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

[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.This work was supported by the European Commission Horizon 2020 project Diverfarming [grant number 728003].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/10.1080/00207543.2019.1634849S380038205812Agaran, 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.xBhattacharya, 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/s00170-010-2987-6Boulaksil, 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/s00291-007-0086-3Brown, 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.2015.08.003Campuzano, 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.2009.12.002Campuzano-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:10.1080/00207543.2012.760854De 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.2017.01.003Díaz-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:10.1080/00207543.2014.920115Díaz-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.2017.08.009Disney, 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)00464-4Dominguez, 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.2015.03.012Fransoo, 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:10.1108/13598540010319993Geary, 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.2005.05.009Giard, 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:10.1080/00207543.2012.754552Hosoda, 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.2017.07.020Hussain, 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:10.1108/09600031111185248Jakšič, 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.2006.12.018Karimi, 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)00059-8Li, 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.2016.10.005Lian, 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.20416D. 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-06-2012-0238Moreno, 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)2.284Mula, 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.2010.06.007Mula, 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.2005.05.045Mula, 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.2006.11.003Mula, 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:10.1080/00207540701413912Ostberg, 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-9-479-2018Pacheco, 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.2017.09.015Nyoman 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)00419-3Rafiei, 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:10.1080/00207543.2013.869631Sahin, 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:10.1080/00207543.2013.775523Sahin, 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.xSahin, 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.2004.08.007Schmidt, 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.2015.04.024Simpson, 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)00005-8Sridharan, 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.xTaylor, 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:10.1108/13598540610682381Van 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.2004.10.001Vargas, 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.2011.04.025Wagner, H. M., & Whitin, T. M. (1958). Dynamic Version of the Economic Lot Size Model. Management Science, 5(1), 89-96. doi:10.1287/mnsc.5.1.89WEMMERLÖV, U., & WHYBARK, D. C. (1984). Lot-sizing under uncertainty in a rolling schedule environment. International Journal of Production Research, 22(3), 467-484. doi:10.1080/00207548408942467Zhang, 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.2015.07.01

Adaptive morphological filters based on a multiple orientation vector field dependent on image local features.

International audienceThis paper addresses the formulation of adaptive morphological filters based on spatially-variant structuring elements. The adaptivity of these filters is achieved by modifying the shape and orientation of the structuring elements according to a multiple orientation vector field. This vector field is provided by means of a bank of directional openings which can take into account the possible multiple orientations of the contours in the image. After reviewing and formalizing the definition of the spatially-variant dilation, erosion, opening and closing, the proposed structuring elements are described. These spatially-variant structuring elements are based on ellipses which vary over the image domain adapting locally their orientation according to the multiple orientation vector field and their shape (the eccentricity of the ellipses) according to the distance to relevant contours of the objects. The proposed adaptive morphological filters are used on gray-level images and are compared with spatially-invariant filters, with spatially-variant filters based on a single orientation vector field, and with adaptive morphological bilateral filters. Results show that the morphological filters based on a multiple orientation vector field are more adept at enhancing and preserving structures which contains more than one orientation

Multiscale estimation of multiple orientations based on morphological directional openings

International audienceThis paper introduces a novel approach to estimate multiple orientations at each pixel of a gray image at different scales. The main orientations are provided by a bank of directional openings. Gathering the responses of the filtered directional openings provide at each pixel a discrete sequence which is the directional signature. Then, the directional signature is interpolated by cubic B-splines, and the multiple orientations at each pixel are obtained by means of peak detection in the continuous directional signature. This procedure is performed using structuring elements with different lengths which results in a multiscale approach. The comparison with other existing methods as well as the experimental results on images shows the ability of the proposed method to detect multiple orientations in textured images at different scales with high accuracy