BFC-MSMIP: An exact branch-and-fix coordination approach for solving multistage stochastic mixed 0-1 problems

TOP

Volumn 17, Issue 1, 2009, Pages 96-122

  Escudero, Laureano F ^a   Garín, Araceli ^b   Merino, María ^b   Pérez, Gloria ^b  

^a UNIVERSIDAD REY JUAN CARLOS   (Spain)

^b UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

Branch and fix coordination; Branchand node selection; Multistage stochastic mixed 0 1 programming; Nonanticipativity constraints; Scenario clustering; Twinnode family

Indexed keywords

EID: 67349231593     PISSN: 11345764     EISSN: 18638279     Source Type: Journal    
DOI: 10.1007/s11750-009-0083-6     Document Type: Conference Paper

References (58)

1. Ahmed S, King A, Parija G (2003) A multi-stage stochastic integer programming approach for capacity expansion under uncertainty. J Glob Optim 26:3-24
2. Alonso-Ayuso A, Escudero L, Ortuño M (2000) A stochastic 0-1 program based approach for air traffic management. Eur J Oper Res 120:47-62
3. Alonso-Ayuso A, Escudero L, Garín A, Ortuño M, Pérez G (2003) An approach for strategic supply chain planning based on stochastic 0-1 programming. J Glob Optim 26:97-124
4. Alonso-Ayuso A, Escudero L, Ortuño M (2003b) BFC, a branch-and-fix coordination algorithmic framework for solving some types of stochastic pure and mixed 0-1 programs. Eur J Oper Res 151:503-519
5. Alonso-Ayuso A, Escudero L, Garín A, Ortuntild;o M, Pérez G (2005a) On the product selection and plant dimensioning problem under uncertainty. Omega 33:307-318
6. Alonso-Ayuso A, Escudero L, Ortuño M (2005b) Modeling production planning and scheduling under uncertainty. In: Wallace S, Ziemba W (eds) Applications of stochastic programming. MPS-SIAM-series in optimization, pp 217-252
7. Alonso-Ayuso A, Escudero L, Ortuño M, Pizarro C (2007) On a stochastic sequencing and scheduling problem. Comput Oper Res 34:2604-2624
8. Alonso-Ayuso A, Escudero L, Guignard M, Quinteros M, Weintraub A (2008) Forest management under uncertainty, in the refereeing process
9. Benders J (1962) Partitioning procedures for solving mixed variables programming problems. Numer Math 4:238-252
10. Birge J, Louveaux F (1997) Introduction to stochastic programming. Springer, Berlin
11. CaroØe C, Schultz R (1999) Dual decomposition in stochastic integer programming. Oper Res Lett 24:37-45
12. Carøe C, Tind J (1998) L-shaped decomposition of two-stage stochastic programs with integer recourse. Math Program 83:451-464
13. Cristobal M, Escudero L, Monge J (2009) On stochastic dynamic programming for solving large-scale planning problems under uncertainty. Comput Oper Res 36:2418-2428
14. Dert C (1998) A dynamic model for asset liability management for defined benefit pension funds. In: Ziemba W, Mulvey J (eds) Worldwide asset and liability modeling. Cambridge University Press, Cambridge, pp 501-536
15. Dolan E, Moré J (2002) Benchmarking optimization software with performance profiles. Math Program 201-213
16. Drijver S, Haneveld WK, van der Vlerk M (2003) Asset liability management modeling using multistage mixed-integer stochastic programming. In: Scherer B (ed) Asset and liability management tools: a handbook for best practices. Risk Books
17. Escudero L, Garín A, Merino M, Pérez G (2006) On multistage stochastic integer programming model and algorithm for incorporating logical constraints in assets and liabilities management under uncertainty. Comput Manag Sci, doi: 10.1007/s10287-006-0035-7
18. Escudero L, Garín A, Merino M, Pérez G (2007) A two-stage stochastic integer programming approach as a mixture of branch-and-fix coordination and benders decomposition schemes. Ann Oper Res 152:395-420
19. Escudero L,Garín A, Merino M, Pérez G, 2009. A general algorithm for solving two-stage stochastic mixed 0-1 first-stage problems. Comput Oper Res 36:2590-2600
20. Escudero L, Monge J, Romero Morales D, Wang J (2009b) On a stochastic multi-stage dynamic programming approach for revenue management, in preparation
21. Groewe-Kuska N, Kiwiel K, Nowak M, Römisch W, Wegner I (2001) Power management in a hydro-thermal system under uncertainty by Lagrangian relaxation. In: Greengard C, Ruszczynski A (eds) Decision making under uncertainty: Energy and power, pp 39-70
22. Heitsch H, Römisch W (2003) Scenario reduction algorithms in stochastic programming. Comput Algorithms Appl 24:187-206
23. Heitsch H, Römisch W (2009) Scenario tree modeling for multistage stochastic programs. Math Program Ser A 118:371-406
24. Hemmecke R, Schultz R (2001) Decomposition methods for two-stage stochastic integer programs. In: Grotschel M, Krumke SO, Rambau J (eds) Online optimization of large scale systems. Springer, Berlin, pp 601-622
