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 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.

Application of Mixed-Integer Programming and Dispatching Rules on Parallel Machine Scheduling with Inserted Idle Time

2013 ◽  
Vol 437 ◽  
pp. 748-751
Author(s):  
Chi Yang Tsai ◽  
Yi Chen Wang
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Parallel Machines ◽  
Optimal Solution ◽  
Parallel Machine Scheduling ◽  
Idle Time ◽  
Dispatching Rules ◽  
Mixed Integer ◽  
Scheduling Problems

This research considers the problem of scheduling jobs on unrelated parallel machines with inserted idle times to minimize the earliness and tardiness. The aims at investigating how particular objective value can be improved by allowing machine idle time and how quality solutions can be more effectively obtained. Two mixed-integer programming formulations combining with three dispatching rules are developed to solve such scheduling problems. They can easy provide the optimal solution to problem involving about nine jobs and four machines. From the results of experiments, it is found that: (1) the inserted idle times decreases objective values more effectively; (2) three dispatching rules are very competitive in terms of efficiency and quality of solutions.

Mixed-Integer Programming Model for Fixture Layout Optimization

1999 ◽  
Vol 121 (4) ◽  
pp. 701-708 ◽  
Author(s):  
Q. A. Sayeed ◽  
E. C. De Meter
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Programming Model ◽  
Optimal Solution ◽  
Geometric Error ◽  
Mixed Integer ◽  
Integer Programming Model ◽  
Mixed Integer Programming Model ◽  
Fea Model ◽  
Candidate Regions

Workpiece deformation during machining is a significant source of machined feature geometric error. This paper presents a linear, mixed integer programming model for determining the optimal locations of locator buttons, supports, and their opposing clamps for minimizing the affect of static workpiece deformation on machined feature geometric error. This model operates on discretized candidate regions as opposed to continuous candidate regions. In addition it utilizes a condensed FEA model of the workpiece in order to minimize model size and computation expense. This model has two advantages over existing nonlinear programming (NLP) formulations. The first is its ability to solve problems in which fixture elements can be placed over multiple regions. The second is that a global optimal solution to this model can be obtained using commercially available software.

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

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