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.

Lagrangian Bounds and a Heuristic for the Two-Stage Capacitated Facility Location Problem

2012 ◽  
Vol 1 (1) ◽  
pp. 59-71 ◽  
Author(s):  
Igor Litvinchev ◽  
Edith L. Ozuna
Keyword(s):  
Facility Location ◽  
Location Problem ◽  
Computational Study ◽  
Facility Location Problem ◽  
Mixed Integer ◽  
Variable Cost ◽  
Fixed Cost ◽  
Two Stage ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location

In the two-stage capacitated facility location problem, a single product is produced at some plants in order to satisfy customer demands. The product is transported from these plants to some depots and then to the customers. The capacities of the plants and depots are limited. The aim is to select cost minimizing locations from a set of potential plants and depots. This cost includes fixed cost associated with opening plants and depots, and variable cost associated with both transportation stages. In this work, two different mixed integer linear programming formulations are considered for the problem. Several Lagrangian relaxations are analyzed and compared, and a Lagrangian heuristic producing feasible solutions is presented. The results of a computational study are reported.

An effective hybrid approach to the two-stage capacitated facility location problem

2019 ◽  
Vol 275 (2) ◽  
pp. 467-480 ◽  
Author(s):  
Zhen Yang ◽  
Haoxun Chen ◽  
Feng Chu ◽  
Nengmin Wang
Keyword(s):  
Facility Location ◽  
Location Problem ◽  
Hybrid Approach ◽  
Facility Location Problem ◽  
Two Stage ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location

scholarly journals An Extended Stochastic Goal Mixed Integer Programming for Optimal Portfolio Selection in the Amman Stock Exchange

2019 ◽  
Vol 10 (2) ◽  
pp. 36
Author(s):  
Rula Hani Salman AlHalaseh ◽  
Aminul Islam ◽  
Rosni Bakar
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Portfolio Selection ◽  
Large Scale ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Financial Instrument ◽  
Main Challenge ◽  
Time Period ◽  
Future Time Period

This paper optimally solves the portfolio selection problem that consists of multi assets in a continuous time period to achieve the optimal trade-off between multi-objectives. In this paper, the Stochastic Goal Mixed Integer programming of Stoyan (2009) is extended. The empirical contributions of this research presented on extending the SGMIP model by adding information as a new factor that selects the portfolio elements. The information element used as a portfolio managing characteristics to see whether it is applicable for different problems. The data was collected on a daily basis for all the parameters of the individual stock. Brownian motion formula was used to predict the stock price in the future time period. SP framework used to capture numerous sources of uncertainty and to formulate the portfolio problem. The main challenge of this model is that it contains additional real-world objective and multi types of financial assets, which form a Mixed Integer Programming (MIP). This large-scale problem solved using Optimising Programming Language (OPL) and decomposition algorithm to improve the memory allocation and CPU time. A fascinating result was obtained from the portfolio algorithm design. The ESGMIP portfolio outperforms the Index portfolio return. Under uncertain environment, the availability of information rationalized the diversity when the dynamic portfolio invested in one financial instrument (stocks), and tend to be diversifiable when invested in more than one financial instrument (stock and bond). This work presents a novel extended SGMIP model to reach an optimal solution.

scholarly journals Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

PLoS ONE ◽  
2011 ◽  
Vol 6 (9) ◽  
pp. e24162 ◽  
Author(s):  
Joonhoon Kim ◽  
Jennifer L. Reed ◽  
Christos T. Maravelias
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Large Scale ◽  
Mixed Integer ◽  
Strain Design ◽  
Programming Solution ◽  
Solution Techniques
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