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.
In the project "Competence-Driven Project Portfolio Analysis" (CDPPA), an integrated system for supporting R&D project selection, staff assignment and activity scheduling with special consideration of the strategic development of competencies has been designed and implemented prototypically. The system has been field-tested at the Electronic Commerce Competence Center (EC3), a publicprivate partnership R&D enterprise. Experiences from this trial application are summarised and discussed, particularly concerning data collection and competence measurement, the benefits and limits of the chosen multi-criteria decision analysis approach, the evaluation of introduced changes to the decision making processes, and the transparency of the formal planning model and its components.
UNIDO maintains a long tradition in the compilation of international statistics on industrial production and, particularly, has developed a suite of tools and techniques for improving third-party data, mainly supplied directly or indirectly by National Statistics Offices, to enable and enhance both cross-country and long-term comparability. However, changing IT environments, socio-economic conditions, and customer requirements increasingly challenge established procedures and behaviors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.