- -

A rolling horizon simulation approach for managing demand with lead time variability

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

A rolling horizon simulation approach for managing demand with lead time variability

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author CAMPUZANO BOLARIN, FRANCISCO es_ES
dc.contributor.author Mula, Josefa es_ES
dc.contributor.author Díaz-Madroñero Boluda, Francisco Manuel es_ES
dc.contributor.author Legaz-Aparicio, Álvar-Ginés es_ES
dc.date.accessioned 2021-02-11T04:32:35Z
dc.date.available 2021-02-11T04:32:35Z
dc.date.issued 2020-06-17 es_ES
dc.identifier.issn 0020-7543 es_ES
dc.identifier.uri http://hdl.handle.net/10251/161054
dc.description.abstract [EN] This paper proposes a rolling horizon (RH) approach to deal with management problems under dynamic demand in planning horizons with variable lead times using system dynamics (SD) simulation. Thus, the nature of dynamic RH solutions entails no inconveniences to contemplate planning horizons with unpredictable demands. This is mainly because information is periodically updated and replanning is done in time. Therefore, inventory and logistic costs may be lower. For the first time, an RH is applied for demand management with variable lead times along with SD simulation models, which allowed the use of lot-sizing techniques to be evaluated (Wagner-Whitin and Silver-Meal). The basic scenario is based on a real-world example from an automotive single-level SC composed of a first-tier supplier and a car assembler that contemplates uncertain demands while planning the RH and 216 subscenarios by modifying constant and variable lead times, holding costs and order costs, combined with lot-sizing techniques. Twenty-eight more replications comprising 504 new subscenarios with variable lead times are generated to represent a relative variation coefficient of the initial demand. We conclude that our RH simulation approach, along with lot-sizing techniques, can generate more sustainable planning results in total costs, fill rates and bullwhip effect terms. es_ES
dc.description.sponsorship This work was supported by the European Commission Horizon 2020 project Diverfarming [grant number 728003]. es_ES
dc.language Inglés es_ES
dc.publisher Taylor & Francis es_ES
dc.relation.ispartof International Journal of Production Research es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Rolling horizon es_ES
dc.subject Demand management es_ES
dc.subject Simulation es_ES
dc.subject Supply chain dynamics es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title A rolling horizon simulation approach for managing demand with lead time variability es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1080/00207543.2019.1634849 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/728003/EU/Crop diversification and low-input farming across Europe: from practitioners engagement and ecosystems services to increased revenues and chain organisation/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Campuzano Bolarin, F.; Mula, J.; Díaz-Madroñero Boluda, FM.; Legaz-Aparicio, Á. (2020). A rolling horizon simulation approach for managing demand with lead time variability. International Journal of Production Research. 58(12):3800-3820. https://doi.org/10.1080/00207543.2019.1634849 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1080/00207543.2019.1634849 es_ES
dc.description.upvformatpinicio 3800 es_ES
dc.description.upvformatpfin 3820 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 58 es_ES
dc.description.issue 12 es_ES
dc.relation.pasarela S\390948 es_ES
dc.contributor.funder European Commission es_ES
dc.description.references Agaran, B., W. W. Buchanan, and M. K. Yurtseven. 2007. “Regulating Bullwhip Effect in Supply Chains through Modern Control Theory.” in PICMET ‘07 – 2007 Portland International Conference on Management of Engineering & Technology, 2391–2398. IEEE. http://doi.org/10.1109/PICMET.2007.4349573. es_ES
dc.description.references Baker, K. R. (1977). AN EXPERIMENTAL STUDY OF THE EFFECTIVENESS OF ROLLING SCHEDULES IN PRODUCTION PLANNING. Decision Sciences, 8(1), 19-27. doi:10.1111/j.1540-5915.1977.tb01065.x es_ES
dc.description.references Bhattacharya, R., & Bandyopadhyay, S. (2010). A review of the causes of bullwhip effect in a supply chain. The International Journal of Advanced Manufacturing Technology, 54(9-12), 1245-1261. doi:10.1007/s00170-010-2987-6 es_ES
dc.description.references Boulaksil, Y., Fransoo, J. C., & van Halm, E. N. G. (2007). Setting safety stocks in multi-stage inventory systems under rolling horizon mathematical programming models. OR Spectrum, 31(1). doi:10.1007/s00291-007-0086-3 es_ES
