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Model-based production cost estimation to support bid processes: an automotive case study. (English) Zbl 07252390
Summary: In the automobile supplier industry companies frequently need to make bids, typically based on cost estimates for the production process, to obtain incoming orders. The production process is executed in several main stages, which are linked by intra-plant logistics. To model different scenarios, we consider two separate organizational approaches towards cost estimation. In the first one, all the main stages are optimized via a central authority. The second approach models a decentralized decision making process, as it is currently used in practice. Moreover, we analyze different coordination mechanisms to improve the decentralized approach. To capture the uncertainty during the bid process, associated with key parameters like demand, capacity consumption and cost, we formulate a stochastic version of the model, capturing different risk preferences to compare risk-neutral and risk-averse decision making. The resulting MILPs are solved with CPLEX and results for an illustrative example based on a real data set are presented.
90B Operations research and management science
Full Text: DOI
[1] Aghezzaf, EH; Sitompul, C.; Najid, NM, Models for robust tactical planning in multi-stage production systems with uncertain demands, Comput Oper Res, 37, 5, 880-889 (2010) · Zbl 1177.90120
[2] Ahmed S, Shapiro A (2002) The sample average approximation method for stochastic programs with integer recourse. Technical report, Georgia Institute of Technology
[3] Albers, S.; Krafft, M., Regeln zur Bestimmung des fast-optimalen Angebotsaufwands, Z Betr, 70, 10, 1083-1107 (2000)
[4] Aouam, T.; Brahimi, N., Integrated production planning and order acceptance under uncertainty: a robust optimization approach, Eur J Oper Res, 228, 3, 504-515 (2013) · Zbl 1317.90099
[5] Artzner, P.; Delbaen, F.; Eber, JM; Heath, D., Coherent measures of risk, Math Finance, 9, 203-228 (1999) · Zbl 0980.91042
[6] Borenich A, Greistorfer P, Reimann M, Schafler M, Unzeitig W (2014) Unterstützung des Angebotsprozesses eines Automobilzulieferers durch ein Produktionsmodell mit mehreren Gewerken. In: AKS Zsifkovits HE (ed) Logistische Modellierung. Rainer Hampp Verlag
[7] da Silva, C.; Figueira, J.; Lisboa, J.; Barman, S., An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming, Omega, 34, 2, 167-177 (2006)
[8] Fernandes, R.; Gouveia, B.; Pinho, C., A real options approach to labour shifts planning under different service level targets, Eur J Oper Res, 231, 1, 182-189 (2013) · Zbl 1317.90158
[9] Gansterer, M., Aggregate planning and forecasting in make-to-order production systems, Int J Prod Econ, 170, 521-528 (2015)
[10] Gebhard M, Kuhn P (2009) Hierarchische Produktionsplanung bei Unsicherheit, chap. 5, pp. 67-89. Produktion und Logistik. Gabler Verlag
[11] Hanssmann, F.; Hess, S., A linear programming approach to production and employment scheduling, Manag Sci, MT-1, 1, 46-51 (1960)
[12] Heigl KM, Rennhak C (2008) Zukünftige Wettbewerbsstrategien für Automobilzulieferer. Chancen und Risiken der dritten Revolution in der Automobilindustrie. ibidem-Verlag, Stuttgart, Deutschland
[13] Holt, C.; Modigliani, F.; Simon, H., A linear decision rule for production and employment scheduling, Manag Sci, 2, 1, 1-30 (1955)
[14] Holt, C.; Modigliani, F.; Muth, J., Derivation of a linear decision rule for production and employment, Manag Sci, 2, 2, 159-177 (1956) · Zbl 0995.90548
[15] Lalmazloumian, M.; Wong, KY; Govindan, K.; Kannan, D., A robust optimization model for agile and build-to-order supply chain planning under uncertainties, Ann Oper Res, 240, 2, 435-470 (2016) · Zbl 1357.90097
[16] Meyr, H., Supply chain planning in the German automotive industry, OR Spectr, 26, 4, 447-470 (2004) · Zbl 1069.90033
[17] Mula, J.; Poler, R.; García-Sabater, GS; Lario, FC, Models for production planning under uncertainty: a review, Int J Prod Econ, 103, 1, 271-285 (2006)
[18] Nam, S.; Logendran, R., Aggregate production planning: a survey of models and methodologies, Eur J Oper Res, 61, 3, 255-272 (1992)
[19] Nam, S.; Logendran, R., Modified production switching heuristics for aggregate production planning, Comput Oper Res, 22, 5, 531-541 (1995) · Zbl 0833.90063
[20] Oliff, M.; Lewis, H.; Markland, R., Aggregate planning in crew-loaded production environments, Comput Oper Res, 16, 1, 13-25 (1989)
[21] Peidro, D.; Mula, J.; Poler, R.; Lario, FC, Quantitative models for supply chain planning under uncertainty: a review, Int J Adv Manuf Technol, 43, 3-4, 400-420 (2009)
[22] Rockafellar, RT; Uryasev, S., Optimization of conditional value-at-risk, J Risk, 2, 3, 21-41 (2000)
[23] Rockafellar, RT; Uryasev, S., Conditional value-at-risk for general loss distributions, J Bank Finance, 26, 1443-1471 (2002)
[24] Schneeweiß, C., Distributed decision making (2003), Berlin: Springer, Berlin · Zbl 1079.90074
[25] Schultz, R., Stochastic programming with integer variables, Math Program, 97, 285-309 (2003) · Zbl 1035.90053
[26] Sillekens T (2008) Aggregierte Produktionsplanung in der Automobilindustrie unter besonderer Berücksichtigung von Personalflexibilität. Ph.D. thesis, Universitat Paderborn, Fakultat fur Wirtschaftswissenschaften, Wirtschaftsinformatik
[27] Sillekens, T.; Koberstein, A.; Suhl, L., Aggregate production planning in the automotive industry with special consideration of workforce flexibility, Int J Prod Res, 49, 17, 5055-5078 (2011) · Zbl 1356.90078
[28] Volling, T.; Spengler, T., Modeling and simulation of order-driven planning policies in build-to-order automobile production, Int J Prod Econ, 131, 1, 183-193 (2011)
[29] Wang, RC; Liang, TF, Applying possibilistic linear programming to aggregate production planning, Int J Prod Econ, 98, 3, 328-341 (2005)
[30] Wyman O (2012) FAST 2025—Massiver Wandel in der automobilen Wertschöpfungsstruktur https://docplayer.org/1744306-Fast-2025-massiver-wandel-in-derautomobilen-wertschoepfungsstruktur.html. Accessed 21 Jan 2019
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