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Operations planning test bed under rolling horizons, multiproduct,multiechelon, multiprocess for capacitated production planning modelling with strokes

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Operations planning test bed under rolling horizons, multiproduct,multiechelon, multiprocess for capacitated production planning modelling with strokes

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dc.contributor.author Rius-Sorolla, G. es_ES
dc.contributor.author Maheut, Julien es_ES
dc.contributor.author Estelles Miguel, Sofia es_ES
dc.contributor.author García Sabater, José Pedro es_ES
dc.date.accessioned 2021-11-05T14:06:35Z
dc.date.available 2021-11-05T14:06:35Z
dc.date.issued 2021-12 es_ES
dc.identifier.issn 1435-246X es_ES
dc.identifier.uri http://hdl.handle.net/10251/176246
dc.description.abstract [EN] One of the problems when conducting research in mathematical programming models for operations planning is having an adequate database of experiments that can be used to verify advances and developments with enough factors to understand different consequences. This paper presents a test bed generator and instances database for a rolling horizons analysis for multiechelon planning, multiproduct with alternatives processes, multistroke, multicapacity with different stochastic demand patterns to be used with a stroke-like bill of materials considering production costs, setup, storage and delays for operations management. From the analysis of the operations planning obtained from this test bed, it is concluded that a product structure with an alternative process obtains the lowest total cost and the highest service level. In addition, decreasing seasonal demand could present a lower total cost than constant demand, but would generate a worse service level. This test bed will allow researchers further investigation so as to verify improvements in forecast methods, rolling horizons parameters, employed software, etc. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Central European Journal of Operations Research es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Rolling horizon es_ES
dc.subject Scheduling es_ES
dc.subject GMOP es_ES
dc.subject Supply chain management es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title Operations planning test bed under rolling horizons, multiproduct,multiechelon, multiprocess for capacitated production planning modelling with strokes es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10100-020-00687-5 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 Rius-Sorolla, G.; Maheut, J.; Estelles Miguel, S.; García Sabater, JP. (2021). Operations planning test bed under rolling horizons, multiproduct,multiechelon, multiprocess for capacitated production planning modelling with strokes. Central European Journal of Operations Research. 29:1289-1315. https://doi.org/10.1007/s10100-020-00687-5 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10100-020-00687-5 es_ES
dc.description.upvformatpinicio 1289 es_ES
dc.description.upvformatpfin 1315 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 29 es_ES
dc.relation.pasarela S\413463 es_ES
dc.description.references Araujo SA, Arenales MN, Clark A (2007) Joint rolling-horizon scheduling of materials processing and lot-sizing with sequence-dependent setups. J Heuristics 13(4):337–358. https://doi.org/10.1007/s10732-007-9011-9 es_ES
dc.description.references ASIC (2018) Clúster de cálculo: Rigel. http://www.upv.es/entidades/ASIC/catalogo/857893normalc.html. Accessed date 22 July 2018 es_ES
dc.description.references Baker KR (1977) An experimental study of the effectiveness of rolling schedules in production planning. Decis Sci 8(1):19–27. https://doi.org/10.1111/j.1540-5915.1977.tb01065.x es_ES