dc.description.references Brown, M. E., & Kshirsagar, V. (2015). Weather and international price shocks on food prices in the developing world. Global Environmental Change, 35, 31-40. doi:10.1016/j.gloenvcha.2015.08.003 es_ES
dc.description.references Campuzano, F., Mula, J., & Peidro, D. (2010). Fuzzy estimations and system dynamics for improving supply chains. Fuzzy Sets and Systems, 161(11), 1530-1542. doi:10.1016/j.fss.2009.12.002 es_ES
dc.description.references Campuzano-Bolarín, F., Mula, J., & Peidro, D. (2013). An extension to fuzzy estimations and system dynamics for improving supply chains. International Journal of Production Research, 51(10), 3156-3166. doi:10.1080/00207543.2012.760854 es_ES
dc.description.references De Sampaio, R. J. B., Wollmann, R. R. G., & Vieira, P. F. G. (2017). A flexible production planning for rolling-horizons. International Journal of Production Economics, 190, 31-36. doi:10.1016/j.ijpe.2017.01.003 es_ES
dc.description.references Díaz-Madroñero, M., Mula, J., & Jiménez, M. (2014). Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions. International Journal of Production Research, 52(23), 6971-6988. doi:10.1080/00207543.2014.920115 es_ES
dc.description.references Díaz-Madroñero, M., Mula, J., & Peidro, D. (2017). A mathematical programming model for integrating production and procurement transport decisions. Applied Mathematical Modelling, 52, 527-543. doi:10.1016/j.apm.2017.08.009 es_ES
dc.description.references Disney, S. M., Naim, M. M., & Potter, A. (2004). Assessing the impact of e-business on supply chain dynamics. International Journal of Production Economics, 89(2), 109-118. doi:10.1016/s0925-5273(02)00464-4 es_ES
dc.description.references Dominguez, R., Cannella, S., & Framinan, J. M. (2015). The impact of the supply chain structure on bullwhip effect. Applied Mathematical Modelling, 39(23-24), 7309-7325. doi:10.1016/j.apm.2015.03.012 es_ES
dc.description.references Fransoo, J. C., & Wouters, M. J. F. (2000). Measuring the bullwhip effect in the supply chain. Supply Chain Management: An International Journal, 5(2), 78-89. doi:10.1108/13598540010319993 es_ES
dc.description.references Geary, S., Disney, S. M., & Towill, D. R. (2006). On bullwhip in supply chains—historical review, present practice and expected future impact. International Journal of Production Economics, 101(1), 2-18. doi:10.1016/j.ijpe.2005.05.009 es_ES
dc.description.references Giard, V., & Sali, M. (2013). The bullwhip effect in supply chains: a study of contingent and incomplete literature. International Journal of Production Research, 51(13), 3880-3893. doi:10.1080/00207543.2012.754552 es_ES
dc.description.references Hosoda, T., & Disney, S. M. (2018). A unified theory of the dynamics of closed-loop supply chains. European Journal of Operational Research, 269(1), 313-326. doi:10.1016/j.ejor.2017.07.020 es_ES
dc.description.references Hussain, M., & Drake, P. R. (2011). Analysis of the bullwhip effect with order batching in multi‐echelon supply chains. International Journal of Physical Distribution & Logistics Management, 41(10), 972-990. doi:10.1108/09600031111185248 es_ES
dc.description.references Jakšič, M., & Rusjan, B. (2008). The effect of replenishment policies on the bullwhip effect: A transfer function approach. European Journal of Operational Research, 184(3), 946-961. doi:10.1016/j.ejor.2006.12.018 es_ES
dc.description.references Karimi, B., Fatemi Ghomi, S. M. T., & Wilson, J. M. (2003). The capacitated lot sizing problem: a review of models and algorithms. Omega, 31(5), 365-378. doi:10.1016/s0305-0483(03)00059-8 es_ES
dc.description.references Li, J., Ghadge, A., & Tiwari, M. K. (2016). Impact of replenishment strategies on supply chain performance under e-shopping scenario. Computers & Industrial Engineering, 102, 78-87. doi:10.1016/j.cie.2016.10.005 es_ES
dc.description.references Lian, Z., Liu, L., & Zhu, S. X. (2010). Rolling-horizon replenishment: Policies and performance analysis. Naval Research Logistics (NRL), 57(6), 489-502. doi:10.1002/nav.20416 es_ES
dc.description.references D. Mendoza, J., Mula, J., & Campuzano-Bolarin, F. (2014). Using systems dynamics to evaluate the tradeoff among supply chain aggregate production planning policies. International Journal of Operations & Production Management, 34(8), 1055-1079. doi:10.1108/ijopm-06-2012-0238 es_ES
dc.description.references Moreno, J. R., Mula, J., & Campuzano-Bolarin, F. (2015). Increasing the Equity of a Flower Supply Chain by Improving Order Management and Supplier Selection. International Journal of Simulation Modelling, 14(2), 201-214. doi:10.2507/ijsimm14(2)2.284 es_ES
