Optimal Project Selection Research Articles

Skill-based framework for optimal software project selection and resource allocation

This paper presents a conceptual framework and a mathematical formulation for software resource allocation and project selection at the level of software skills. First, we introduce a skill-based framework that considers universities, software companies, and potential projects of a country. Based on this framework, we formulate a linear integer program PMax which determines the selection of projects and the allocation of human resources that maximize profit for a certain company. We show that PMax is NP-complete. Therefore, we devise a meta-heuristic, called Tabu Select and Greedily Allocate (TSGA), to overcome the computational complexities. When compared to PMax running on CPLEX, TSGA performs 15 times faster with an accuracy of 98% on small to large size problems where CPLEX converges. On larger problems where CPLEX does not return an answer, TSGA computes a feasible solution in the order of minutes.For demonstration, the proposed skill-based framework and the corresponding mathematical model are applied to Lebanon by performing two surveys on the Lebanese software industry and academia. The case study shows that the proposed framework and mathematical model can be used in practice to improve project selection and resource allocation decisions in software companies.

Mean-Variance Model for Optimal Multinational Project Adjustment and Selection

This paper discusses the multinational project adjustment and selection problem in which project parameters are regarded as random variables. In the paper, adjustment of foreign existing projects and selection of the new foreign projects are considered simultaneously. Typical cash flows and value sources of foreign projects are introduced and a mean-variance optimal multinational adjustment and selection model is proposed. As an illustration, a numerical example is also presented.

Multi-objective discrete urban road network design

This paper addresses the problem of designing urban road networks in a multi-objective decision making framework. Given a base network with only two-way links, and the candidate lane addition and link construction projects, the problem is to find the optimal combination of one-way and two-way links, the optimal selection of network capacity expansion projects, and the optimal lane allocations on two-way links to optimize the reserve capacity of the network, and two new travel time related performance measures. The problem is considered in two variations; in the first scenario, two-way links may have different numbers of lanes in each direction and in the second scenario, two-way links must have equal number of lanes in each direction. The proposed variations are formulated as mixed-integer programming problems with equilibrium constraints. A hybrid genetic algorithm, an evolutionary simulated annealing, and a hybrid artificial bee colony algorithm are proposed to solve these two new problems. A new measure is also proposed to evaluate the effectiveness of the three algorithms. Computational results for both problems are presented.

IT Project Selection using Fuzzy Real Option Optimization Model

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.

Evaluation and Optimization of Research Projects Based on Grey Relational Analysis Method

Research project evaluation is an important part of research management, and there is no unified approach to evaluate research projects．On the basis of the characteristics of complexity and the uncertainty of optimal selection for research projects, grey relational analysis model in research projects selection application has been put forward in this paper. The index system of influencing research projects selection is determined. Application model and procedures of the grey relational analysis method are introduced. According to the degree how close it is with the ideal dot, the optimal project is easy to be selected out. Through the optimization analysis projects selection for research projects, it is proved that the optimal project selection for research projects based on grey relational analysis has the strong recognition judgment ability. It is convenient, quantitative, and strict. It provided a possible new way for evaluation of research projects selecting．

Optimal multinational capital budgeting under uncertainty

This paper discusses the multinational capital budgeting problem — when there are some candidate foreign projects, which project(s) should the investor choose? In the paper, special cash flows and value sources of foreign projects are introduced. Regarding project parameters such as construction costs, annual net operating cash flows, terminal values of the projects as well as the foreign exchange rates as uncertain variables, the paper proposes one new uncertain zero–one integer model for optimal multinational project selection. To solve the problem, a hybrid intelligent algorithm integrating the 99 Methods and genetic algorithm is provided. As an illustration, an application example is also presented.

Dynamic risk management in petroleum project investment based on a variable precision rough set model

In this paper, an adaptive algorithm is designed for dynamic risk management in petroleum project investment based on a variable precision rough set (VPRS) model. In risk management, at each stage of decision-making, experts are invited to identify risk indices and support the decision-maker in evaluating the risk exposure (RE) of individual projects. The VPRS model is used to mine risk rules and determine the significance of risk indices from RE decision tables. Considering that there are multiple risks involved in any petroleum project investment, we use multi-objective programming to obtain the optimal selection of projects with minimum RE, where the significance of risk indices is assigned to each of the corresponding multi-objective functions as a weight. Moreover, we develop a risk ranking model to measure the degree of risk for individual projects in a portfolio. Finally, a numerical example based on a Chinese petroleum company's investments in overseas projects is presented to illustrate the proposed approach, and then conclusions are drawn.

