IT Project Selection using Fuzzy Real Option Optimization Model

2012 ◽  
Vol 3 (3) ◽  
pp. 37-49
Author(s):  
Shashank Pushkar ◽  
Prity Kumari ◽  
Akhileshwar Mishra
Keyword(s):  
Mathematical Model ◽  
Portfolio Selection ◽  
Optimization Model ◽  
Real Option ◽  
Programming Model ◽  
Optimization Technique ◽  
Option Value ◽  
It Services ◽  
Solution Approach ◽  
Service Portfolio

Optimal selection of interdependent IT and e-business projects for funding in multi-period has been challenging in the framework of Real Option analysis. This paper presents a mathematical model to optimize the fuzzy Option value for multi-stage portfolio of such projects. A fuzzy Option model is used to maximize the Option value of each project. The IT and e-service portfolio selection problem is formulated as a fuzzy zero–one integer programming model that can handle both uncertain and flexible parameters to determine the optimal project portfolio. The idea of optimizing the fuzzy real option value of the portfolio is to maximize the overall value and to minimize the downside risk of the selected portfolio for funding. A transformation method based on qualitative possibility theory is developed to convert the fuzzy portfolio selection model into a crisp mathematical model from the risk-averse perspective. The transformed model can be solved by an optimization technique. The optimization model and solution approach can help e-entrepreneurs and IT managers in optimal funding decision making for projects prioritization to implement e-business and other IT services.

scholarly journals A TYPE-2 FUZZY OPTIMIZATION MODEL FOR PROJECT PORTFOLIO SELECTION AND SCHEDULING INCORPORATING PROJECT INTERDEPENDENCY AND SPLITTING

2021 ◽  
Vol 27 (2) ◽  
pp. 493-510
Author(s):  
Samaneh Zolfaghari ◽  
Seyed Meysam Mousavi ◽  
Jurgita Antuchevičienė
Keyword(s):  
Portfolio Selection ◽  
Optimization Model ◽  
Fuzzy Optimization ◽  
Project Portfolio ◽  
Time Value Of Money ◽  
Solution Approach ◽  
Project Portfolio Selection ◽  
The Interest Rate ◽  
Interval Type

This paper presents a new optimization model and a new interval type-2 fuzzy solution approach for project portfolio selection and scheduling (PPSS) problem, in which split of projects and re-execution are allowable. Afterward, the approach is realized as a multi-objective optimization that maximizes total benefits of projects concerning economic concepts by considering the interest rate and time value of money and minimizes the tardiness value and total number of interruptions of chosen projects. Besides, budget and resources limitation, newfound relations are proposed to consider dependency relationships via a synergy among projects to solve PPSS problem hiring interval type-2 fuzzy sets. For validation of the model, numerical instances are provided and solved by a new extended procedure based on fuzzy optimistic and pessimistic viewpoints regarding several situations. In the end, their results are studied. The results show that it is more beneficial when projects are allowed to be split.

CREDIBILITY-BASED FUZZY MATHEMATICAL PROGRAMMING MODEL FOR PORTFOLIO SELECTION UNDER UNCERTAINTY

2014 ◽  
Vol 13 (01) ◽  
pp. 101-135 ◽  
Author(s):  
MUKESH KUMAR MEHLAWAT ◽  
PANKAJ GUPTA
Keyword(s):  
Mathematical Programming ◽  
Portfolio Selection ◽  
Programming Model ◽  
Fuzzy Optimization ◽  
Chance Constrained Programming ◽  
Fuzzy Mathematical Programming ◽  
Mathematical Programming Model ◽  
Two Phase ◽  
Solution Approach ◽  
Practical Applications

In this paper, we develop a hybrid bi-objective credibility-based fuzzy mathematical programming model for portfolio selection under fuzzy environment. To deal with imprecise parameters, we use a hybrid credibility-based approach that combines the expected value and chance constrained programming techniques. The model simultaneously maximizes the portfolio return and minimizes the portfolio risk. We also consider an additional important criterion, namely, portfolio liquidity as a constraint in the model to make it better suited for practical applications. The proposed fuzzy optimization model is solved using a two-phase approach. An empirical study is included to demonstrate applicability of the proposed model and the solution approach in real-world applications of portfolio selection.

scholarly journals Mathematical Model for Cargo Allocation Problem in Synchromodal Transportation

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 540
Author(s):  
Nijolė Batarlienė ◽  
Raimondas Šakalys
Keyword(s):  
Mathematical Model ◽  
Integer Programming ◽  
Environmental Impact ◽  
Waiting Time ◽  
Optimization Model ◽  
Traffic Management ◽  
Transport Process ◽  
Programming Model ◽  
Stochastic Integer Programming ◽  
Optimization Task

Synchromodality is a freight transport process in which information is exchanged expeditiously in order to maximize the benefits of different modes of transport and transport nodes in terms of efficiency and environmental impact. The aim of the study is to analyze the problems of synchronized intermodal traffic management between the main port and inland transport nodes in European transport corridors and to find reliable solutions to these problems. Therefore, the main purpose of this article is to investigate the problem of the distribution of containers transported by rail between two transport terminals in a synchronous transport network. A specific optimization model is presented in this article. This optimization task is formulated as a stochastic integer programming model between the terminals located in Vilnius and Klaipeda Seaport, the essence of which is as follows: (a) to minimize the waiting time for container cargo at the location—terminal No. 1; (b) to minimize the total journey time of the train; (c) to minimize the waiting time for containerized cargo at the point of arrival—terminal No. 2.

