A survey on metaheuristics for stochastic combinatorial optimization
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New fuzzy models for time-cost trade-off problem
Fuzzy Optimization and Decision Making
Using artificial bee colony to solve stochastic resource constrained project scheduling problem
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Empirical stochastic branch-and-bound for optimization via simulation
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A Fuzzy Simulated Annealing approach for project time-cost tradeoff
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Many applications such as project scheduling, workflow modeling, or business process re-engineering incorporate the common idea that a product, task, or service consisting of interdependent time-related activities should be produced or performed within given time limits. In real-life applications, certain measures like the use of additional manpower, the assignment of highly-skilled personnel to specific jobs, or the substitution of equipment are often considered as means of increasing the probability of meeting a due date and thus avoiding penalty costs. This paper investigates the problem of selecting, from a set of possible measures of this kind, the combination of measures that is the most cost-efficient. Assuming stochastic activity durations, the computation of the optimal combination of measures may be very expensive in terms of runtime. In this article, we introduce a powerful stochastic optimization approach to determine a set of efficient measures that crash selected activities in a stochastic activity network. Our approach modifies the conventional Stochastic Branch-and-Bound, using a heuristic--instead of exact methods--to solve the deterministic subproblem. This modification spares computational time and by doing so provides an appropriate method for solving various related applications of combinatorial stochastic optimization. A comparative computational study shows that our approach not only outperforms standard techniques but also definitely improves conventional Stochastic Branch-and-Bound.