Large-scale Zone-based Evacuation Planning: Generating Convergent and Non-Preemptive Evacuation Plans via Column Generation

2022 ◽  
Author(s):  
Jorge Huertas ◽  
Pascal Van Hentenryck
Keyword(s):  
Column Generation ◽  
Large Scale ◽  
Evacuation Planning ◽  
Evacuation Plans

scholarly journals Maximum Variance Hashing via Column Generation

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Lei Luo ◽  
Chao Zhang ◽  
Yongrui Qin ◽  
Chunyuan Zhang
Keyword(s):  
Column Generation ◽  
Large Scale ◽  
Web Search ◽  
Nearest Neighbor ◽  
Computational Cost ◽  
Multimedia Retrieval ◽  
Training Data ◽  
Nonlinear Dimensionality Reduction ◽  
Maximum Variance ◽  
Data Volume

With the explosive growth of the data volume in modern applications such as web search and multimedia retrieval, hashing is becoming increasingly important for efficient nearest neighbor (similar item) search. Recently, a number of data-dependent methods have been developed, reflecting the great potential of learning for hashing. Inspired by the classic nonlinear dimensionality reduction algorithm—maximum variance unfolding, we propose a novel unsupervised hashing method, named maximum variance hashing, in this work. The idea is to maximize the total variance of the hash codes while preserving the local structure of the training data. To solve the derived optimization problem, we propose a column generation algorithm, which directly learns the binary-valued hash functions. We then extend it using anchor graphs to reduce the computational cost. Experiments on large-scale image datasets demonstrate that the proposed method outperforms state-of-the-art hashing methods in many cases.

A column-generation approach for joint mobilization and evacuation planning

Constraints ◽  
2015 ◽  
Vol 20 (3) ◽  
pp. 285-303 ◽  
Author(s):  
Victor Pillac ◽  
Manuel Cebrian ◽  
Pascal Van Hentenryck
Keyword(s):  
Column Generation ◽  
Evacuation Planning ◽  
Joint Mobilization

scholarly journals Flow Improvement in Evacuation Planning with Budget Constrained Switching Costs

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Ram Chandra Dhungana ◽  
Tanka Nath Dhamala
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Maximum Flow ◽  
Evacuation Planning ◽  
Solution Technique ◽  
Planning Problem ◽  
Constrained Problems ◽  
Full Capacity ◽  
Evacuation Plan ◽  
Flow Improvement

Many large-scale natural and human-created disasters have drawn the attention of researchers towards the solutions of evacuation planning problems and their applications. The main focus of these solution strategies is to protect the life, property, and their surroundings during the disasters. With limited resources, it is not an easy task to develop a universally accepted model to handle such issues. Among them, the budget-constrained network flow improvement approach plays significant role to evacuate the maximum number of people within the given time horizon. In this paper, we consider an evacuation planning problem that aims to shift a maximum number of evacuees from a danger area to a safe zone in limited time under the budget constraints for network modification. Different flow improvement strategies with respect to fixed switching cost will be investigated, namely, integral, rational, and either to increase the full capacity of an arc or not at all. A solution technique on static network is extended to the dynamic one. Moreover, we introduce the static and dynamic maximum flow problems with lane reversal strategy and also propose efficient algorithms for their solutions. Here, the contraflow approach reverses the direction of arcs with respect to the lane reversal costs to increase the flow value. As an implementation of an evacuation plan may demand a large cost, the solutions proposed here with budget constrained problems play important role in practice.

A Path-Generation Matheuristic for Large Scale Evacuation Planning

2014 ◽  
pp. 71-84 ◽  
Author(s):  
Victor Pillac ◽  
Pascal Van Hentenryck ◽  
Caroline Even
Keyword(s):  
Large Scale ◽  
Evacuation Planning ◽  
Path Generation

scholarly journals Solving the Multiscenario Max-Min Knapsack Problem Exactly with Column Generation and Branch-and-Bound

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Telmo Pinto ◽  
Cláudio Alves ◽  
Raïd Mansi ◽  
José Valério de Carvalho
Keyword(s):  
Column Generation ◽  
Branch And Bound ◽  
Knapsack Problem ◽  
Large Scale ◽  
Programming Model ◽  
State Of The Art ◽  
Objective Evaluation ◽  
Benchmark Instances ◽  
Total Profit ◽  
Integrality Property

Despite other variants of the standard knapsack problem, very few solution approaches have been devised for the multiscenario max-min knapsack problem. The problem consists in finding the subset of items whose total profit is maximized under the worst possible scenario. In this paper, we describe an exact solution method based on column generation and branch-and-bound for this problem. Our approach relies on a reformulation of the standard compact integer programming model based on the Dantzig-Wolfe decomposition principle. The resulting model is potentially stronger than the original one since the corresponding pricing subproblem does not have the integrality property. The details of the reformulation are presented and analysed together with those concerning the column generation and branch-and-bound procedures. To evaluate the performance of our algorithm, we conducted extensive computational experiments on large scale benchmark instances, and we compared our results with other state-of-the-art approaches under similar circumstances. We focused in particular on different relevant aspects that allow an objective evaluation of the efficacy of our approach. From different standpoints, the branch-and-price algorithm proved to outperform the other state-of-the-art methods described so far in the literature.

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