Abstract
This work considers the strategic flexibility and capacity planning under uncertain demands in production networks of automobile manufacturers. We present a deterministic and a stochastic model, which extend existing approaches, especially by an anticipation scheme for tactical workforce planning. This scheme is compared to an extended formulation of the deterministic model, which incorporates workforce planning via detailed shift models. The stochastic model is efficiently solved by an accelerated decomposition approach. The solution approach is integrated into a decision support system, which calculates minimum-cost product allocations and capacity plans. Our numerical results show that, in spite of the considerably increased complexity, our approach can efficiently handle hundreds of scenarios. Finally, we present an industrial case study.
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References
Alonso-Ayuso A, Escudero LF, Garfín A, Ortuño MT, Pérez G (2003) An approach for strategic supply chain planning under uncertainty based on stochastic 0–1 programming. J Global Optim 26(1): 97–124
Arntzen BC, Brown GG, Harrison TP, Traffton LL (1995) Global supply chain management at digital equipment corporation. Interfaces 25(1): 69–93
Askar G, Zimmermann J (2006) Optimal usage of flexibility instruments in automotive plants. Oper Res Proc, pp 479–484
Barnett L, Leaney PG, Matke J (1995) Striving for manufacturing flexibility in body construction. In: Proceedings of the IMechE AutoTech/95 Conference
Beamon BM (1998) Supply chain design and analysis: Models and methods. Int J Prod Econ 55(3): 281–294
Becker H (2005) Auf Crashkurs. Automobilindustrie im globalen Verdrängungswettbewerb. Springer, Berlin
Benders JF (1962) Partitioning prodedures for solving mixed-variables programming problems. Numer Math 4: 238–252
Birge JR, Louveaux F (1988) A multicut algorithm for two-stage stochastic linear programs. Euro J Oper Res 34(3): 384–392
Birge JR, Louveaux F (1997) Introduction to stochastic programming. Springer Series in Operations Research. Springer, New York
Boettcher J (1989) Stochastische lineare Programme mit Kompensation, Mathematical systems in economics. vol 115. Athenäum, Frankfurt am Main
Boyer KK, Leong GK (1996) Manufacturing flexibility at the plant level. Omega Int J Manage Sci 24(5): 495–510
Bruynesteyn M (2003) Flex appeal. Prudential Equity Group, LLC
Chandra C, Everson M, Grabis J (2005) Evaluation of enterprise-level benefits of manufacturing flexibility. Omega Int J Manage Sci 33(1): 17–31
Cordeau SF, Pasin F, Solomon MM (2006) An integrated model for logistics network design. Ann Oper Res 144(1): 59–82
Ferber S (2005) Strategische Kapazitäts- und Investitionsplanung in der globalen Supply Chain eines Automobilherstellers. Shaker, Indianapolis
Fleischmann B, Ferber S, Henrich P (2006) Strategic planning of BMW’s global production network. Interfaces 36: 194–208
Francas D, Kremer M, Minner S, Friese M (2007) Strategic process flexibility under lifecycle demand. Int J Prod Econ. doi:10.1016/j.ijpe.2006.12.062
Friese M, Bihlmaier R, Buerkner S (2005) Planning of flexible production networks in the automotive industry. In: International conference on changeable, agile, reconfigurable and virtual production, Muenchen, 22–23, September
Geoffrion AM, Graves GW (1974) Multicommodity distribution system design by benders decomposition. Manage Sci 20(5): 822–844
Gerwin D (1982) Do’s and don’ts of computerized manufacturing. Harvard Bus Rev 60(2): 107–116
Goetschalckx Strategic Network Planning. BT and Centre for Automotive Industry Research, Cardiff (2001)
Goetschalckx M (2002) Strategic network planning. In: Stadtler H, Kilger C (eds) Supply chain management and advanced planning. Springer, Heidelberg, pp 105–122
ILOG Inc. ILOG CPLEX 10.0 Advanced reference manual. ILOG CPLEX Division, France, 2006
Jordan WC, Graves SC (1995) Principles on the benefits of manufacturing process flexibility. Manage Sci 41(4): 577–594
Kall P, Wallace SW (1994) Stochastic programming. Wiley, Chichester
Larranaga P, Lozano JA (2001) Estimation of distribution algorithms: a new tool for evolutionary computation. Springer, Heidelberg
Meyer B (2004) Value-adding logistics for a world assembly line. Bonifatius Verlag, Paderborn
MirHassani SA, Lucas C, Mitra G, Messina E, Poojari CA (2000) Computational solution of capacity planning models under uncertainty. Parallel Comput 26(5): 511–538
Pibernik R (2001) Flexibilitätsplanung in Wertschöpfungsnetzwerken, 1 edn. Dt. Universitäts-Verlag, Wiesbaden
Roscher J (2008) Bewertung von Flexibilitätsstrategien fuer die Endmontage in der Automobilindustrie. PhD Thesis, Institut für Industrielle Fertigung und Fabrikbetrieb, Universität Stuttgart
Ruszczyński A, Shapiro A (2003) Stochastic Programming, Handbooks in Operations Research and Management Science. vol 10. Academic Press, Australia
Santoso T (2003) A comprehensive model and efficient solution algorithm for the design of global supply chains under uncertainty. PhD Thesis, Georgia Institute of Technology
Santoso T, Ahmed S, Goetschalckx M, Shapiro A (2005) A stochastic programming approach for supply chain network design under uncertainty. Euro J Oper Res 167(1): 96–115
Schneeweiss C (1999) Hierarchies in distributed decision making. Springer, Berlin
Sethi A, Sethi S (1990) Flexibility in manufacturing: a survey. Int J Flexible Manufact Syst 2(4): 289–328
Vidal CJ, Goetschalckx M (2000) Modeling the impact of uncertainties on global logistics systems. J Bus Logistics 29(1): 95–120
Wentges P (1996) Accelerating bender’s decomposition for the capacitated facility location problem. Math Methods Oper Res 44: 267–290
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Bihlmaier, R., Koberstein, A. & Obst, R. Modeling and optimizing of strategic and tactical production planning in the automotive industry under uncertainty. OR Spectrum 31, 311–336 (2009). https://doi.org/10.1007/s00291-008-0147-2
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DOI: https://doi.org/10.1007/s00291-008-0147-2