Solving stochastic programming models in supply chain design using sampling heuristics

IMA Journal of Management Mathematics

Volumn 22, Issue 1, 2011, Pages 65-77

  Mo, Yin ^a   Harrison, Terry P ^a   Barton, Russell R ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Stochastic programming; Supply chain design; Uniform experiment designs

Indexed keywords

DESIGN; EXPERIMENTS; STOCHASTIC PROGRAMMING; STOCHASTIC SYSTEMS; SUPPLY CHAIN MANAGEMENT; SUPPLY CHAINS;

APPROXIMATE SOLUTION; DEMAND UNCERTAINTY; SAMPLING PROCEDURES; STOCHASTIC PROGRAMMING MODEL; SUPPLY CHAIN DESIGN; SUPPLY CHAIN STRUCTURES; TWO-STAGE STOCHASTIC PROGRAMMING; UNIFORM EXPERIMENT;

STOCHASTIC MODELS;

EID: 79952845550     PISSN: 1471678X     EISSN: 14716798     Source Type: Journal    
DOI: 10.1093/imaman/dpq003     Document Type: Article

References (19)

1. ARNTZEN, B. C., BROWN, G. G., HARRISON, T. P. & TRAFTON, L. L. (1995) Global supply chain management at digital equipment corporation. Interfaces, 25, 69-93.
2. BREITMAN, R. & LUCAS, J. (1987) PLANETS: a modeling system for business planning. Interfaces, 17, 94-106.
3. CAMM, J. D., CHORMAN, T. E., DILL, F. A., EVANS, J. R., SWEENEY, D. J. & WEGRYN, G. W. (1997) Blending OR/MS, judgment, and GIS: restructuring P&G's supply chain. Interfaces, 27, 128-142. (Pubitemid 127342024)
4. COHEN, M. A. & LEE, H. L. (1987) Manufacturing strategy: concepts and methods. The Management of Productivity and Technology in Manufacturing (P. Kleindorfer ed.). New York: Plenum Press, pp. 153-188.
5. DANTZIG, G. B. & INFANGER, G. (1993) Multi-stage stochastic linear programs for portfolio optimization. Ann. Oper. Res., 45, 59-76.
6. DEMPSTER, M. A. H. (2004) Sequential importance sampling algorithms for dynamic stochastic programming. Zapiski Nauchnykh Seminarov POMI, 312, 94-129.
7. FANG, K. T. (1980) The uniform design: application of number theoretic methods in experimental design. Acta Math. Appl. Sin., 3, 363-372.
8. FANG, K. T., LIN, D. K. J., WINKER, P. & ZHANG, Y. (2000) Uniform design: theory and application. Technometrics, 42, 237-248.
9. INFANGER, G. (1992) Monte Carlo (importance) sampling within a benders decomposition algorithm for stochastic linear programs. Ann. Oper. Res., 39, 69-95.
10. KLEYWEGT, A. J., SHAPIRO, A. & HOMEM-DE-MELLO, T. (2001) The sample average approximation method for stochastic discrete optimization. SIAM J. Optim., 12, 479-502.
11. LOUVEAUX, F. V. (1993) Stochastic location analysis. Locat. Sci., 1, 127-154. (Pubitemid 24387333)
12. MAK, W. K., MORTON, D. P. & WOOD, R. K. (1999) Monte Carlo bounding techniques for determining solution quality in stochastic programs. Oper. Res. Lett., 24, 47-56.
13. MELO, M. T., NICKEL, S. & SALDANHA DA GAMA, F. (1997) Facility location and supply chain management- a comprehensive review. Technical Report, ITWM No. 130, Fraunhaufer Institute. ISSN 1434-9973. Available at http://kluedo.ub.uni-kl.de/volltexte/2008/2221/pdf/ bericht130.pdf. Accessed 17 November 2008.
14. MO, Y. (2002) A robust supply chain design under demand uncertainty and hybrid postponement strategies: issues in supply chain management. Ph.D. Dissertation, Department of Management Science and Information Systems, Smeal College of Business, Penn State University, University Park, PA.
15. OWEN, S. H. & DASKIN, M. S. (1998) Strategic facility location: a review. Eur. J. Oper. Res., 111, 423-447. (Pubitemid 128410903)
16. SABRI, E. H. & BEAMON, B. M. (2000) A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28, 581-598.
17. SAHIN, G. & SURAL, H. (2007) A review of hierarchical facility location models. Comput. Oper. Res., 34, 2310-2331. (Pubitemid 46073424)
18. WANG, Y. & FANG, K. T. (1981). A note on uniform distribution and experimental design. Chin. Sci. Bull., 26, 485-489.
19. WANG, Y. C., LIN, D. K. J. & FANG, K. T. (1995) Designing outer array points. J. Qual. Technol., 27, 226-241.