ILP-Based Reduced Variable Neighborhood Search for Large-Scale Minimum Common String Partition

2018 ◽  
Vol 66 ◽  
pp. 15-22 ◽  
Author(s):  
Christian Blum
Keyword(s):  
Large Scale ◽  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Minimum Common String Partition

scholarly journals Self-Adjusting Variable Neighborhood Search Algorithm for Near-Optimal k-Means Clustering

Computation ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 90
Author(s):  
Lev Kazakovtsev ◽  
Ivan Rozhnov ◽  
Aleksey Popov ◽  
Elena Tovbis
Keyword(s):  
Large Scale ◽  
Variable Neighborhood Search ◽  
Graphics Processing Unit ◽  
Search Algorithm ◽  
Fixed Time ◽  
Exact Algorithms ◽  
Neighborhood Search ◽  
Data Sets ◽  
Processing Unit ◽  
Online Computation

The k-means problem is one of the most popular models in cluster analysis that minimizes the sum of the squared distances from clustered objects to the sought cluster centers (centroids). The simplicity of its algorithmic implementation encourages researchers to apply it in a variety of engineering and scientific branches. Nevertheless, the problem is proven to be NP-hard which makes exact algorithms inapplicable for large scale problems, and the simplest and most popular algorithms result in very poor values of the squared distances sum. If a problem must be solved within a limited time with the maximum accuracy, which would be difficult to improve using known methods without increasing computational costs, the variable neighborhood search (VNS) algorithms, which search in randomized neighborhoods formed by the application of greedy agglomerative procedures, are competitive. In this article, we investigate the influence of the most important parameter of such neighborhoods on the computational efficiency and propose a new VNS-based algorithm (solver), implemented on the graphics processing unit (GPU), which adjusts this parameter. Benchmarking on data sets composed of up to millions of objects demonstrates the advantage of the new algorithm in comparison with known local search algorithms, within a fixed time, allowing for online computation.

scholarly journals Solving balanced multi-weighted attribute set partitioning problem with variable neighborhood search

Filomat ◽  
2019 ◽  
Vol 33 (9) ◽  
pp. 2875-2891
Author(s):  
Dusan Dzamic ◽  
Bojana Cendic ◽  
Miroslav Maric ◽  
Aleksandar Djenic
Keyword(s):  
Local Search ◽  
Large Scale ◽  
Variable Neighborhood Search ◽  
Heuristic Method ◽  
Set Partitioning ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Computationally Efficient ◽  
Local Search Procedure ◽  
Problem Instances

This paper considers the Balanced Multi-Weighted Attribute Set Partitioning (BMWASP) problem which requires finding a partition of a given set of objects with multiple weighted attributes into a certain number of groups so that each attribute is evenly distributed amongst the groups. Our approach is to define an appropriate criterion allowing to compare the degree of deviation from the ?perfect balance? for different partitions and then produce the partition that minimizes this criterion. We have proposed a mathematical model for the BMWASP and its mixed-integer linear reformulation. We evaluated its efficiency through a set of computational experiments. To solve instances of larger problem dimensions, we have developed a heuristic method based on a Variable Neighborhood Search (VNS). A local search procedure with efficient fast swap-based local search is implemented in the proposed VNS-based approach. Presented computational results show that the proposed VNS is computationally efficient and quickly reaches all optimal solutions for smaller dimension instances obtained by exact solver and provide high-quality solutions on large-scale problem instances in short CPU times.

scholarly journals Enhanced Variable Neighborhood Search-Based Recovery Supplier Selection for Post-Disruption Supply Networks

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 670
Author(s):  
Yuting Chen ◽  
Ping Lou ◽  
Wen Jiang
Keyword(s):  
Supplier Selection ◽  
Large Scale ◽  
Network Performance ◽  
Variable Neighborhood Search ◽  
Performance Metrics ◽  
Recovery Process ◽  
Neighborhood Search ◽  
Supply Network ◽  
Supply Networks ◽  
Selection Of

With the increasing reliance on global sourcing and the growth in the likelihood of disruptive incidents, today’s supply networks are more prone to unexpected natural and man-made disruptive events. In order to alleviate the losses caused by these disruptive events, when a large-scale event disrupts multiple suppliers simultaneously, a single or several critical suppliers should be selected from the disrupted ones to assist them to recover their production as soon as possible. The selection of these recovery suppliers is of great importance in the recovery process of the entire supply network. Thus, this paper proposes a recovery supplier selection method from the view of the supply network structure. Firstly, a tripartite graph-based supply model is proposed to depict a two-stage supply network, which consists of multiple manufacturers and suppliers as well as the diverse product supply-demand interdependence connecting them. To measure the impacts caused by supplier disruptions and to evaluate the effectiveness of recovery supplier decisions, two supply network performance metrics reflecting product supply availability are also given. Then, the recovery supplier selection problem is described as a combinatorial optimization problem. To solve this problem, a heuristic algorithm, with enhanced variable neighborhood search (EVNS) is designed based on the general framework of a variable neighborhood search. Finally, experiments based on a real-world supply network are conducted. The experimental results indicate that the proposed method is applicable and effective.

Application of variable neighborhood search for solving large-scale many to many hub location routing problems

2019 ◽  
Vol 16 (5) ◽  
pp. 683-697
Author(s):  
Mehdi Abbasi ◽  
Nahid Mokhtari ◽  
Hamid Shahvar ◽  
Amin Mahmoudi
Keyword(s):  
Large Scale ◽  
Variable Neighborhood Search ◽  
Benders Decomposition ◽  
Neighborhood Search ◽  
Np Hard ◽  
Computational Results ◽  
Hub Location ◽  
Routing Problems ◽  
Content Type ◽  
Location Routing

Purpose The purpose of this paper is to solve large-scale many-to-many hub location-routing problem (MMHLRP) using variable neighborhood search (VNS). The MMHLRP is a combination of a single allocation hub location and traveling salesman problems that are known as one of the new fields in routing problems. MMHLRP is considered NP-hard since the two sub-problems are NP-hard. To date, only the Benders decomposition (BD) algorithm and the variable neighborhood particle swarm optimization (VNPSO) algorithm have been applied to solve the MMHLRP model with ten nodes and more (up to 300 nodes), respectively. In this research, the VNS method is suggested to solve large-scale MMHLRP (up to 1,000 nodes). Design/methodology/approach Generated MMHLRP sample tests in the previous work were considered and were added to them. In total, 35 sample tests of MMHLRP models between 10 and 1,000 nodes were applied. Three methods (BD, VNPSO and VNS algorithms) were run by a computer to solve the generated sample tests of MMHLRP. The maximum available time for solving the sample tests was 6 h. Accuracy (value of objective function solution) and speed (CPU time consumption) were considered as two major criteria for comparing the mentioned methods. Findings Based on the results, the VNS algorithm was more efficient than VNPSO for solving the MMHLRP sample tests with 10–440 nodes. It had many similarities with the exact BD algorithm with ten nodes. In large-scale MMHLRP (sample tests with more than 440 nodes (up to 1,000 nodes)), the previously suggested methods were disabled to solve the problem and the VNS was the only method for solving samples after 6 h. Originality/value The computational results indicated that the VNS algorithm has a notable efficiency in comparison to the rival algorithm (VNPSO) in order to solve large-scale MMHLRP. According to the computational results, in the situation that the problems were solved for 6 h using both VNS and VNPSO, VNS solved the problems with more accuracy and speed. Additionally, VNS can only solve large-scale MMHLRPs with more than 440 nodes (up to 1,000 nodes) during 6 h.

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