dc.description.references Barrett RT, LaForge RL (1991) A study of replanning frequencies in a material requirements planning system. Comput Oper Res 18(6):569–578. https://doi.org/10.1016/0305-0548(91)90062-V es_ES
dc.description.references Behnamian J, Fatemi Ghomi SMT (2014) A survey of multi-factory scheduling. J Intell Manuf 27:1–19. https://doi.org/10.1007/s10845-014-0890-y es_ES
dc.description.references Billington PJ, McClain JO, Thomas LJ (1983) Mathematical programming approaches to capacity-constrained MRP systems: review, formulation and problem reduction. Manag Sci 29(10):1126–1141. https://doi.org/10.1287/mnsc.29.10.1126 es_ES
dc.description.references Blackburn JD, Millen RA (1980) Heuristic lot-sizing performance in a rolling-schedule environment. Decis Sci 11(4):691–701. https://doi.org/10.1111/j.1540-5915.1980.tb01170.x es_ES
dc.description.references Cao Y (2015) Long-distance procurement planning in global sourcing. Ecole Centrale Paris. https://tel.archives-ouvertes.fr/tel-01154871/. Accessed 15 Dec 2017 es_ES
dc.description.references Carlson RC, Beckman SL, Kropp DH (1982) The effectiveness o extending the horizon in rolling production scheduling. Decis Sci 13(1):129–146. https://doi.org/10.1111/j.1540-5915.1982.tb00136.x es_ES
dc.description.references Chand S, Hsu VN, Sethi S (2002) Forecast, solution, and rolling horizons in operations management problems: a classified bibliography. Manuf Serv Oper Manag 4(1):25–43. https://doi.org/10.1287/msom.4.1.25.287 es_ES
dc.description.references Coronado-Hernández JR (2016) Análisis del efecto de algunos factores de complejidad e incertidumbre en el rendimiento de las Cadenas de Suministro. Propuesta de una herramienta de valoración basada en simulación. Universitat Politècnica de València, Valencia (Spain). https://doi.org/10.4995/Thesis/10251/61467 es_ES
dc.description.references de Sampaio RJB, Wollmann RRG, Vieira PFG (2017) A flexible production planning for rolling-horizons. Int J Prod Econ 190:31–36. https://doi.org/10.1016/j.ijpe.2017.01.003 es_ES
dc.description.references DeYong GD, Cattani KD (2016) Fenced in? Stochastic and deterministic planning models in a time-fenced, rolling-horizon scheduling system. Eur J Oper Res 251(1):85–95. https://doi.org/10.1016/j.ejor.2015.11.006 es_ES
dc.description.references Federgruen A, Tzur M (1994) Minimal forecast horizons and a new planning procedure for the general dynamic lot sizing model: nervousness revisited. Oper Res 42(3):456–468. https://doi.org/10.1287/opre.42.3.456 es_ES
dc.description.references Fisher ML, Ramdas K, Zheng YS (2001) Ending inventory valuation in multiperiod production scheduling. Manag Sci 47(5):679–692. https://doi.org/10.1287/mnsc.47.5.679.10485 es_ES
dc.description.references Garcia-Sabater JP, Maheut J, Garcia-Sabater JJ (2009. A capacitated material requirements planning model considering delivery constraints: a case study from the automotive industry. In: 2009 international conference on computers and industrial engineering, IEEE, pp 378–383. https://doi.org/10.1109/ICCIE.2009.5223806 es_ES
dc.description.references Garcia-Sabater JP, Maheut J, Garcia-Sabater JJ (2012) A two-stage sequential planning scheme for integrated operations planning and scheduling system using MILP: the case of an engine assembler. Flex Serv Manuf J 24(2):171–209. https://doi.org/10.1007/s10696-011-9126-z es_ES
dc.description.references Garcia-Sabater JP, Maheut J, Marin-Garcia JA (2013) A new formulation technique to model materials and operations planning: the generic materials and operations planning (GMOP) problem. Eur J Ind Eng 7(2):119. https://doi.org/10.1504/EJIE.2013.052572 es_ES
dc.description.references Hair JF, Prentice E, Cano D (1999) Análisis multivariante. Prentice-Hall, Upper Saddle River es_ES
