall pavement performance of the network. This approach is expected to be efficient to quantify the mid- and long-term financial impact of different treatment policies applied to LVRs within modest resources.

Locomotive Assignment Under Consist Busting and Maintenance Constraints

2014 ◽  
Author(s):  
Brigitte Jaumard ◽  
Huaining Tian ◽  
Peter Finnie
Keyword(s):  
Column Generation ◽  
Large Scale ◽  
Optimization Techniques ◽  
Large Scale Optimization ◽  
Railway Network ◽  
Time Period ◽  
Scale Optimization ◽  
Solution Accuracy ◽  
Locomotive Assignment ◽  
The Impact

We propose a new optimization model with column generation (CG) decomposition for the locomotive assignment problem (LAP). Although several algorithms have been developed for LAP, including exact mathematics models, approximate dynamic programming and heuristics, previously published optimization algorithms all suffer from scalability or solution accuracy issues. In addition, each of the optimization models lacks part of the constraints that are necessary in real-world train/locomotive assignments, e.g., maintenance shop constraints or consist-busting avoidance. We propose a train string based LAP model, which includes those constraints and which can efficiently be solved using large scale optimization techniques, namely column generation techniques. Numerical results are conducted on the railway network infrastructure of Canada Pacific Railway, with up to 1,394 trains and 4 types of locomotives over a two-week time period. We investigate the impact of the size of the locomotive fleet on the consist busting.

scholarly journals Large-scale optimization of households with photovoltaic-battery system and demand response

2020 ◽  
Vol 53 (2) ◽  
pp. 12572-12577
Author(s):  
Fernando Lezama ◽  
Ricardo Faia ◽  
Omid Abrishambaf ◽  
Pedro Faria ◽  
Zita Vale
Keyword(s):  
Demand Response ◽  
Large Scale ◽  
Large Scale Optimization ◽  
Battery System ◽  
Scale Optimization
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