Dynamic order acceptance and capacity planning on a single bottleneck resource

AbstractWe present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for aggregate regular as well as nonregular capacity. The stream of incoming order arrivals is the main source of uncertainty in dynamic order acceptance and the company only has forecasts of the main properties of the future incoming projects. Project proposals arrive sequentially with deterministic interarrival times and a decision on order acceptance and capacity planning needs to be made each time a proposal arrives and its project characteristics are revealed. We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as to deterministically allocate a single bottleneck resource to the accepted projects, both with an eye on maximizing the expected revenues within the problem horizon. We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selection and aggregate capacity usage. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007

On optimizing the selection of business transformation projects

To compete and thrive in a changing business environment, a business can adapt by initiating and successfully carrying out business transformation projects. In this paper we propose a methodology for the optimal selection of such transformational projects. We propose a two-stage methodology based on (1) correlation analytics for identifying key drivers of business performance and (2) advanced portfolio-optimization techniques for selecting optimal business-transformation portfolios in the face of resource constraints, budget constraints, and a rich variety of business rules. We illustrate our methodology through a case study from the electronics industry.

Real Options Based Analysis of Optimal Pharmaceutical Research and Development Portfolios

This paper presents a stochastic optimization model (OptFolio) of pharmaceutical research and development (R&D) portfolio management using a real options approach for making optimal project selection decisions. A method is developed to model new product development as a series of continuation/abandonment options, deciding at each stage in pharmaceutical R&D whether to proceed further or stop development. Multistage stochastic programming is utilized to model the flexibility afforded by the abandonment option. The resulting mixed-integer linear programming formulation is applied to a case study involving the selection of the optimal product portfolio from a set of 20 candidate drugs at different stages in the developmental pipeline over a planning horizon of 6 years. This proposed framework provides a road map for future decisions by tracking the decision of abandonment over time and calculating the minimum market value above which development is continued under changing resource constraints and estimated ...

Capital Budgeting with Multiple Criteria and Multiple Decision Makers: A Fuzzy Approach

A capital budgeting model with multiple criteria and multiple decision makers (MCMDM) is more likely to provide realistic solutions than linear or goal programming models. This paper adopts a fuzzy approach to solve MCMDM capital budgeting problems. This approach is based on two fundamental human cognitive processes: (i) all decision makers who are involved in the capital budgeting problem have goal setting and compromise behavior for seeking multiple criteria, and (ii) each decision maker has a preference for the budget availability level. A solution procedure is proposed to systematically identify a fuzzy optimal selection of possible projects that can not only reach the best compromise value for the multiple criteria, but also use the best budget availability level according to the multiple decision makers’ preferences. The optimal selection can help the firm make a realistic decision regarding its strategic investment. A comparison study of the fuzzy approach with other approaches shows the advantages of using the fuzzy approach.

Selected Papers from IFSA'99

The past few years we have witnessed a crystallization of soft computing as a means towards the conception and design of intelligent systems. Soft Computing is a synergetic integration of neural networks, fuzzy logic and evolutionary computation including genetic algorithms, chaotic systems, and belief networks. In this volume, we are featuting seven papers devoted to soft computing as a special issue. These papers are selected from papers submitted to the "The eighth International Fuzzy Systems Association World Congress (IFSA'99)", held in Taipei, Taiwan, in August 1999. Each paper received outstanding recommendations from its reviewers. G-H Tzeng et al. integrate fuzzy numbers, fuzzy regression, and a fuzzy DEA approach as a performance evaluation model for forecasting the productive efficiency of a set of production units when some data are fuzzy numbers. A case of Taipei City Bus Company is adopted for illustration. Y. Shi et al. adopts a fuzzy programming approach to solve a MCMDM (multiple criteria and multiple decision makers) capital budget problem. A solution procedure is proposed to systematically identify a fuzzy optimal selection of possible projects. N. Nguyen et al. propose a new formalism (Chu spaces) to describe parallelism and information flow. Chu spaces provide uniform explanations for different choices of fuzzy methodology, such as choices of fuzzy logical operations of membership functions or defuzzifications. M-C Su et al. propose a technique based on the SOM-based fuzzy systems for voltage security margin estimation. This technique was tested on 1604 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficiency. By defining the concept of approximate dependency and a similarity measure, S-L Wang et al. present a method of using analogical reasoning to infer approximate answers for null queries on similarity-based fuzzy relational databases. K.Yeh et al. use adaptive fuzzy sliding mode control for the structural control of bridges. Combing fuzzy control and sliding mode control can reduce the complexity of fuzzy rule bases and ensure the stability and robustness. This model is demonstrated by three types of bridges, with LRB, sliding isolators and no isolation device. Based on a novel fuzzy clustering algorithm, Y-H Kuo et al. propose an adaptive traffic prediction approach to generalize and unveil the hidden structure of traffic patterns with features of robustness, high accuracy and high adaptability. The periodical, Poisson and real video traffic patterns have been used to verify their approach and investigate its properties. We would like to express our sincere gratitude to everyone who has contributed to this special issue including the authors, the co-reviewers, the JACI Editors-in-Chief Toshio Fukuda and Kaoru Hirota.

