Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Experiments on traveling salesman heuristics
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
Computers and Operations Research - Neural networks in business
Tabu Search
Neuro-Dynamic Programming
Rollout Algorithms for Combinatorial Optimization
Journal of Heuristics
Rollout Algorithms for Stochastic Scheduling Problems
Journal of Heuristics
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
Reinforcement and Local Searc: A Case Study TITLE2:
Reinforcement and Local Searc: A Case Study TITLE2:
Exact and heuristic dynamic programming algorithms for the vehicle routing problem with stochastic demands
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Rollout algorithms are innovative methods, recently proposed by Bertsekas et al. [3], for solving NP-hard combinatorial optimization problems. The main advantage of these approaches is related to their capability of magnifying the effectiveness of any given heuristic algorithm. However, one of the main limitations of rollout algorithms in solving large-scale problems is represented by their computational complexity. Innovative versions of rollout algorithms, aimed at reducing the computational complexity in sequential environments, have been proposed in our previous work [9]. In this paper, we show that a further reduction can be accomplished by using parallel technologies. Indeed, rollout algorithms have very appealing characteristics that make them suitable for efficient and effective implementations in parallel environments, thus extending their range of relevant practical applications.We propose two strategies for parallelizing rollout algorithms and we analyze their performance by considering a shared-memory paradigm. The computational experiments have been carried out on a SGI Origin 2000 with 8 processors, by considering two classical combinatorial optimization problems. The numerical results show that a good reduction of the execution time can be obtained by exploiting parallel computing systems.