Competence-driven project portfolio selection, scheduling and staff assignment

Gutjahr, Walter J.; Katzensteiner, Stefan; Reiter, P.; Stummer, Christian; Denk, Michaela

doi:10.1007/s10100-008-0081-z

Cited by 20 publications

(36 citation statements)

References 22 publications

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“…Secondly, the current model considers the efficiency of a human resource in a certain skill as a fixed, static quantity. The model may be extended by taking the possibility of learning into account; this can be done along the lines of the models in [15,17,18,22]. Third, whereas the present model assumes that each project once included in the portfolio must be carried out (this is realistic for situations where projects are based on contracts with customers), also the possibility of abandoning projects might be taken into consideration.…”

Section: Discussionmentioning

confidence: 98%

“…The staff of the firm consists of the employees j 2 J: For the execution of the projects, qualified human labor in different categories which have been called competencies in [17,18] is required. Following [21], we shall use the shorter alternative term skills in this paper.…”

Section: The Modelmentioning

confidence: 99%

“…A set of 24 defined projects (as of late 2005) has been chosen from a (past) real-life scenario originating from the Vienna-based Electronic Commerce Compentence Center (EC3), a public-private-partnership enterprise dedicated to applied research and development in new business models of electronic and mobile business. Actually, the data set used has been prepared for the sake of a dedicated Project portfolio selection under uncertainty with outsourcing opportunities research project focusing on competence-driven project portfolio analysis [15,17,18], resolving the skills of EC3 staff into 80 elementary competencies tailored to the fields of competence specific of EC3, ranging from data and business analytics to digital technologies. Basically, the set of problem data provided for the sample of trial projects covers all parameters of the stochastic optimization problem defined through Eqs.…”

Section: An Illustrative Examplementioning

confidence: 99%

“…Examples of approaches to project scheduling and staffing considering the skills of the available human resources are provided in [1,7,11,32]. In combination with the project portfolio selection decision, scheduling and/or staffing is studied in [4,17,18,31,33].…”

Section: Introductionmentioning

confidence: 99%

“…E.g., in [10,17], Ant Colony Optimization has been used. Genetic Algorithms have been applied in [14,17,18], while [6] uses Scatter Search. In the financial portfolio optimization literature, also other metaheuristic techniques have been adopted, e.g., Simulated Annealing [9].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Project portfolio selection under uncertainty with outsourcing opportunities

Gutjahr

Froeschl

2011

Flex Serv Manuf J

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The paper presents a stochastic optimization model for project portfolio selection under uncertainty about the real efforts required for the execution of the work packages contained in the projects. As a subproblem, the assignment of the work to human resources and the distribution of work over time is addressed. The available workforce is assumed as multi-skilled. Required efforts are modeled as random variables. The recourse action for the case where the available capacities of the internal human resources do not suffice to cover the actual work times consists in delegating parts of the work to external human resources. The staffingand-scheduling subproblem is solved by means of a Frank-Wolfe type algorithm. To solve the upper-level problem of project portfolio determination, a modification of the Variable Neighborhood Search (VNS) algorithm is applied. Experimental results for a benchmark of synthetically generated test instances and for an illustrative example from the E-Commerce Competence Center Austria are provided.

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Section: Discussionmentioning

confidence: 98%

Section: The Modelmentioning

confidence: 99%

Section: An Illustrative Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Project portfolio selection under uncertainty with outsourcing opportunities

Gutjahr

Froeschl

2011

Flex Serv Manuf J

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Knowledge accelerator by transversal competences and multivariate adaptive regression splines

Graczyk-Kucharska

Özmen

Szafrański

et al. 2019

Cent Eur J Oper Res

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Transversal competences constitute a set of the knowledge, skills, and attitudes required for various positions and in different professions. Such competences include: entrepreneurship, teamwork, creativity, and communicativeness; they are increasingly listed by employers in different countries as the key requirements in the labor market. The article presents the model of accelerating the process of acquiring transversal competences, developed based on the analysis of data collected in four countries of the European Union: Poland, Finland, Slovakia, and Slovenia. In the analysis, multivariate additive regression spline method was used, along with artificial neural networks, in order to create the best model describing the influence of different variables on the acceleration of acquiring transversal competences. Herewith, we demonstrated that by accelerating the acquisition of the transversal competence of entrepreneurship is influenced by the following factors: rank of the training method in the developed matrix, student numbers and the weighted average of the pace of acceleration regarding the acquisition of the remaining transversal competences, i.e., teamwork, communicativeness and creativity by the given student. The results validate our new method of the acceleration of acquiring transversal competences by students. Students may be from various higher education institutions in different countries. Developed results may be used in the course of education within the framework of the already planned vocational courses and for developing the skills required by employers for various positions and in different professions.

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A provably convergent heuristic for stochastic bicriteria integer programming

Gutjahr

2008

J Heuristics

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We propose a general-purpose algorithm APS (Adaptive Pareto-Sampling) for determining the set of Pareto-optimal solutions of bicriteria combinatorial optimization (CO) problems under uncertainty, where the objective functions are expectations of random variables depending on a decision from a finite feasible set. APS is iterative and population-based and combines random sampling with the solution of corresponding deterministic bicriteria CO problem instances. Special attention is given to the case where the corresponding deterministic bicriteria CO problem can be formulated as a bicriteria integer linear program (ILP). In this case, well-known solution techniques such as the algorithm by Chalmet et al. can be applied for solving the deterministic subproblem. If the execution of APS is terminated after a given number of iterations, only an approximate solution is obtained in general, such that APS must be considered a metaheuristic. Nevertheless, a strict mathematical result is shown that ensures, under rather mild conditions, convergence of the current solution set to the set of Pareto-optimal solutions. A modification replacing or supporting the bicriteria ILP solver by some metaheuristic for multicriteria CO problems is discussed. As an illustration, we outline the application of the method to stochastic bicriteria knapsack problems by specializing the general framework to this particular case and by providing computational examples.

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Competence-driven project portfolio selection, scheduling and staff assignment

Cited by 20 publications

References 22 publications

Project portfolio selection under uncertainty with outsourcing opportunities

Project portfolio selection under uncertainty with outsourcing opportunities

Knowledge accelerator by transversal competences and multivariate adaptive regression splines

A provably convergent heuristic for stochastic bicriteria integer programming

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