Publication: On modelling planning under uncertainty in manufacturing
Loading...
Full text at PDC
Publication Date
2007
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Institut d'Estadística de Catalunya (Idescat)
Citations
Exportar
Abstract
We present a modelling framework for two-stage and multi-stage mixed 0-1 problems under uncertainty for strategic Supply Chain Management, tactical production planning and operations assignment and scheduling. A scenario tree based scheme is used to represent the uncertainty. We present the Deterministic Equivalent Model of the stochastic mixed 0-1 programs with complete recourse that we study. The constraints are modelled by compact and splitting variable representations via scenarios.
Description
Incluye: Discussion of “On Modelling Planning under Uncertainty in Manufacturing”
UCM subjects
Unesco subjects
Keywords
Citation
Ahmed, S. (2004). Mean-risk objectives in stochastic programming. Stochastic Programming E-Print Series, http://dochost.rz.hu-berlin.de/speps.
Ahmed, S., King, A.J. and Parija, G. (2003). A multi-stage stochastic integer programming approach for capacity expansion under uncertainty. Journal of Global Optimization, 26, 3-24.
Alonso-Ayuso, A., Escudero, L.F., Garín, A., Ortuño, M.T. and P´erez, G. (2003). An approach for strategic supply chain planning based on stochastic 0−1 programming. Journal of Global Optimization, 26, 97-124.
Alonso-Ayuso, A., Escudero, L.F., Garín, A., Ortuño, M.T. and P´erez, G. (2005). On the product selection and plant dimensioning problem under uncertainty. Omega, 33, 307-318.
Alonso-Ayuso, A., Escudero, L.F. and Ortuño, M.T. (2003). 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, 503-519.
Alonso-Ayuso, A., Escudero, L.F. and Ortuño, M.T. (2005). Modelling production planning and scheduling under uncertainty. In S.W. Wallace and W.T. Ziemba, editors, Applications of Stochastic Programming. MPS-SIAM-Series in Optimization, 217-252.
Alonso-Ayuso, A., Escudero, L.F., Ortuño, M.T. and Pizarro, C. (2007). On a Stochastic Sequencing and Scheduling Problem. Computers & Operations Research, 34, 2604-2624.
Alonso-Ayuso, A., Escudero, L.F., Pizarrro, C., Romeijn, H.E. and Romero Morales, D. (2006). On solving the multi-period single-sourcing problem under uncertainty. Computational Management Science, 3, 29-53.
Alonso-Ayuso, A., Escudero, L.F. and Pizarrro, C. (2007). On SIP algorithms for minimizing the mean-risk function in the Multi Period Single Source Problem under uncertainty. In the refereeing process.
Andrade, R., Lisser, A., Maculan, N. and Plateau, G. (2006). Enhancing a Branch-and-Bound algorithm for two-stage stochastic integer network design-based models. Management Science, 52, 1450-1455.
Anthony, R.N. (1965). Planning and control systems: A framework for analysis. Technical report, Harvard University, Graduate School of Business Administration, Cambridge, Ma, USA.
Baptiste, Ph., Le Pape, C. and Nuijten, W. (2001). Constraint-Based Scheduling. Kluwer Academic Publishers.
Baricelli, P., Lucas, C. and Mitra, G. (1996). A model for strategic planning under uncertainty. TOP, 4, 361-384.
Beale, E.M.L. and Forrest, J.J.H. (1976). Global optimization using special ordered sets. Mathematical Programming, 10, 52-69.
Beale, E.M.L. and Tomlin, J.A. (1970). Special facilities in a general mathematical programming system for nonconvex problems using ordered sets of variables. In J. Lawrence, editor. Operations Research’69. Tavistock Publishing, 447-454.
Belvaux, G. and Wolsey, L.A. (2001). Modelling practical lot sizing problems as mixed-integer programs. Management Science, 47, 993-1007.
Benders, J.F. (1962). Partitioning procedures for solving mixed variables programming problems. Numerische Mathematik, 4, 238-252.
Bertsimas, D. and Stock-Patterson, S. (1998). The air traffic flow management problem with enroute capacities. Operations Research, 46, 406-422.
Birge, J.R. (1985). Decomposition and partitioning methods for multi-stage stochastic linear programs. Operations Research, 33, 1089-1107.
Birge, J.R. and Louveaux, F.V. (1997). Introduction to Stochastic Programming. Springer.
