scholarly journals Optimization of -Cycle Placement for Differentiated Levels of Protection

2013 ◽  
Vol 2013 ◽  
pp. 1-5
Author(s):  
Hong Hui Li ◽  
Xue Liang Fu
Keyword(s):  
Large Scale ◽  
Design Method ◽  
Optimization Techniques ◽  
Spare Capacity ◽  
Node Failure ◽  
Large Scale Network ◽  
Node Protection ◽  
Service Basis ◽  
Scale Optimization ◽  
Link Protection

This paper develops a new scalable and efficient model for the design ofp-cycles with the differentiated levels of node protection. The proposed model allows the indicated level of node survivability ranging from 0% to 100%, which could facilitate a carrier offer node-failure survivability (and hence availability) on a differentiated service basis. To designp-cycles, an integer linear program (ILP) is usually formulated with the prerequisite of a prior enumeration of all possiblep-cycle candidates. A huge number of candidates may exist in a large-scale network. Thus, the resulting ILP becomes intractable. We propose a new design and solution method based on large-scale optimization techniques, known as column generation (CG). With CG, our design method generatesp-cycle candidates dynamically when needed. Extensive experiments have been conducted for evaluation. The numerical results show that, with the spare capacity used only for link protection, up to 50% node-failure survivability can be achieved for free. Full node protection can be achieved at a marginal cost in comparison with those for link protection only.

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 AN EFFICIENT Hybrid Swarm Intelligence Technique for Solving Integer Programming

2013 ◽  
Vol 10 (4) ◽  
pp. 1531-1538
Author(s):  
Mahmoud M. Ismail ◽  
Ibrahim M. El-henawy
Keyword(s):  
Integer Programming ◽  
Large Scale ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Research Area ◽  
Optimization Techniques ◽  
Computational Time ◽  
Hybrid Swarm ◽  
Integer Programming Problems ◽  
Scale Optimization

In this paper, a hybridization of two different swarm intelligent approaches, stochastic diffusion search, and particle swarm optimization techniques is presented  for solving integer programming problems. The hybrid implementation allows us to avoid certain drawbacks and weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. Our hybrid implementation allows the IP algorithm to reach the optimal solution in a considerably shorter time than is needed to solve the model using the entire dataset directly within the model. Our hybrid approach outperforms the results obtained by each technique separately. It is able to find the optimal solution in a shorter time than each technique on its own, and the results are highly competitive with the state-of-the-art in large-scale optimization. Furthermore, according to our results, combining the PSO with SDS approach for solving IP problems appears to be an interesting research area in combinatorial optimization. 

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2022 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid ◽  
Abeer Alsadoon ◽  
Nebojsa Bacanin ◽  
Polla Fattah ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization ◽  
Metaheuristic Techniques ◽  
Scale Optimization ◽  
Benchmark Test Functions

<p></p><p></p><p>The dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><p></p><p></p>

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network
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