Scenario tree-based heuristics for stochastic inventory-routing problems

INFORMS Journal on Computing

Volumn 21, Issue 2, 2009, Pages 268-285

  Hvattum, Lars Magnus ^a   Løkketangen, Arne ^b   Laporte, Gilbert ^c  

^a NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^b MOLDE UNIVERSITY COLLEGE   (Norway)

^c HEC MONTRÉAL   (Canada)

Author keywords

Heuristic; Inventory routing; Scenario; Stochastic

Indexed keywords

EID: 67649958525     PISSN: 10919856     EISSN: 15265528     Source Type: Journal    
DOI: 10.1287/ijoc.1080.0291     Document Type: Article

References (16)

1. Adelman, D. 2004. A price-directed approach to stochastic inventory/ routing. Oper. Res. 52(4) 499-514.
2. Campbell, A. M., M. W. P. Savelsbergh. 2004. A decomposition approach for the inventory-routing problem. Transportation Sci. 38(4) 488-502.
3. Campbell, A. M., L. W. Clarke, A. J. Kleywegt, M. W. P. Savelsbergh. 1998. The inventory-routing problem. T. G. Crainic, G. Laporte, eds. Fleet Management and Logistics. Kluwer, Boston, 95-113.
4. Chien, T. W., A. Balakrishnan, R. T. Wong. 1989. An integrated inventory allocation and vehicle routing problem. Transportation Sci. 23(2) 67-76.
5. Festa, P., M. G. C. Resende. 2004. An annotated bibliography of GRASP. Technical Report TD-5WYSEW, AT&T Labs Research, Florham Park, NJ.
6. Glover, F. 2000. Multi-start and strategic oscillation methods- Principles to exploit adaptive memory. M. Laguna, J. L. González-Velarde, eds. OR ComputingT ools for Modeling, Optimization and Simulation2 Interfaces in Computer Science and Operations Research. Kluwer, Boston, 1-24.
7. Høyland, K., M. Kaut, S. W. Wallace. 2003. A heuristic for momentmatching scenario generation. Comput. Optim. Appl. 24(2-3) 169-185.
8. Hvattum, L. M., A. Løkketangen, F. Glover. 2005. New heuristics and adaptive memory procedures for Boolean optimization problems. J. Karlof, ed. Integer Programming2 Theory and Practice. CRC Press, Boca Raton, FL, 1-18.
9. Kaut, M., S. W. Wallace. 2007. Evaluation of scenario-generation methods for stochastic programming. Pacific J. Optim. 3(2) 257-271.
10. Kleywegt, A. J., V. S. Nori, M. W. P. Savelsbergh. 2002. The stochastic inventory-routing problem with direct deliveries. Transportation Sci. 36(1) 94-118.
11. Kleywegt, A. J., V. S. Nori, M. W. P. Savelsbergh. 2004. Dynamic programming approximations for a stochastic inventoryrouting problem. Transportation Sci. 38(1) 42-70.
12. Løkketangen, A., F. Glover. 1999. Candidate list and exploration strategies for solving 0/1 MIP problems using a pivot neighborhod. S. Voß, S. Martello, I. H. Osman, C. Roucairol, eds. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Kluwer, Dordrecht, The Netherlands, 141-154.
13. Powell, W. B. 2007. Approximate Dynamic Programming: Solving the Curses of Dimensionality. Wiley, Hoboken, NJ.
14. Prais, M., C. C. Ribeiro. 2000. Reactive GRASP: An application to a matrix decomposition problem in TDMA traffic assignment. INFORMS J. Comput. 12(3) 164-176.
15. Puterman, M. L. 1994. Markov Decision Processes. John Wiley & Sons, New York.
16. Resende, M. G. C., C. C. Ribeiro. 2003. Greedy randomized adaptive search procedures. F. Glover, G. A. Kochenberger, eds. Handbook of Metaheuristics. Kluwer, Boston, 219-249.