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BFC, A branch-and-fix coordination algorithmic framework for solving some types of stochastic pure and mixed 0-1 programs

dc.contributor.authorOrtuño Sánchez, María Teresa
dc.contributor.authorAlonso Ayuso, Antonio
dc.contributor.authorEscudero Bueno, Laureano Fernando
dc.date.accessioned2023-06-20T09:43:14Z
dc.date.available2023-06-20T09:43:14Z
dc.date.issued2003-12-16
dc.description.abstractWe present a framework for solving some types of 0-1 multi-stage scheduling/planning problems under uncertainty in the objective function coefficients and the right-hand-side. A scenario analysis scheme with full recourse is used. The solution offered for each scenario group at each stage takes into account all scenarios but without subordinating to any of them. The constraints are modelled by a splitting variables representation via scenarios. So, a 0-1 model for each scenario is considered plus the non-anticipativity constraints that equate the 0-1 variables from the same group of scenarios in each stage. The mathematical representation of the model is very amenable for the proposed framework to deal with the 0-1 character of the variables. A branch-and-fix coordination approach is introduced for coordinating the selection of the branching nodes and branching variables in the scenario subproblems to be jointly optimized. Some computational experience is reported for different types of problems.en
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.facultyInstituto de Matemática Interdisciplinar (IMI)
dc.description.refereedTRUE
dc.description.sponsorshipComisión Interministerial de Ciencia y Tecnología (España)
dc.description.sponsorshipComunidad de Madrid
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/17501
dc.identifier.citationAlonso-Ayuso, Antonio, Laureano F. Escudero, y M. Teresa Ortuño. «BFC, A Branch-and-Fix Coordination Algorithmic Framework for Solving Some Types of Stochastic Pure and Mixed 0–1 Programs». European Journal of Operational Research 151, n.o 3 (diciembre de 2003): 503-19. https://doi.org/10.1016/S0377-2217(02)00628-8.
dc.identifier.doi10.1016/S0377-2217(02)00628-8
dc.identifier.issn0377-2217
dc.identifier.officialurlhttps//doi.org/10.1016/S0377-2217(02)00628-8
dc.identifier.relatedurlhttp://www.sciencedirect.com/science/article/pii/S0377221702006288
dc.identifier.urihttps://hdl.handle.net/20.500.14352/50244
dc.issue.number3
dc.journal.titleEuropean journal of operational research
dc.language.isoeng
dc.page.final519
dc.page.initial503
dc.publisherElsevier Science
dc.relation.projectIDTIC2000-1750-C06-04/05
dc.relation.projectID07T/0005/2001
dc.rights.accessRightsrestricted access
dc.subject.cdu519.8
dc.subject.keywordStochastic programming
dc.subject.keywordMultistage scenario tree
dc.subject.keywordMixed 0-1 programs
dc.subject.keywordSplitting variables representation
dc.subject.keywordTwin node families
dc.subject.keywordFlow management problem
dc.subject.keywordInteger recourse
dc.subject.keywordDecomposition
dc.subject.ucmInvestigación operativa (Matemáticas)
dc.subject.unesco1207 Investigación Operativa
dc.titleBFC, A branch-and-fix coordination algorithmic framework for solving some types of stochastic pure and mixed 0-1 programsen
dc.typejournal article
dc.volume.number151
dcterms.referencesAhmed, S., King, A.J., Parija, G., 2000. A multi-stage stochastic integer programming approach for capacity expansion under uncertainty. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA. Alonso-Ayuso, A., 1997. Optimización combinatoria estocástica (aplicada al control del tráfico aéreo), Ph.D. Thesis, Universidad Complutense de Madrid, Madrid. Alonso-Ayuso, A., Escudero, L.F., Ortuño, M.T., 2000. A stochastic 0–1 program based approach for air traffic management. European Journal of Operational Research 120, 47–62. Alonso-Ayuso, A., Escudero, L.F., Garín, A., Ortuño, M.T., Pérez, G., 2002. An approach for strategic supply chain planning under uncertainty based on stochastic 0–1 programming. Journal of Global Optimization. Applegate, D., Bixby, R.E., Chvatal, V., Cock, W.J., 1997. A BCC TSP, 16th International Symposium on Mathematical Programming, Lausanne, Switzerland. Atamtürk, A., Nemhauser, G.L, Savelsbergh, M.W.P., 2000. Conflict graphs in solving integer programming problems. European Journal of Operational Research 121, 40–55. Beale, E.M.L., 1955. On minimizing a convex function subject to linear inequalities. Journal of Royal Statistical Society 17b, 173–184. Benders, J.F., 1962. Partitioning procedures for solving mixed variables