1997
DOI: 10.1016/s0925-5273(96)00019-9
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Stochastic network project scheduling with non-consumable limited resources

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Cited by 110 publications
(42 citation statements)
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“…In highly uncertain and usually very long range, a more sophisticated forecasting method (a possibilistic or probabilistic approach with several estimated parameters and special operators) is not necessarily a crystal ball in the imagination of the future. In order to illustrate the efficiency and stability of the proposed SoS metaheuristic we presented detailed computational results for a larger and challenging project instance borrowed from Golenko-Ginzburg and Gonik [17] and discussed by several authors in the literature.…”
Section: Discussionmentioning
confidence: 99%
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“…In highly uncertain and usually very long range, a more sophisticated forecasting method (a possibilistic or probabilistic approach with several estimated parameters and special operators) is not necessarily a crystal ball in the imagination of the future. In order to illustrate the efficiency and stability of the proposed SoS metaheuristic we presented detailed computational results for a larger and challenging project instance borrowed from Golenko-Ginzburg and Gonik [17] and discussed by several authors in the literature.…”
Section: Discussionmentioning
confidence: 99%
“…At the running of the resource-constrained project borrowed from GolenkoGinzburg and Gonik [17] we changed the default optimality tolerance parameters (Relative Gap = 0.01 % and Absolute Gap = 5 period) and the Time Limit parameter (10 hours). In the presented example, the { WA, W B} = { 1, 1} weight set was used.…”
Section: Implementation Detailsmentioning
confidence: 99%
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“…Research into SRCPSP, however, has remained limited to date, with few computational publications addressing this problem: Igelmund and Radermacher (1983) and Stork (2001) report on experiments with branch-and-bound algorithms, while Golenko-Ginzburg and Gonik (1997) and Tsai and Gemmill (1998) develop greedy and local-search heuristics.…”
Section: Policymentioning
confidence: 99%
“…The difference between PPGA and ABGR is small, which might be due to fact that the solutions in both procedures are near optimal. Golenko-Ginzburg and Gonik [15] test their procedures on only one instance, which has 36 activities and a single renewable resource type. The authors introduce two approaches, one based on an exact procedure to solve consecutive multi-dimensional knapsack problems (Heuristic 1) and one that resorts to heuristic solution of those knapsack problems (Heuristic 2).…”
Section: Comparison With Other Algorithmsmentioning
confidence: 99%