A stochastic programming approach for supply chain network design under uncertainty

European Journal of Operational Research

Volumn 167, Issue 1, 2005, Pages 96-115

  Santoso, Tjendera ^a   Ahmed, Shabbir ^a   Goetschalckx, Marc ^a   Shapiro, Alexander ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

Decomposition methods; Facilities planning and design; Sampling; Stochastic programming; Supply chain network design

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL METHODS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; SAMPLING;

DECOMPOSITION METHODS; FACILITIES PLANNING AND DESIGN; MATERIAL FLOW; SUPPLY CHAIN NETWORK DESIGN;

STOCHASTIC PROGRAMMING;

EID: 13544260501     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2004.01.046     Document Type: Article

References (34)

1. Aikens C.H. Facility location models for distribution planning. European Journal of Operational Research. 22(3):1985;263-279
2. Alonso-Ayuso A., Escudero L.F., Garin A., Ortuno M.T., Perez G. An approach for strategic supply chain planning under uncertainty based on stochastic 0-1 programming. Journal of Global Optimization. 26:2003;97-124
3. Benders J.F. Partitioning procedures for solving mixed variables programming problems. Numersiche Mathematik. 4:1962;238-252
4. Birge J.R., Louveaux F. Introduction to Stochastic Programming. 1997;Springer, New York
5. Birge J.R., Louveaux F.V. A multicut algorithm for two-stage stochastic linear programs. European Journal of Operational Research. 34(3):1988;384-392
6. Cheung R.K.-M., Powell W.B. Models and algorithms for distribution problems with uncertain demands. Transportation Science. 30:1996;43-59
7. Dogan K., Goetschalckx M. A primal decomposition method for the integrated design of multi-period production-distribution systems. IIE Transactions. 31:1999;1027-1036
8. Geoffrion A.M., Graves G.W. Multi-commodity distribution system design by Benders decomposition. Management Science. 20:1974;822-844
9. Geoffrion A.M., Powers R.F. Twenty years of strategic distribution system design: An evolutionary perspective. Interfaces. 25:1995;105-128
10. Gutierrez G.J., Kouvelis P., Kurawala A.A. A robustness approach to uncapacitated network design problems. European Journal of Operations Research. 94:1996;362-376
11. Hamming R.W. Error detecting and error correcting codes. Bell System Tech Journal. 9:1950;147-160
12. M. Hicks, When the chain snaps, eWeek - Enterprise News & Reviews. Available from 〈http://www.eweek.com〉, 2002
13. Hiriart-Urruty J.-B., Lemaréchal C. Convex Analysis and Minimization Algorithms II. 1996;Springer-Verlag, Berlin
14. ILOG Inc, CPLEX 7.0 User's Manual, ILOG CPLEX Division, Incline Village, NV, 2000
15. Kamath K.R., Pakkala T.P.M. A Bayesian approach to a dynamic inventory model under an unknown demand distribution. Computers & Operations Research. 29:2002;403-422
16. Kleywegt A.J., Shapiro A., Homem-De-Mello T. The sample average approximation method for stochastic discrete optimization. SIAM Journal of Optimization. 12:2001;479-502
17. Van Landeghem H., Vanmaele H. Robust planning: A new paradigm for demand chain planning. Journal of Operations Management. 20:2002;769-783
18. J.T. Linderoth, A. Shapiro, S.J. Wright, The empirical behavior of sampling methods for stochastic programming, Annals of Operations Research (forthcoming)
19. Linderoth J.T., Wright S.J. Implementing a decomposition algorithm for stochastic programming on a computational grid. Computational Optimization and Applications. 24:2003;207-250
20. Magnanti T.L., Wong R.T. Accelerating Benders decomposition: Algorithmic enhancement and model selection criteria. Operations Research. 29:1981;464-484
21. Mak W.K., Morton D.P., Wood R.K. Monte Carlo bounding techniques for determining solution quality in stochastic programs. Operations Research Letters. 24:1999;47-56
22. MirHassani S.A., Lucas C., Mitra G., Messina E., Poojari C.A. Computational solution of capacity planning models under uncertainty. Parallel Computing. 26:2000;511-538
23. Norkin V.I., Pflug G.Ch., Ruszczyński A. A branch and bound method for stochastic global optimization. Mathematical Programming. 83:1998;425-450
24. Norkin V.I., Ermoliev Y.M., Ruszczyński A. On optimal allocation of indivisibles under uncertainty. Operations Research. 46:1998;381-395
25. Owen S.H., Daskin M.S. Strategic facility location: A review. European Journal of Operational Research. 111:1998;423-447
26. Ruszczyński A. A regularized decomposition method for minimizing a sum of polyhedral functions. Mathematical Programming. 35:1986;309-333
27. T. Santoso, A comprehensive model and efficient solution algorithm for the design of global supply chains under uncertainty, Ph.D. Thesis, School of Industrial & Systems Engineering, Georgia Institute of Technology, 2002
28. Shapiro A., Homem de Mello T. A simulation-based approach to stochastic programming with recourse. Mathematical Programming. 81:1998;301-325
29. Tsiakis P., Shah N., Pantelides C.C. Design of multi-echelon supply chain networks under demand uncertainty. Industrial & Engineering Chemistry Research. 40:2001;3585-3604
30. Van Slyke R., Wets R.J.-B. L-shaped linear programs with applications to optimal control and stochastic programming. SIAM Journal on Applied Mathematics. 17:1969;638-663
31. Verweij B., Ahmed S., Kleywegt A.J., Nemhauser G., Shapiro A. The sample average approximation method applied to stochastic routing problems: A computational study. Computational Optimization and Applications. 24:2003;289-333
32. Vidal C., Goetschalckx M. Strategic production-distribution models: A critical review with emphasis on global supply chain models. European Journal of Operational Research. 98:1997;1-18
33. Vidal C., Goetschalckx M. A global supply chain model with transfer pricing and transportation cost allocation. European Journal Of Operational Research. 129:2001;134-158
34. Yu C.-S., Li H.-L. A robust optimization model for stochastic logistic problems. International Journal of Production Economics. 64:2000;385-397