Selecting Large Portfolios of Social Projects in Public Organizations

Table 1

General characteristics.

Paper	Objectives	Maximum number of projects in portfolio	Resource allocation policy	Solution methodology
Ghasemzadeh and Archer, 2000 [18]	General benefits integrated in a weighted sum	10	Complete	Integer linear programming model adjusted interactively
Stummer and Heidenberger, 2003 [4]	General benefit and resources consumption by period of scheduling	30	Complete	Integer linear programming model to get efficient solutions; the best compromise solution is selected interactively
Wang and Hwang, 2007 [6]	Total benefit obtained by summing expected returns of the projects minus total inversion	20	Partial	Fuzzy model
Gutjahr et al., 2008 [7]	Economic gains and strategic gains	18	Partial	Nonlinear mixed integer programming model, greedy heuristic, and two alternative metaheuristics (ACO and GA)
Carazo et al., 2010 [5]	General benefit categories	90	Complete	Metaheuristics (SS-PPS, Scatter Search)
Gutjahr et al., 2010 [8]	Economic benefits and competence benefits	18	Partial	Mixed integer linear model, two metaheuristics (NSGA-II and P-ACO)
Litvinchev et al., 2010 [15]	Portfolio quality and number of projects in portfolio integrated in a weighted sum	25000	Partial	Mixed integer linear programming model; a compromise solution is obtained interactively
Litvinchev et al., 2011 [16]	Portfolio quality and number of projects in portfolio integrated in a weighted sum	10000	Partial	Mixed integer linear programming model; a compromise solution is obtained interactively
Gutjahr and Froeschl, 2013 [9]	Expected return minus outsourced costs	15	Partial	Metaheuristic method (S-VNS) and the Frank-Wolfe algorithm