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Master production schedule using robust optimization approaches in an automobile second-tier supplier

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Master production schedule using robust optimization approaches in an automobile second-tier supplier

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Martín, AG.; Díaz-Madroñero Boluda, FM.; Mula, J. (2020). Master production schedule using robust optimization approaches in an automobile second-tier supplier. Central European Journal of Operations Research. 28(1):143-166. https://doi.org/10.1007/s10100-019-00607-2

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Título: Master production schedule using robust optimization approaches in an automobile second-tier supplier
Autor: Martín, Antonio G. Díaz-Madroñero Boluda, Francisco Manuel Mula, Josefa
Entidad UPV: Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses
Fecha difusión:
Resumen:
[EN] This paper considers a real-world automobile second-tier supplier that manufactures decorative surface finishings of injected parts provided by several suppliers, and which devises its master production schedule by a ...[+]
Palabras clave: Robust optimization , Master production schedule , Uncertainty , Automotive industry
Derechos de uso: Reserva de todos los derechos
Fuente:
Central European Journal of Operations Research. (issn: 1435-246X )
DOI: 10.1007/s10100-019-00607-2
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s10100-019-00607-2
Código del Proyecto:
info:eu-repo/grantAgreement/EC/H2020/636909/EU/Cloud Collaborative Manufacturing Networks (C2NET)/
Agradecimientos:
Funding was provided by Horizon 2020 Framework Programme (Grant Agreement No. 636909) in the frame of the "Cloud Collaborative Manufacturing Networks" (C2NET) project.
Tipo: Artículo

References

Alem DJ, Morabito R (2012) Production planning in furniture settings via robust optimization. Comput Oper Res 39:139–150. https://doi.org/10.1016/j.cor.2011.02.022

Aloulou MA, Dolgui A, Kovalyov MY (2014) A bibliography of non-deterministic lot-sizing models. Int J Prod Res 52:2293–2310. https://doi.org/10.1080/00207543.2013.855336

As’ad R, Demirli K, Goyal SK (2015) Coping with uncertainties in production planning through fuzzy mathematical programming: application to steel rolling industry. Int J Oper Res 22:1–30. https://doi.org/10.1504/IJOR.2015.065937 [+]
Alem DJ, Morabito R (2012) Production planning in furniture settings via robust optimization. Comput Oper Res 39:139–150. https://doi.org/10.1016/j.cor.2011.02.022

Aloulou MA, Dolgui A, Kovalyov MY (2014) A bibliography of non-deterministic lot-sizing models. Int J Prod Res 52:2293–2310. https://doi.org/10.1080/00207543.2013.855336

As’ad R, Demirli K, Goyal SK (2015) Coping with uncertainties in production planning through fuzzy mathematical programming: application to steel rolling industry. Int J Oper Res 22:1–30. https://doi.org/10.1504/IJOR.2015.065937

Atamturk A, Zhang M (2007) Two-stage robust network flow and design under demand uncertainty. Oper Res 55:662–673. https://doi.org/10.1287/opre.1070.0428

Aytac B, Wu SD (2013) Characterization of demand for short life-cycle technology products. Ann Oper Res 203:255–277. https://doi.org/10.1007/s10479-010-0771-5

Ben-Tal A, Nemirovski A (1998) Robust convex optimization. Math Oper Res 23:769–805. https://doi.org/10.1287/moor.23.4.769

Ben-Tal A, Nemirovski A (2000) Robust solutions of linear programming problems contaminated with uncertain data. Math Program 88:411–424. https://doi.org/10.1007/PL00011380

Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52:35–53. https://doi.org/10.1287/opre.1030.0065

Caulkins JJ, Morrison E, Weidemann T (2007) Spreadsheet errors and decision making: evidence from field interviews. J Organ End User Comput 19:1–23

Childerhouse P, Towill DR (2002) Analysis of the factors affecting real-world value stream performance. Int J Prod Res 40:3499–3518. https://doi.org/10.1080/00207540210152885

Chu SCK (1995) A mathematical programming approach towards optimized master production scheduling. Int J Prod Econ 38:269–279. https://doi.org/10.1016/0925-5273(95)00015-G

Conlon JR (1976) Is your master production schedule feasible? Prod Invent Manag 17:56–63

De La Vega J, Munari P, Morabito R (2017) Robust optimization for the vehicle routing problem with multiple deliverymen. Cent Eur J Oper Res. https://doi.org/10.1007/s10100-017-0511-x

Díaz-Madroñero M, Mula J, Jiménez M (2014a) Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions. Int J Prod Res 52:6971–6988. https://doi.org/10.1080/00207543.2014.920115