A fuzzy multi-objective programming optimization model for emergency resource dispatching under equitable distribution principle

2021 ◽  
pp. 1-10
Author(s):  
Zhaoping Tang ◽  
Wenda Li ◽  
Shijun Yu ◽  
Jianping Sun
Keyword(s):  
Optimization Model ◽  
Programming Model ◽  
Ideal Point ◽  
Optimal Solution ◽  
Multi Objective ◽  
Emergency Rescue ◽  
Satisfaction Degree ◽  
Multi Objective Programming ◽  
Parameter Interval ◽  
Objective Programming

In the initial stage of emergency rescue for major railway emergencies, there may be insufficient emergency resources. In order to ensure that all the emergency demand points can be effectively and fairly rescued, considering the fuzzy properties of the parameters, such as the resource demand quantity, the dispatching time and the satisfaction degree, the railway emergency resources dispatching optimization model is studied, with multi- demand point, multi-depot, and multi-resource. Based on railway rescue features, it was proposed that the couple number of relief point - emergency point is the key to affect railway rescue cost and efficiency. Under the premise of the maximum satisfaction degree of quantity demanded at all emergency points, a multi-objective programming model is established by maximizing the satisfaction degree of dispatching time and the satisfaction degree of the couple number of relief point - emergency point. Combined with the ideal point method, a restrictive parameter interval method for optimal solution was designed, which can realize the quick seek of Pareto optimal solution. Furthermore, an example is given to verify the feasibility and effectiveness of the method.

scholarly journals ENTRY-LEVEL ENTREPRENEURS AND THE CHOICE-OF-ENTITY CHALLENGE

2013 ◽  
Vol 10 (2) ◽  
Author(s):  
Emily Ann Satterthwaite
Keyword(s):  
Real Option ◽  
Lower Income ◽  
Option Value ◽  
Entry Level ◽  
Deliberation Process ◽  
New Business ◽  
Tax Savings ◽  
First Time ◽  
Real Option Value ◽  
Future Growth

For first-time, lower-income and credit-constrained entrepreneurs (“entry-level entrepreneurs”), the employment tax savings proffered by a longstanding tax shelter known as the “Sub-S Shelter” can be particularly salient. Such hypersalience is problematic from a policy perspective. It not only increases the costs and complexity of the entry-level entrepreneur’s deliberation process concerning the appropriate entity for her business, but it distorts her incentives to choose the entity that best supports her business’s future growth. I argue that because the hypersalience of the Sub-Shelter is likely to be more pronounced for entry-level entrepreneurs than for entrepreneurs with more experience or better access to capital, the burdens of the shelter are distributionally regressive. As an alternative to full-scale reforms that would eliminate the demand for the Sub-S Shelter but may be politically infeasible, I suggest that the shelter’s regressive hypersalience can be addressed by government measures to provide choice-of-entity information tailored to the needs and concerns of entry-level entrepreneurs. Such targeted information can mitigate the hypersalience of the Sub-S Shelter by underscoring the risks of relying on it, while highlighting the real option value of choosing a more flexible business entity such as an LLC. By nudging entry-level entrepreneurs towards neutrality in regard to their choice-of-entity decisions, this approach has the potential to improve both the efficiency and the equity of a key step in formalizing a new business. 

scholarly journals A Novel Approach for Efficient Training of Deep Neural Networks

2018 ◽  
Vol 11 (3) ◽  
pp. 954
Author(s):  
D.T.V. Dharmajee Rao ◽  
K.V. Ramana
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Programming Model ◽  
Optimization Technique ◽  
Matrix Multiplication ◽  
Training Algorithms ◽  
Training Time ◽  
Large Matrix ◽  
Novel Approach ◽  
Parallel Programming Model

<p style="text-indent: 1.27cm; margin-bottom: 0.35cm; line-height: 115%;" align="justify"><span style="font-family: Arial,serif;"><span style="font-size: small;"><em>Deep Neural Network training algorithms consumes long training time, especially when the number of hidden layers and nodes is large. Matrix multiplication is the key operation carried out at every node of each layer for several hundreds of thousands of times during the training of Deep Neural Network. Blocking is a well-proven optimization technique to improve the performance of matrix multiplication. Blocked Matrix multiplication algorithms can easily be parallelized to accelerate the performance further. This paper proposes a novel approach of implementing Parallel Blocked Matrix multiplication algorithms to reduce the long training time. The proposed approach was implemented using a parallel programming model OpenMP with collapse() clause for the multiplication of input and weight matrices of Backpropagation and Boltzmann Machine Algorithms for training Deep Neural Network and tested on multi-core processor system. Experimental results showed that the proposed approach achieved approximately two times speedup than classic algorithms.</em></span></span></p>

scholarly journals Real option value and random jumps: application of a simulation model

2009 ◽  
Vol 41 (23) ◽  
pp. 2977-2989 ◽  
Author(s):  
Susana Alonso-Bonis ◽  
Valentín Azofra-Palenzuela ◽  
Gabriel de la Fuente-Herrero
Keyword(s):  
Simulation Model ◽  
Real Option ◽  
Option Value ◽  
Random Jumps ◽  
Real Option Value
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