Bitran, G.R. and Tirupati, D. (1993). Hierarchical production planning. In A.H.G. Rinnooy-Kan S.C. Graves and P.H. Zipkin, editors. Logistics of Production and Inventory, 523-568. North-Holland.
Carøe, C.C. and Schultz, R. (1999). Dual decomposition in stochastic integer programming. Operations Research Letters, 24, 37-45.
Carøe, C.C. and Tind, J. (1998). L-shaped decomposition of two-stage stochastic programs with integer recourse. Mathematical Programming, 83, 451-464.
Cheung, R.K.M. and Powell, W.B. (1996). Models and algorithms for distribution problems with uncertain demands. Transportation Science, 39, 43-59.
Cohen, M.A. and Lee, H.L. (1989). Resource deployment analysis of global manufacturing and distribution networks. Journal of Manufacturing and Operations Management, 2, 81-104.
Cristobal, M.P., Escudero, L.F. and Monge, J.F. (2007). On Stochastic Dynamic Programming for solving large-scale tactical production planning problems. In the refereeing process.
Dert, C.K. (1998). A dynamic model for asset liability management for defined benefit pension funds. InW.T. Ziemba and J.Mulvey, editors.Worldwide Asset and Liability Modelling, 501-536. Cambridge University Press.
Dillenberger, Ch., Escudero, L.F. Wollensak, A. and Zhang, W. (1994). On practical resource allocation for production planning and scheduling with period overlapping setups. European Journal of Operational Research, 75, 275-286.
Eppen, G.D. Martin, R.K. and Schrage, L. (1989). A scenario approach to capacity planning. Operations Research, 37, 517-527.
Escudero, L.F. (1982). On maintenance scheduling of production units. European Journal of Operational Research, 9, 264-274.
Escudero, L.F. (1988). S3 sets. An extension of the Beale-Tomlin special ordered sets. Mathematical Programming, 42, 113-123.
Escudero, L.F. (1994). CMIT: A capacitated multi-level implosion tool for production planning. European Journal of Operational Research, 76, 511-528.
Escudero, L.F., de la Fuente, J.L., Garcia, C. and Prieto, F.J. (1999). A parallel computation approach for solving multi-stage stochastic network problems. Annals of Operations Research, 90, 131-160.
Escudero, L.F., Kamesam, P.V., King, A. and Wets, R.J.-B (1993). Production planning via scenario modelling. Annals of Operations Research, 43, 311-335.
Escudero, L.F., Galindo, E., G´omez, E., Garc´ıa, G. and Sabau, V. (1999). SCHUMANN, a modelling framework for supply chain management under uncertainty. European Journal of Operational Research, 119, 13-34.
Escudero, L.F., Garín, A., Merino, M. and Pérez. G. (to appear 2007). On multistage Stochastic Integer Programming for incorporating logical constraints in asset and liability management under uncertainty. Computational Management Science.
Escudero, L.F., Garín, A., Merino, M. and Pérez, G. (2007). On structuring Mortgage-Backed Securities porfolios under uncertainty. Annals of Operations Research, 152, 395-420.
Escudero, L.F., Quintana, F.J. and Salmer´on, J. (1999). CORO, a modelling and an algorithmic framework for oil supply, transformation and distribution optimization under uncertainty. European Journal of Operational Research, 114, 638-656.
Escudero, L.F. and Salmerón, J. (2005). Fix-and-Relax Partitioning. An algorithmic framework for large-scale resource constrained project selection and scheduling. Annals of Operations Research, 140, 163-188.
Gassmann, H.I. (1990). MSLIP: A computer code for the multi-stage linear programming problem. Mathematical Programming, 47, 407-423.
Graves, S.C., Rinnooy Kan, A.H.G. and Zipkin, E. (eds.) (1993). Logistics of Production and Inventory, North-Holland.
Groewe-Kuska, N., Kiwiel, K., Nowak, M.P., Römisch,W. and Wegner, I. (2001). Power management in a hydro-thermal system under uncertainty by Lagrangeanrelaxation, In C. Greengard and A. Ruszczynski, editors. Decision making under uncertainty: Energy and Power, 39-70.
Hahn, C.K., Duplaga, E.A. and Hartley, J.L. (2000). Supply-chain synchronization: Lessons from hyundai motor company. Interfaces, 30, 32-45.
Hartmann, S. (1999). Project Scheduling under Limited Resources. Models, Methods and Applications. Springer.
