Large-Scale VM Placement with Disk Anti-Colocation Constraints Using Hierarchical Decomposition and Mixed Integer Programming

2017 ◽  
Vol 28 (5) ◽  
pp. 1361-1374 ◽  
Author(s):  
Ye Xia ◽  
Mauricio Tsugawa ◽  
Jose A. B. Fortes ◽  
Shigang Chen
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Large Scale ◽  
Mixed Integer ◽  
Hierarchical Decomposition ◽  
Vm Placement

scholarly journals A New Decomposition-Based Pricing Technique For Solving Large-Scale Mixed IP with a Computer Technique

2016 ◽  
Vol 34 ◽  
pp. 5-20
Author(s):  
A Islam ◽  
M Babul Hasan ◽  
HK Das
Keyword(s):  
Integer Programming ◽  
Mathematical Programming ◽  
Mixed Integer Programming ◽  
Programming Language ◽  
Large Scale ◽  
Mixed Integer ◽  
Huge Number ◽  
Computer Technique

In this paper, we develop a new Decomposition-Based Pricing (DBP) procedure to filter the unnecessary decision ingredients from large scale mixed integer programming (MIP) problem, where the variables are in huge number will be abated and the complicacy of restrictions will be straightforward. We then develop a generalized computer technique corresponding to our new DBP method by using the programming language A Mathematical Programming Language (AMPL). A number of examples have been illustrated to demonstrate our method.GANIT J. Bangladesh Math. Soc.Vol. 34 (2014) 5-20

scholarly journals On the solution of large-scale mixed integer programming scheduling models

2015 ◽  
Vol 136 ◽  
pp. 139-157 ◽  
Author(s):  
Sara Velez ◽  
Andres F. Merchan ◽  
Christos T. Maravelias
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Large Scale ◽  
Mixed Integer ◽  
Scheduling Models

scholarly journals A Hybrid Approach Using an Artificial Bee Algorithm with Mixed Integer Programming Applied to a Large-Scale Capacitated Facility Location Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Guillermo Cabrera G. ◽  
Enrique Cabrera ◽  
Ricardo Soto ◽  
L. Jose Miguel Rubio ◽  
Broderick Crawford ◽  
...  
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Large Scale ◽  
Location Problem ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Facility Location Problem ◽  
Mixed Integer ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location

We present a hybridization of two different approaches applied to the well-known Capacitated Facility Location Problem (CFLP). The Artificial Bee algorithm (BA) is used to select a promising subset of locations (warehouses) which are solely included in the Mixed Integer Programming (MIP) model. Next, the algorithm solves the subproblem by considering the entire set of customers. The hybrid implementation allows us to bypass certain inherited weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. In this paper we demonstrate that BA can be significantly improved by use of the MIP algorithm. At the same time, our hybrid implementation allows the MIP 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 BA with a mathematical programming approach appears to be an interesting research area in combinatorial optimization.

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