Competence development, Metaheuristics, Optimization, Project portfolio, Project scheduling, Staff assignment,

No abstract

This paper presents a new model for project portfolio selection, paying specific attention to competence development. The model seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub-problem, scheduling and staff assignment for a candidate set of selected projects must also be optimized. We provide a nonlinear mixed-integer program formulation for the overall problem, and then propose heuristic solution techniques composed of (i) a greedy heuristic for the scheduling and staff assignment part, and (ii) two (alternative) metaheuristics for the project selection part. The paper outlines experimental results on a real-world application provided by the E

Abstract. Motivated by an application in project portfolio analysis under uncertainty, we develop an algorithm S-VNS for solving stochastic combinatorial optimization (SCO) problems based on the Variable Neighborhood Search (VNS) metaheuristic, and show its theoretical soundness by a mathematical convergence result. S-VNS is the first generalpurpose algorithm for SCO problems using VNS. It combines a classical VNS search strategy with a sampling approach with suitably increasing sample size. After the presentation of the algorithm, the considered application problem in project management, which combines a project portfolio decision on an upper level and project scheduling as well as staff assignment decisions on a lower level, is described. Uncertain work times require a treatment as an SCO problem. First experimental results on the application of S-VNS to this problem are outlined.

In this study, we analyzed the expression of different leukocyte surface antigens, of the adhesion molecules ELAM-1 and GMP-140 and binding of various lectins and neoglycoproteins in inflamed gingival tissue. Cell suspensions from collagenase-digested gingiva were analyzed by flow cytometry in a FACScan. The expression of ELAM-1, GMP-140, carbohydrate structures and lectins in gingival specimens was also studied by immunohistochemistry. Gingival tissue of patients with active periodontal disease contained between 5% and 50% CD45+ mononuclear cells, consisting mainly of CD19+ cells (B lymphocytes). CD62, resembling GMP-140, and ELAM-1 were strongly expressed on endothelial cells of these patients. Control subjects usually contained almost no CD45+ cells in their gingiva and no CD62+ or ELAM-1-positive endothelial cells could be found in 5 of 6 control persons. Analysis of the glycosylation pattern revealed staining of infiltrating cells by peanut agglutinin (PNA; specificity for galactose), whereas soy bean agglutinin (SBA; specificity for N-acetyl-galactosamine) bound to epithelial cells. An endogenous lactosyl-specific lectin could be detected on endothelial cells by binding of lactosyl-BSA. Ulex europeus I agglutinin (UEA-1, specific for fucose) showed selective staining of endothelial and epithelial cells. Expression of a fucose-binding lectin, demonstrated by binding of fucosylated BSA, could be found on infiltrating cells. The adhesion molecules ELAM-1 and GMP-140 seem to be involved in cell adhesion during chronic inflammation of the gingiva. Interaction of other carbohydrate residues with endogenous lectins might resemble additional adhesion mechanisms in inflamed gingiva.