Hemmecke, R. and Schultz, R. (2001). Decomposition methods for two-stage stochastic integer programs. In M. Grötschel, S.O. Krumke and J. Rambau, editors. Online Optimization of Large Scale Systems, 601-622. Springer.
Higle, J.L. and Sen, S. (1996). Stochastic Decomposition. A Statistical Method for Large-Scale Stochastic Linear Programming. Kluwer Academic Publishers.
Huang, K. and Ahmed, S. (2005). The value of multi-stage stochastic programming in capacity planning under uncertainty. Stochastic Programming E-Print Series, http://dochost.rz.huberlin.de/speps.
Kall, P. and Wallace, S.W. (1994). Stochastic Programming, John Wiley.
Karmarkar, U.S. (1989). Capacity loading and release planning with work in progress and lead times. Journal of Manufacturing and Operations Management, 2, 105-123.
Klein Haneveld, W.K. and Vlerk, M.H. van der (1999). Stochastic integer programming: General models and algorithms. Annals of Operations Research, 85, 39-57.
Klein Haneveld, W.K. and van der Vlerk, M.H. (2001). Optimizing electricity distribution using integer recourse models. In S. Uryasev and P.M. Pardalos, editors. Stochastic Optimization:Algorithms and Applications, 137-154. Kluwer Academic Publishers.
de Kok, A.G. and Graves, S.C. (eds.) (2003). Supply Chain Management: Design, Coordination and Operation. North-Holland.
Laporte, G. and Louveaux, F.V. (1993). The integer L-shaped method for stochastic integer programs with complete recourse. Operations Research Letters, 13, 133-142.
Li, X. and Wang, Q. (2007). Coordination mechanisms of supply chain systems. European Journal of Operational Research, 179, 1-16.
Lucas, C., Mirhassani, S.A., Mitra, G. and Poojari, C.A. (2001). An application of lagrangian relaxation to a capacity planning problem under uncertainty. Journal of the Operational Research Society, 52, 1256-1266.
Lulli, G. and Sen, S. (2002). Stochastic batch-sizing problems: models and algorithms. In D.F. Woodruff, editor, Stochastic Integer Programming and Network Interdiction Models. Kluwer Academic Press.
Lulli, G. and Sen, S. (2004). A branch-and-price algorithm for multi-stage stochastic integer programming with application to stochastic batch-sizing problems. Management Science, 50, 786-796.
Lulli, G. and Sen, S. (2006). A heuristic procedure for stochastic integer progrmas with complete recourse. European Journal of Operational Research, 171, 879-890.
Maatan, A., Schweigman, C., Ruijs, A. and van der Vlerk, M.H. (2002). Modelling farmers’ response to uncertain rainfall in burkina faso: A stochastic programming approach. Operations Research, 50, 399-414.
Miller, A.J. Nemhauser, G.L. and Savelsbergh, M.W.P. (2000). On capacitated lot-sizing and continuous 0−1 knapsack polyhedra. European Journal of Operational Research, 125, 298-315.
MirHassani, S.A., Lucas, C., Mitra, G. and Poojari, C.A. (2000). Computational solution of capacity planning model under uncertainty. Parallel Computing Journal, 26, 511-538.
Mitra, G., Hajian, M. and Hai, I. (1977). A distributed processing algorithm for solving integer programs using a cluster of workstations. Parallel Computing Journal, 23, 733-753.
Ntaimo, L. and Sen, S. (2005). The million variable ’march’ for stochastic combinatorial optimization. Journal of Global Optimization, 32, 385-400.
Novak, M.P., Shultz, R. and Westphalen, M. (2002). Optimization of simultaneous power production and trading by stochastic integer programming. Technical report, Stochastic Programming E-Print Series.
Nürnberg, R. and R¨omisch, W. (2002). A two-stage planning model for power scheduling in a hydrothermal system under uncertainty. Optimization and Engineering, 3, 355-378, 2002.
Ogryczak, W. and Ruszczynski, A. (1999). From stochastic dominance to mean-risk models: semideviations as risk measures. European Journal of Operational Research, 116, 33-50.
Parija, G., Ahmed, S. and King, A.J. (2004). On bridging the gap between stochastic integer programming and MIP solver strategies. INFORMS Journal on Computing, 16, 73-83.
Pinedo, M. (1995). Scheduling Theory, Algorithms and Systems. Prentice-Hall.
Pochet, Y. and Wolsey, L.A. (1991). Solving multi-item lot sizing problems using strong cutting planes. Management Science, 37, 53-67.