programming problems. Numeische Mathematik 4, 238–252. Bertsimas, D.J., Stock, S., 1998. Air traffic flow management problem with enroute capacities. Operations Research 46, 406–422 (Made available as a MIT report in 1994). Birge, J., Louveaux, F.V., 1997. Introduction to Stochastic Programming. Springer, Berlin. Caroe, C.C., Schultz, R., 1998. A two-stage stochastic program for unit commitment under uncertainty in a hydro power systems, SC 98-13, Konrad-Zuse-Zentrum f€ur Informationstechnik Berlin (ZIB), Berlin. Caroe, C.C., Schultz, R., 1999. Dual decomposition in stochastic integer programming. Operational Research Letters 24, 37–45 (Made available as a ZIB report in 1996). Caroe, C.C., Tind, J., 1998. L-shaped decomposition of twostage stochastic programs with integer recource. Mathematical Programming 83, 451–464. Dantzig, G.B., 1955. Linear programming under uncertainty. Management Science 1, 197–206. Escudero, L.F., de la Fuente, J.L., Garc_ıa, C., Prieto, F.J., 1999a. A parallel computation approach for solving multistage stochastic network problems. Annals of Operations Research 90, 131–160. Escudero, L.F., Galindo, E., G_omez, E., Garc_ıa, G., Sabau, V., 1999b. SCHUMANN, a modelling framework for supply chain management under uncertainty. European Journal of Operational Research 119, 13–34. Guignard, M., Spielberg, K., 1981. Logical reduction methods in zero-one programming (minimal preferred variables). Operations Research 29, 49–74. Higle, J.L., Sen, S., 1996. Stochastic decomposition. A statistical method for large-scale stochastic linear programming. Kluwer Academic Publishers, Boston. Hemmecke, R., Schultz, R., 2001. Decomposition methods for two-stage stochastic integer programs. In: Grötschel, M., Krumke, S.O., Rambau, J. (Eds.), Online Optimization of Large Scale Systems. Springer, Berlin, pp. 601–622. Johnson, E.L., Nemhauser, G.L., Savelsbergh, M.W.P., 2000. Progress in linear programming-based algorithms for integer programming: An exposition. INFORMS Journal of Computing 12, 2–23. Kall, P., Wallace, S.W., 1994. Stochastic Programming. J. Wiley & Sons, New York. Klein Haneveld, W.K., Van der Vlerk, M.H., 1999. Stochastic integer programming: General models and algorithms. Annals of Operations Research 85, 39–57. Laporte, G., Louveaux, F.V., 1993. The integer L-shaped method for stochastic integer programs with complete recourse. Operations Research Letters 13, 133–142. Linderoth, J., Savelsbergh, M.W.P., 1999. Search strategies for mixed integer programming. INFORMS Journal of Computing 11, 173–187. Lokketangen, A., Woodruff, D.L., 1996. Progressive hedging and tabu search applied to mixed integer (0,1) multi-stage stochastic programming. Journal of Heuristics 2, 111–128. Mulvey, J.M., Vanderbei, R.J., Zenios, S.A., 1995. Robust optimisation of large-scale systems: General modeling framework and computations. Operations Research 43, 244–281. Rockafellar, R.T., Wets, R.J.-B., 1991. Scenario and policy aggregation in optimisation under uncertainty. Mathematics of Operations Research 16, 119–147. Römisch, W., Schultz, R., 2001. Multi-stage stochastic integer programs: An introduction, In: Grötschel, M.,Krumke, S.O., Rambau, J., (Eds.), Online Optimization of Large Scale Systems. Springer, Berlin, pp. 581–600 Savelsbergh, M.W.P., 1994. Preprocessing and probing techniques for mixed integer programming models. ORSA Journal of Computing 6, 445. Schultz, R., Stougie, L., Van der Vlerk, M.H., 1998. Solving stochastic programs with integer recourse by enumeration: A framework using Gr€obner basis reductions. Mathematical Programming 83, 229–252. Stougie, L., Van der Vlerk, M.H., 1997. Stochastic Integer Programming. In: Dell_Amico, M., Maffioli, F., Martello, S. (Eds.), Annotated Bibliography in Combinatorial Optimization. John Wiley, NY, pp. 127–141. Takriti, S., Birge, J.R., 2000. Lagrangean solution techniques and bounds for loosely coupled mixed-integer stochastic programs. Operations Research 48, 91–98. Van der Vlerk, M.H., 1995. Stochastic Programming with Integer Recourse, Ph.D. Thesis, Institute of Economics, University of Groningen, The Netherlands. Van Slyke, R., Wets, R.J.-B., 1969. L-shaped linear programs with applications to optimal control and stochastic programming. SIAM Journal on Applied Mathematics 17, 638– 663. Wets, R.J.-B., 1989. The aggregation principle in scenario analysis and stochastic optimisation. In: Wallace, S.W. (Ed.), Algorithms and model formulations in Mathematical Programming. Springer, Berlin, pp. 92–113.
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