Díaz-Madroñero M, Mula J, Peidro D (2014b) A review of discrete-time optimization models for tactical production planning. Int J Prod Res 52:5171–5205. https://doi.org/10.1080/00207543.2014.899721

Díaz-Madroñero M, Peidro D, Mula J (2014c) A fuzzy optimization approach for procurement transport operational planning in an automobile supply chain. Appl Math Model 38:5705–5725. https://doi.org/10.1016/j.apm.2014.04.053

Dolgui A, Ben Ammar O, Hnaien F et al (2013) Supply planning and inventory control under lead time uncertainty: a review. Stud Inform Control 22:255–268

Dzuranin AC, Slater RD (2014) Business risks all identified? If you’re using a spreadsheet, think again. J Corp Account Finance 25:25–30. https://doi.org/10.1002/jcaf.21936

Englberger J, Herrmann F, Manitz M (2016) Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment. Int J Prod Res 54:6192–6215. https://doi.org/10.1080/00207543.2016.1162917

Gabrel V, Murat C, Thiele A (2014) Recent advances in robust optimization: an overview. Eur J Oper Res 235:471–483

Gharakhani M, Taghipour T, Farahani KJ (2010) A robust multi-objective production planning. Int J Ind Eng Comput 1:73–78. https://doi.org/10.5267/j.ijiec.2010.01.007

González JJ, Reeves GR (1983) Master production scheduling: a multiple-objective linear programming approach. Int J Prod Res 21:553–562. https://doi.org/10.1080/00207548308942390

Gorissen BL, Yanıkoğlu İ, den Hertog D (2015) A practical guide to robust optimization. Omega 53:124–137. https://doi.org/10.1016/j.omega.2014.12.006

Grubbstrom RW, Tang O (2000) An overview of input-output analysis applied to production-inventory systems. Econ Syst Res 12:3–25. https://doi.org/10.1080/095353100111254

Grubbström RW, Bogataj M, Bogataj L (2010) Optimal lotsizing within MRP theory. Annu Rev Control 34:89–100. https://doi.org/10.1016/J.ARCONTROL.2010.02.004

Haojie Y, Lixin M, Canrong Z (2017) Capacitated lot-sizing problem with one-way substitution: a robust optimization approach. In: In 2017 3rd international conference on information management (ICIM). Institute of Electrical and Electronics Engineers Inc., pp 159–163

Kara G, Özmen A, Weber G-W (2017) Stability advances in robust portfolio optimization under parallelepiped uncertainty. Cent Eur J Oper Res. https://doi.org/10.1007/s10100-017-0508-5

Kawas B, Laumanns M, Pratsini E (2013) A robust optimization approach to enhancing reliability in production planning under non-compliance risks. OR Spectr 35:835–865. https://doi.org/10.1007/s00291-013-0339-2

Kimms A (1998) Stability measures for rolling schedules with applications to capacity expansion planning, master production scheduling, and lot sizing. Omega 26:355–366. https://doi.org/10.1016/S0305-0483(97)00056-X

Ko M, Tiwari A, Mehnen J (2010) A review of soft computing applications in supply chain management. Appl Soft Comput 10:661–674. https://doi.org/10.1016/j.asoc.2009.09.004

Körpeolu E, Yaman H, Selim Aktürk M (2011) A multi-stage stochastic programming approach in master production scheduling. Eur J Oper Res 213:166–179. https://doi.org/10.1016/j.ejor.2011.02.032

Kovačić D, Bogataj M (2013) Reverse logistics facility location using cyclical model of extended MRP theory. Cent Eur J Oper Res 21:41–57. https://doi.org/10.1007/s10100-012-0251-x

Kuchta D (2011) A concept of a robust solution of a multicriterial linear programming problem. Cent Eur J Oper Res 19:605–613. https://doi.org/10.1007/s10100-010-0150-y

Lage Junior M, Godinho Filho M (2017) Master disassembly scheduling in a remanufacturing system with stochastic routings. Cent Eur J Oper Res 25:123–138. https://doi.org/10.1007/s10100-015-0428-1

Lee SM, Moore LJ (1974) Practical approach to production scheduling. Prod Invent Manag J 15:79–92

Lehtimaki AK (1987) Approach for solving decision planning of master scheduling by utilizing theory of fuzzy sets. Int J Prod Res 25:1781–1793

Li Z, Li Z (2015) Optimal robust optimization approximation for chance constrained optimization problem. Comput Chem Eng 74:89–99. https://doi.org/10.1016/j.compchemeng.2015.01.003