Rockafellar, R.T. and Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2, 21-41.
Rockafellar, R.T. and Wets, R.J-B. (1991). Scenario and policy aggregation in optimisation under uncertainty. Mathematics of Operations Research, 16, 119-147.
Römisch, W. and Schultz, R. (2001). Multi-stage stochastic integer programs: An introduction. In M. Grötschel, S.O. Krumke and J. Rambau, editors. Online Optimization of Large Scale Systems, 581-600. Springer.
Santoso, T., Ahmed, S., Goetschalckx, M. and Shapiro, A. (2005). A stochastic programming approach for supply network design under uncertainty. European Journal of Operational Research, 167, 96-115.
Schultz, R. (2003). Stochastic programming with integer variables. Mathematical Programming, Ser. B 97, 285-309.
Schultz, R., Nowak, M. and Westphalen, M. (2005). A stochastic integer Programming model for incorporating day-ahead trading of electricity into hydro-thermal unit commitment. Optimization and Engineering, 6, 163-176.
Schultz, R. and Tiedemann, S. (2004). Risk aversion via excess probabilities in stochastic programs with mixed-integer recourse. SIAM Journal on Optimization, 14, 115-138.
Schultz, R. and Tiedemann, S. (2006). Conditional Value-at-Risk in stochastic programs with mixed integer recourse. Mathematical Programming, Ser. B 105, 365-386.
Sen, S. and Sherali, H.D. (2006). Decomposition with branch-and-cut approaches for two-stage stochastic mixed-integer programming. Mathematical Programming, Ser. A 106, 203-223.
Sen, S. and Higle, J.L. (2005). The C3 theorem and a D2 algorithm for large scale stochastic mixedinteger programming: Set convexification. Mathematical programing: Ser. A 104, 1-20.
Sherali, H.D. and Zhu, X. (2006). On solving discrete two stage stochastic programs having mixedinteger first and second stage variables. Mathematical Programming, Ser. A 108, 597-611.
Shapiro, J.F. (1993). Mathematical programming models and methods for production planning and scheduling. In S.C. Graves, A.H.G. Rinnooy Kan and E. Zipkin, editors. Logistics of Production and Inventory, 371-443. North-Holland.
Shapiro, J.F. (2001). Modelling the Supply Chain. Duxbury.
Sousa, J. and Wolsey, L.A. (1992). A time indexed formulation of non-preemtive single machine scheduling problems. European Journal of Operational Research, 54, 353-357.
Takriti, S. and Birge, J.R. (2000). Lagrangean solution techniques and bounds for loosely coupled mixed-integer stochastic programs. Operations Research, 48, 91-98.
Tomasgard, A. and Høeg, E. (2005). A supply chain management model with stochastic demand. In S.W. Wallace and W.T. Ziemba, editors, Applications of Stochastic Programming, 253–276. MPSSIAM-Series in Optimization.
Tsiakis, P., Shah, N. and Pantelides, C.C. (2001). Design of a multiechelon supply chain network under demand uncertainty. Industrial and Engineering Chemistry Research, 40, 3585-3604.
Uryasev, S. and Pardalos, P.M. (eds.) (2001). Stochastic Optimization: Algorithms and Applications. Kluwer Academic Publishers.
Valente, P. (2002). Software tools for the investigation of stochastic programming problems. PhD thesis, Dept. of Mathematics and Computation, Brunel University, UK.
van der Vlerk, M.H. (2003). Integrated chance constraints in an ALM model for pension funds. Stochastic Programming E-Print Series, http://dochost.rz.hu-berlin.de/speps.
Wagner, H.M. and Beman, O. (1995). Models for planning capacity expansion of convenience store under uncertain demand and the value of information. Annals of Operations Reserarch, 44, 19-44.
Wagner, H.M. and Within, T.M. (1958). A dynamic version of the economic lot size model. Management Science, 5, 89-96.
Wallace, S.W. and Ziemba, W.T. (eds.) (2005). Applications of Stochastic Programming. MPS-SIAM Series in Optimization.
Wolsey, L.A. (1990). Valid inequalities for mixed integer programs with generalized and variable upper bounds. Discrete Applied Mathematics, 25, 251-261.
Wolsey, L.A. (1997). MIP modelling of changeovers in production planning and scheduling problems. European Journal of Operational Research, 99, 154-165.
Zipkin, P.H. (1986). Models for design and control of stochastic multi-item batch production systems. Operations Research, 34.