- -

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

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

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

Show full item record

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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/161054

Files in this item

Item Metadata

Title: A rolling horizon simulation approach for managing demand with lead time variability
Author: CAMPUZANO BOLARIN, FRANCISCO Mula, Josefa Díaz-Madroñero Boluda, Francisco Manuel Legaz-Aparicio, Álvar-Ginés
UPV Unit: Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses
Issued date:
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 ...[+]
Subjects: Rolling horizon , Demand management , Simulation , Supply chain dynamics
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
International Journal of Production Research. (issn: 0020-7543 )
DOI: 10.1080/00207543.2019.1634849
Publisher:
Taylor & Francis
Publisher version: https://doi.org/10.1080/00207543.2019.1634849
Project ID:
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/
Thanks:
This work was supported by the European Commission Horizon 2020 project Diverfarming [grant number 728003].
Type: Artículo

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.

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

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 [+]
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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record