dc.description.references Mula, J., Peidro, D., & Poler, R. (2010). The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. International Journal of Production Economics, 128(1), 136-143. doi:10.1016/j.ijpe.2010.06.007 es_ES
dc.description.references Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy Sets and Systems, 157(1), 74-97. doi:10.1016/j.fss.2005.05.045 es_ES
dc.description.references Mula, J., Poler, R., & Garcia-Sabater, J. P. (2007). Material Requirement Planning with fuzzy constraints and fuzzy coefficients. Fuzzy Sets and Systems, 158(7), 783-793. doi:10.1016/j.fss.2006.11.003 es_ES
dc.description.references Mula, J., Poler, R., & Garcia-Sabater, J. P. (2008). Capacity and material requirement planning modelling by comparing deterministic and fuzzy models. International Journal of Production Research, 46(20), 5589-5606. doi:10.1080/00207540701413912 es_ES
dc.description.references Ostberg, S., Schewe, J., Childers, K., & Frieler, K. (2018). Changes in crop yields and their variability at different levels of global warming. Earth System Dynamics, 9(2), 479-496. doi:10.5194/esd-9-479-2018 es_ES
dc.description.references Pacheco, E. de O., Cannella, S., Lüders, R., & Barbosa-Povoa, A. P. (2017). Order-up-to-level policy update procedure for a supply chain subject to market demand uncertainty. Computers & Industrial Engineering, 113, 347-355. doi:10.1016/j.cie.2017.09.015 es_ES
dc.description.references Nyoman Pujawan, I. (2004). The effect of lot sizing rules on order variability. European Journal of Operational Research, 159(3), 617-635. doi:10.1016/s0377-2217(03)00419-3 es_ES
dc.description.references Rafiei, R., Nourelfath, M., Gaudreault, J., Santa-Eulalia, L. A., & Bouchard, M. (2013). A periodic re-planning approach for demand-driven wood remanufacturing industry: a real-scale application. International Journal of Production Research, 52(14), 4198-4215. doi:10.1080/00207543.2013.869631 es_ES
dc.description.references Sahin, F., Narayanan, A., & Robinson, E. P. (2013). Rolling horizon planning in supply chains: review, implications and directions for future research. International Journal of Production Research, 51(18), 5413-5436. doi:10.1080/00207543.2013.775523 es_ES
dc.description.references Sahin, F., & Robinson, E. P. (2002). Flow Coordination and Information Sharing in Supply Chains: Review, Implications, and Directions for Future Research. Decision Sciences, 33(4), 505-536. doi:10.1111/j.1540-5915.2002.tb01654.x es_ES
dc.description.references Sahin, F., & Robinson, E. P. (2004). Information sharing and coordination in make-to-order supply chains. Journal of Operations Management, 23(6), 579-598. doi:10.1016/j.jom.2004.08.007 es_ES
dc.description.references Schmidt, M., Münzberg, B., & Nyhuis, P. (2015). Determining Lot Sizes in Production Areas – Exact Calculations versus Research Based Estimation. Procedia CIRP, 28, 143-148. doi:10.1016/j.procir.2015.04.024 es_ES
dc.description.references Simpson, N. . (1999). Multiple level production planning in rolling horizon assembly environments. European Journal of Operational Research, 114(1), 15-28. doi:10.1016/s0377-2217(98)00005-8 es_ES
dc.description.references Sridharan, S. V., Berry, W. L., & Udayabhanu, V. (1988). MEASURING MASTER PRODUCTION SCHEDULE STABILITY UNDER ROLLING PLANNING HORIZONS. Decision Sciences, 19(1), 147-166. doi:10.1111/j.1540-5915.1988.tb00259.x es_ES
dc.description.references Taylor, D. H., & Fearne, A. (2006). Towards a framework for improvement in the management of demand in agri‐food supply chains. Supply Chain Management: An International Journal, 11(5), 379-384. doi:10.1108/13598540610682381 es_ES
dc.description.references Van den Heuvel, W., & Wagelmans, A. P. M. (2005). A comparison of methods for lot-sizing in a rolling horizon environment. Operations Research Letters, 33(5), 486-496. doi:10.1016/j.orl.2004.10.001 es_ES
dc.description.references Vargas, V., & Metters, R. (2011). A master production scheduling procedure for stochastic demand and rolling planning horizons. International Journal of Production Economics, 132(2), 296-302. doi:10.1016/j.ijpe.2011.04.025 es_ES
dc.description.references Wagner, H. M., & Whitin, T. M. (1958). Dynamic Version of the Economic Lot Size Model. Management Science, 5(1), 89-96. doi:10.1287/mnsc.5.1.89 es_ES
dc.description.references WEMMERLÖV, U., & WHYBARK, D. C. (1984). Lot-sizing under uncertainty in a rolling schedule environment. International Journal of Production Research, 22(3), 467-484. doi:10.1080/00207548408942467 es_ES
dc.description.references Zhang, C., & Qu, X. (2015). The effect of global oil price shocks on China’s agricultural commodities. Energy Economics, 51, 354-364. doi:10.1016/j.eneco.2015.07.012 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem