Minimum and Worst-Case Performance Ratios of Rollout Algorithms

Journal of Optimization Theory and Applications

Volumn 152, Issue 2, 2012, Pages 378-393

  Bertazzi, Luca ^a  

^a UNIVERSITY OF BRESCIA   (Italy)

Author keywords

Minimum performance ratio; Rollout algorithms; Worst case performance ratio

Indexed keywords

COMBINATORIAL OPTIMIZATION; DYNAMIC PROGRAMMING; STOCHASTIC SYSTEMS;

COST TO GO; DETERMINISTICS; DYNAMIC PROGRAMMING PROBLEMS; HEURISTICS ALGORITHM; MINIMUM PERFORMANCE RATIO; OPTIMAL COSTS; PERFORMANCE RATIO; ROLLOUT ALGORITHMS; STOCHASTIC DYNAMIC PROGRAMMING; WORST-CASE PERFORMANCE RATIO;

HEURISTIC ALGORITHMS;

EID: 84856087445     PISSN: 00223239     EISSN: 15732878     Source Type: Journal    
DOI: 10.1007/s10957-011-9902-7     Document Type: Article

References (21)

1. Bertsekas, D. P., Tsitsiklis, J. N.: Neuro-Dynamic Programming. Athena Scientific, Belmont (1996).
2. Tesauro, G., Galperin, G. R.: On-line policy improvement using Monte Carlo search. Adv. Neural Inf. Process. Syst. 9, 1068-1074 (1997).
3. Bertsekas, D. P., Castanõn, D. A.: Rollout algorithms for stochastic scheduling problems. J. Heuristics 5, 89-108 (1998).
4. Secomandi, N.: Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands. Comput. Oper. Res. 27, 1201-1225 (2000).
5. Secomandi, N.: A rollout policy for the vehicle routing problem with stochastic demands. Oper. Res. 49, 796-802 (2001).
6. Secomandi, N.: Analysis of a rollout approach to sequencing problems with stochastic routing applications. J. Heuristics 9, 321-352 (2003).
7. Novoa, C., Storer, R.: An approximate dynamic programming approach for the vehicle routing problem with stochastic demands. Eur. J. Oper. Res. 196, 509-515 (2009).
8. Guerriero, F., Mancini, M.: A cooperative parallel rollout algorithm for the sequential ordering problem. Parallel Comput. 29, 663-677 (2003).
9. Guerriero, F., Mancini, M.: Parallelization strategies for rollout algorithms. Comput. Optim. Appl. 31, 221-244 (2005).
10. Bertsekas, D. P., Tsitsiklis, J. N., Wu, C.: Rollout algorithms for combinatorial optimization. J. Heuristics 3, 245-262 (1997).
11. Guerriero, F.: Hybrid rollout approaches for the job shop scheduling problem. J. Optim. Theory Appl. 139, 419-438 (2008).
12. Bertsimas, D. J., Demir, R.: An approximate dynamic programming approach to multidimensional Knapsack problems. Manag. Sci. 48, 550-565 (2002).
13. Bertsekas, D. P.: Dynamic Programming and Optimal Control, vol. I. Athena Scientific, Belmont (2005).
14. Martello, S., Toth, P.: Knapsack Problems: Algorithms and Computer Implementations. Wiley, Chichester (1990).
15. Kellerer, H., Pferschy, U., Pisinger, D.: Knapsack Problems. Springer, Berlin (2004).
16. Sahni, S.: Approximate algorithms for the 0-1 knapsack problem. J. ACM 22, 115-124 (1975).
17. Caprara, A., Kellerer, H., Pferschy, U., Pisinger, D.: Approximation algorithms for Knapsack problems with cardinality constraints. Eur. J. Oper. Res. 123, 333-345 (2000).
18. Ibarra, O. H., Kim, C. E.: Fast approximation algorithms for the knapsack and sum of subset problem. J. ACM 22, 463-468 (1975).
19. Lawler, E. L.: Fast approximation algorithms for knapsack problems. Math. Oper. Res. 4, 339-356 (1979).
20. Kellerer, H., Pferschy, U.: A new fully polynomial time approximation scheme for the knapsack problem. J. Comb. Optim. 3, 59-71 (1999).
21. Kellerer, H., Pferschy, U.: Improved dynamic programming in connection with an FPTAS for the Knapsack problem. J. Comb. Optim. 8, 5-11 (2004).