Multiple Project Portfolio Scheduling Subject to Mass Customized Service

2015 ◽  
pp. 11-21 ◽  
Author(s):  
Krzysztof Bzdyra ◽  
Zbigniew Banaszak ◽  
Grzegorz Bocewicz
Keyword(s):  
Project Portfolio

Project portfolio valuation using real options

2020 ◽  
Vol 13 (2) ◽  
pp. 126-146
Author(s):  
A.B. Lanchakov ◽  
S.A. Filin ◽  
A.Zh. Yakushev
Keyword(s):  
Real Options ◽  
Real Option ◽  
Project Portfolio ◽  
Risk And Uncertainty ◽  
Expected Effect ◽  
Investment Projects ◽  
Financial Options ◽  
Portfolio Efficiency ◽  
The Face ◽  
Effective Development

Subject. The article analyzes the expected effect of a portfolio of projects in the face of risk and uncertainty, when using real options. Objectives. The purpose is to offer a more objective formula to assess the expected impact of a portfolio of projects for real investment objects under risk and uncertainty, using real options, and provide recommendations for improving the portfolio efficiency. Methods. The study draws on methods of real options and evaluation of investment projects through the real option value, the cash flow discounting method, synthesis, and mathematical modeling. Results. We systematized the main types of real options and developed a formula for calculating the expected effect of project portfolio implementation. The said formula shows that considering the additional long-term costs embedded in a portfolio of real options, which are associated with the use of these real options, and, therefore, reducing the overall risk of projects and the entire portfolio, permit to improve the objectivity of such calculations. Conclusions. When analyzing real options that have real assets as underlying instruments, it is often impossible to apply the computational formulae for financial options, as they differ significantly. The systematization of the main types of real options helps expand the range of application of management solutions. The offered formula enables to improve the efficiency of project insurance under risk and uncertainty and to use additional opportunities for effective development of the company.

Managing Oil and Gas Project Value By Prime (Pertamina Investment Management Engine)

2020 ◽  
Author(s):  
F. Febrian
Keyword(s):  
Portfolio Management ◽  
Corporate Strategy ◽  
Oil And Gas ◽  
Investment Strategy ◽  
Project Portfolio ◽  
Action Plans ◽  
Investment Management ◽  
Micro Scale ◽  
Robust Portfolio ◽  
Oil And Gas Companies

Oil and gas companies are facing an enormous challenge to create value from mature fields. Moreover, price volatility presents a massive impact on project uncertainties. Therefore, robust portfolio management is essential for oil and gas companies to manage critical challenges and uncertainties. The objective of this study is to develop a robust portfolio model to assist top management in oil and gas companies to drive investment strategy. PRIME (Pertamina Investment Management Engine) has been built to visualize advanced oil and gas project portfolio management. The engine observes the relationship between risk-and-return as the main framework drivers. The profitability index is endorsed as a parameter to envisage the investment effectiveness of individual projects. Correspondingly, the risk index is a manifestation of multi-variable analysis involving subsurface uncertainty and price. A nine clusters "tactical board" matrix is provided as the outcome of PRIME to define generic strategy & action plans. The PRIME analysis leads to a dual theme of perspective: both macro and micro-scale. The macro-scale discovers a diversification of strategy and scenario development to achieve long-term objectives. Whereas, micro-scale perspective generates a detailed action plan in a particular cluster as a representation of the short and mid-term corporate strategy. Several strategies and action plans have been recommended, including advanced technology implementation, new gas commercialization, additional incentives in the Production Sharing Contract, tax management renegotiation, and project portfolio rebalancing

scholarly journals A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

2021 ◽  
Vol 11 (10) ◽  
pp. 4575
Author(s):  
Eduardo Fernández ◽  
Nelson Rangel-Valdez ◽  
Laura Cruz-Reyes ◽  
Claudia Gomez-Santillan
Keyword(s):  
Portfolio Optimization ◽  
Imperfect Information ◽  
Model Parameters ◽  
Final Decision ◽  
Project Portfolio ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Group Member

This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

scholarly journals A Hybrid Multi-Population Approach to the Project Portfolio Selection and Scheduling Problem for Future Force Design

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Kyle Robert Harrison ◽  
Saber Elsayed ◽  
Ivan L. Garanovich ◽  
Terence Weir ◽  
Michael Galister ◽  
...  
Keyword(s):  
Portfolio Selection ◽  
Project Portfolio ◽  
Scheduling Problem ◽  
Population Approach ◽  
Project Portfolio Selection

scholarly journals An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jing Xiao ◽  
Jing-Jing Li ◽  
Xi-Xi Hong ◽  
Min-Mei Huang ◽  
Xiao-Min Hu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Experimental Comparison ◽  
Software Project ◽  
Project Portfolio ◽  
Multiobjective Evolutionary Algorithms ◽  
Reference Distance ◽  
Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

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