25. Hernández P, Alonso-Ayuso A, Escudero L, Guignard M, Marianov V, Weintraub A (2008) A branch-and-cluster coordination scheme for selection of prison facility sites under uncertainty, in the refereeing process
26. Higle J, Sen S (1996) Stochastic decomposition. Kluwer Academic, Dordrecht
27. Hoyland K, Wallace W (2001) Generating scenario trees for multi-stage decision problems. Manag Sci 47:295-307
28. Kall P, Wallace S (1994) Stochastic programming. Wiley, New York
29. Klein Haneveld W, van der Vlerk M (1999) Stochastic integer programming: General models and algorithms. Ann Oper Res 85:39-57
30. Klein Haneveld W, van der Vlerk M (2001) Optimizing electricity distribution using integer recourse models. In: Uryasev S, Pardalos P (eds) Stochastic optimization: Algorithms and applications. Kluwer Academic, Dordrecht, pp 137-154
31. Laporte G, Louveaux F (2002) An integer L-shaped algorithm for the capacitated vehicle routing problem with stochastic demands. Oper Res 50:415-423
32. Linderoth J, Savelsbergh M (1999) A computational study of search strategies for mixed integer programming. INFORMS J Comput 11:173-187
33. Lucas C, Mirhassani S, Mitra G, Poojari C (2001) An application of Lagrangian relaxation to a capacity planning problem under uncertainty. J Oper Res Soc 52:1256-1266
34. Lulli G, Sen S (2006) A heuristic procedure for stochastic integer programming with complete recourse. Manag Sci 171:879-890
35. Maatan A, Schweigman C, Ruijs A, van der Vlerk M (2002) Modeling farmer's response to uncertain rainfall in Burkina Faso: A stochastic programming approach. Oper Res 50:399-414
36. Minguez R, Garcia-Bertrand R (2008) Scenario based reduction techniques for optimizing the conditional value-at-risk. INFORMS annual meeting, Washington DC, USA
37. MirHassani S, Lucas C, Mitra G, Poojari C (2000) Computational solution of a capacity planning model under uncertainty. Parallel Comput J 26:511-538
38. Móller A, Rómisch W, Weber K (2004) A new approach to O-D revenue management based on scenario trees. J Revenue Pricing Manag 3:265-276
39. Nowak M, Schultz R, Westphalen W (2002) Optimization of simultaneous power production and trading by stochastic integer programming, technical report. Stochastic programming e-print series, http://dochost.rz.hu-berlin.de/speps
40. Ntaimo L, Sen S, 2005 The million variable march for stochastic combinatorial optimization. J. Glob Optim 32:385-400
41. Nürnberg R, Römisch W (2002) A two-stage planning model for power scheduling in a hydro-thermal system under uncertainty. Optim Eng 3:355-378
42. Ogryczak W, Ruszczynski A (1999) From stochastic dominance to mean-risk models: Semi-deviations as risk measures. Eur J Oper Res 116:33-50
43. Plazas MA, Conejo AJ, Prieto FJ (2005) Multimarket optimal bidding for a power producer. IEEE Trans Power Syst 20:2041-2050
44. Prekopa A (1995) Stochastic programming. Kluwer Academic, Dordrecht
45. Rockafellar R, Uryasev S (2000) Optimization of conditional value-at-risk. J Risk 2:21-41
46. Rockafellar R, Wets RJB (1991) Scenario and policy aggregation in optimisation under uncertainty. Math Oper Res 16:119-147
47. Römisch W, Schultz R (2001) Multi-stage stochastic integer programs: An introduction. In: Grotschel M, Krumke SO, Rambau J (eds) Online optimization of large scale systems. Springer, Berlin, pp 581-600
48. Schultz R (2003) Stochastic programming with integer variables. Math Program Ser B 97:285-309
49. Schultz R, Tiedemann S (2004) Risk aversion via excess probabilities in stochastic programs with mixed-integer recourse. SIAM J Optim 14:115-138
50. Schultz R, Tiedemann S (2006) Conditional value-at-risk in stochastic programs with mixed integer recourse. Math Program Ser B 105:365-386
51. Sherali H, Zhu X (2006) On solving discrete two-stage stochastic programs having mixed-integer first- and second-stage variables. Math Program Ser B 108:597-611
52. Takriti S, Birge J (2000) Lagrangean solution techniques and bounds for loosely coupled mixed-integer stochastic programs. Oper Res 48:91-98
53. Uryasev S, Pardalos P (eds) (2001) Stochastic optimization: Algorithms and applications. Kluwer Academic, Dordrecht
54. Valente P (2002) Software tools for the investigation of stochastic programming problems. PhD thesis, Dept. of Mathematics and Computation, Brunel University, UK
55. Wallace S, Ziemba W (eds) (2005) Applications of stochastic programming. MPS-SIAM-series in optimization
56. Wets R. (1966) Programming under uncertainty: The equivalent convex program. SIAM J Appl Math 14:89-105
57. Wolsey L (1998) Integer programming. Wiley, New York
58. Ziemba W, Mulvey J (eds) (1998) Worldwide asset and liability modeling. Cambridge University Press, Cambridge