Li Z, Ding R, Floudas CA (2011) A Comparative theoretical and computational study on robust counterpart optimization: I. Robust linear optimization and robust mixed integer linear optimization. Ind Eng Chem Res 50:10567–10603. https://doi.org/10.1021/ie200150p

Li Z, Tang Q, Floudas CA (2012) A comparative theoretical and computational study on robust counterpart optimization: II. Probabilistic guarantees on constraint satisfaction. Ind Eng Chem Res 51:6769–6788. https://doi.org/10.1021/ie201651s

Mula J, Poler R, Garcia-Sabater J, Lario F (2006a) Models for production planning under uncertainty: a review. Int J Prod Econ 103:271–285. https://doi.org/10.1016/j.ijpe.2005.09.001

Mula J, Poler R, Garcia JP (2006b) MRP with flexible constraints: a fuzzy mathematical programming approach. Fuzzy Sets Syst 157:74–97. https://doi.org/10.1016/j.fss.2005.05.045

Mula J, Poler R, Garcia-Sabater JP (2008) Capacity and material requirement planning modelling by comparing deterministic and fuzzy models. Int J Prod Res 46:5589–5606. https://doi.org/10.1080/00207540701413912

Mulvey JM, Vanderbei RJ, Zenios SA (1995) Robust optimization of large-scale systems. Oper Res 43:264–281. https://doi.org/10.1287/opre.43.2.264

Nannapaneni S, Mahadevan S (2014) Uncertainty quantification in performance evaluation of manufacturing processes. In: 2014 IEEE international conference on Big Data (Big Data). IEEE, pp 996–1005

Ng TS, Fowler J (2007) Semiconductor production planning using robust optimization. In: 2007 IEEE international conference on industrial engineering and engineering management. IEEE, pp 1073–1077

Peidro D, Mula J, Poler RR, Lario F-C (2009) Quantitative models for supply chain planning under uncertainty: a review. Int J Adv Manuf Technol 43:400–420. https://doi.org/10.1007/s00170-008-1715-y

Pochet Y, Wolsey LA (2006) Production planning by mixed integer programming. Springer, Berlin

Powell SG, Baker KR, Lawson B (2008) A critical review of the literature on spreadsheet errors. Decis Support Syst 46:128–138. https://doi.org/10.1016/j.dss.2008.06.001

Rahmani D, Ramezanian R, Fattahi P, Heydari M (2013) A robust optimization model for multi-product two-stage capacitated production planning under uncertainty. Appl Math Model 37:8957–8971. https://doi.org/10.1016/j.apm.2013.04.016

Sahinidis NV (2004) Optimization under uncertainty: state-of-the-art and opportunities. Comput Chem Eng 28:971–983. https://doi.org/10.1016/j.compchemeng.2003.09.017

Sakhaii M, Tavakkoli-Moghaddam R, Bagheri M, Vatani B (2015) A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Appl Math Model 40:169–191. https://doi.org/10.1016/j.apm.2015.05.005

Soyster AL (1973) Convex programming with set-inclusive constraints and applications to inexact linear programming. Oper Res 21:1154–1157. https://doi.org/10.1287/opre.21.5.1154

Supriyanto I, Noche B (2011) Fuzzy multi-objective linear programming and simulation approach to the development of valid and realistic master production schedule. Logist J. https://doi.org/10.2195/lj_proc_supriyanto_de_201108_01

Tavakkoli-Moghaddam R, Sakhaii M, Vatani B et al (2014) A robust model for a dynamic cellular manufacturing system with production planning. Int J Eng 27:587–598. https://doi.org/10.5829/idosi.ije.2014.27.04a.09

Vargas V, Metters R (2011) A master production scheduling procedure for stochastic demand and rolling planning horizons. Int J Prod Econ 132:296–302. https://doi.org/10.1016/j.ijpe.2011.04.025

Wang J, Shu Y-F (2005) Fuzzy decision modeling for supply chain management. Fuzzy Sets Syst 150:107–127

Weng ZK, Parlar M (2005) Managing build-to-order short life-cycle products: benefits of pre-season price incentives with standardization. J Oper Manag 23:482–495. https://doi.org/10.1016/j.jom.2004.10.008

Werner R (2008) Cascading: an adjusted exchange method for robust conic programming. Cent Eur J Oper Res 16:179–189. https://doi.org/10.1007/s10100-007-0047-6

Yu C-S, Li H-L (2000) A robust optimization model for stochastic logistic problems. Int J Prod Econ 64:385–397. https://doi.org/10.1016/S0925-5273(99)00074-2

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