Asymmetric Information, Corporate Myopia, and Capital Gains Tax Rates: An Analysis of Policy Prescriptions

We develop a model of corporate myopia in which the interaction between asymmetric information and short-term trading by equity holders induces firms to undertake short-term rather than long-term projects, which are intrinsically more valuable. We study the effectiveness of alternative policy prescriptions in eliminating myopia. We show that a capital gains tax cut for long-term equity holders induces optimal project selection; an across-the-board tax cut has no such impact. We characterize the long-term capital gains tax rate which eliminates corporate myopia. Further, we show that a long-term capital gains tax cut does not induce a bias toward inefficient long-term projects when it is, in fact, short-term projects which are more valuable. In contrast, an investment tax credit directed at long-term projects leads to such a bias. Finally, we show that reducing the long-term capital gains tax rate to the level required to eliminate myopic investment behavior may also lead to an increase in government tax revenues.Journal of Economic Literature Classification Numbers: H21, G31, D82.

Optimal Project Selection: Stochastic Knapsack with Finite Time Horizon

Optimal Project Selection: Stochastic Knapsack with Finite Time Horizon

Optimal project selection: Stochastic knapsack with finite time horizon

A time-constrained capital-budgeting problem arises when projects, which can contribute to achieving a desired target state before a specified deadline, arrive sequentially. We model such problems by treating projects as randomly arriving requests, each with a funding cost, a proposed benefit, and a known probability of success. The problem is to allocate a non-renewable initial budget to projects over time so as to maximise the expected benefit obtained by a certain time, T, called the deadline, where T can be either a constant or a random variable. Each project must be accepted or rejected as soon as it arrives. We developed a stochastic dynamic programming formulation and solution of this problem, showing that the optimal strategy is to dynamically determine ‘acceptance intervals’ such that a project of type i is accepted when, and only when, it arrives during an acceptance interval for projects of type i.

Optimal project selection when borrowing and lending rates differ

Mathematical programming is employed to obtain an optimal decision rule for project approval in capital budgeting in a nonperfect capital market. We use the framework due to Martin Weingartner to formulate the decision problem in a deterministic setting and derive the optimal rules for the acceptance/rejection of a single project explicitly in the two cases where the borrowing/lending rates for capital are constant and time-dependent, respectively.

A Stochastic Capital Rationing Model

This paper addresses the issue of optimal project selection for capital expenditures assuming uncertain budgetary allocations. A critical review of the historical development of capital budgeting models indicates two major deficiencies: (i) deterministic models ignore the uncertain nature of capital budgeting problems; and (ii) those models which do incorporate the concept of uncertainty have serious computational problems when applied to larger problems. A stochastic capital rationing model (SCRM) is proposed which makes use of recent developments in stochastic programmes with recourse. This model remains computationally tractable despite the explicit incorporation of uncertainty and the application of theoretically sound penalties for constraint violations. Two problems are introduced which illustrate the model's superiority over comparable deterministic formulations. By varying both the probability distributions of the stochastic constraints and the borrowing rates, it was possible to identify the impact these factors have on project selections.

A Stochastic Capital Rationing Model

This paper addresses the issue of optimal project selection for capital expenditures assuming uncertain budgetary allocations. A critical review of the historical development of capital budgeting models indicates two major deficiencies: (i) deterministic models ignore the uncertain nature of capital budgeting problems; and (ii) those models which do incorporate the concept of uncertainty have serious computational problems when applied to larger problems. A stochastic capital rationing model (SCRM) is proposed which makes use of recent developments in stochastic programmes with recourse. This model remains computationally tractable despite the explicit incorporation of uncertainty and the application of theoretically sound penalties for constraint violations. Two problems are introduced which illustrate the model's superiority over comparable deterministic formulations. By varying both the probability distributions of the stochastic constraints and the borrowing rates, it was possible to identify the impact these factors have on project selections.

Optimal Scheduling of Water Supply Projects

Some capacity expansion problems encountered in water resources development are studied. Models yielding least-cost shedules (i.e. optimal selection and sequencing of potential projects) in order to meet a stipulated increase in water demand are first discussed. However, recognizing uncertainties in the future demand, these ought to be taken into account. In order to do this, a new general dynamic programming algorithm has been developed. Depending on how detailed we are able to state the preferences against shortage, chance-constraints or penalty functions are included in the optimization. The use of the algorithm, as an aid in the current planning, is illustrated by solving an example under deterministic as well as stochastic assumptions about the future demand.

Allocation of funds to R&D laboratories by a central agency

The problem of allocating funds to a number of R&D laboratories under the control of a federal or central agency is considered, which is a two-stage decision-making process involving the optimal selection of projects within laboratories and allocation of budgets to the laboratories by the federal organization. An analytical method, based upon the concept of `equitable dissatisfaction,' is presented for solving the problem. A numerical example is given to illustrate the application of the model.