dc.description.references Hozak K, Hill JA (2009) Issues and opportunities regarding replanning and rescheduling frequencies. Int J Prod Res 47(18):4955–4970. https://doi.org/10.1080/00207540802047106 es_ES
dc.description.references Hsu CH, Yang HC (2017) Real-time near-optimal scheduling with rolling horizon for automatic manufacturing cell. IEEE Access 5:3369–3375. https://doi.org/10.1109/ACCESS.2016.2616366 es_ES
dc.description.references Jans R (2009) Solving lot-sizing problems on parallel identical machines using symmetry-breaking constraints. Inf J Comput 21(1):123–136. https://doi.org/10.1287/ijoc.1080.0283 es_ES
dc.description.references Karimi B, Fatemi Ghomi SMT, Wilson JM (2003) The capacitated lot sizing problem: a review of models and algorithms. Omega 31(5):365–378. https://doi.org/10.1016/S0305-0483(03)00059-8 es_ES
dc.description.references Kimms A (1997) Multi-level lot sizing and scheduling, vol 53. Physica-Verlag, Heidelberg. https://doi.org/10.1007/978-3-642-50162-3 es_ES
dc.description.references Kleindorfer P, Kunreuther H (1978) Stochastic horizons for the aggregate planning problem. Manag Sci 24(5):485–497. https://doi.org/10.1287/mnsc.24.5.485 es_ES
dc.description.references Kumar BK, Nagaraju D, Narayanan S (2016) Supply chain coordination models: a literature review. Indian J Sci Technol. https://doi.org/10.17485/ijst/2016/v9i38/86938 es_ES
dc.description.references Lalami I, Frein Y, Gayon JP (2017) Production planning in automotive powertrain plants: a case study. Int J Prod Res 55(18):5378–5393. https://doi.org/10.1080/00207543.2017.1315192 es_ES
dc.description.references Lee HL, Padmanabhan V, Whang S (1997) The bullwhip effect in supply chains 1. Sloan Manag Rev Assoc 38(3):93–102. https://doi.org/10.1287/mnsc.43.4.546 es_ES
dc.description.references Lee DU, Villasenor JD, Luk W, Leong PHW (2006) A hardware Gaussian noise generator using the box-muller method and its error analysis. IEEE Trans Comput 55(6):659–671. https://doi.org/10.1109/TC.2006.81 es_ES
dc.description.references Lv Y, Zhang J, Qin W (2017) A genetic regulatory network-based method for dynamic hybrid flow shop scheduling with uncertain processing times. Appl Sci 7(1):23. https://doi.org/10.3390/app7010023 es_ES
dc.description.references Maheut J (2013) Modelos y Algoritmos Basados en el Concepto Stroke para la Planificación y Programación de Operaciones con Alternativas en Redes de Suministro. Universitat Politècnica de València, Valencia (Spain). https://doi.org/10.4995/Thesis/10251/29290 es_ES
dc.description.references Maheut J, Garcia-Sabater JP (2011) La matriz de operaciones y materiales y la matriz de operaciones y recursos, un nuevo enfoque para resolver el problema GMOP basado en el concepto del stroke. Direccion y Organizacion 45:46–57 es_ES
dc.description.references Maheut J, Garcia-sabater JP, Mula J (2012) A supply chain operations lot-sizing and scheduling model with alternative operations. In: Sethi SP, Bogataj M, Ros-McDonnell L (eds) Industrial engineering: innovative networks. Springer, London, pp 309–316. https://doi.org/10.1007/978-1-4471-2321-7 es_ES
dc.description.references Meindl B, Templ M (2012) Analysis of commercial and free and open source solvers for linear optimization problems. Common Tools and Harmonized Methodologies for SDC in the ESS, pp 1–13. http://neon.vb.cbs.nl/cascprivate/..%5Ccasc%5CESSNet2%5Cdeliverable_solverstudy.pdf. Accessed 15 Dec 2016 es_ES
dc.description.references Meyr H (2002) Simultaneous lotsizing and scheduling on parallel machines. Eur J Oper Res 139(2):277–292. https://doi.org/10.1016/S0377-2217(01)00373-3 es_ES
dc.description.references Narayanan A, Robinson P (2010) Evaluation of joint replenishment lot-sizing procedures in rolling horizon planning systems. Int J Prod Econ 127(1):85–94. https://doi.org/10.1016/j.ijpe.2010.04.038 es_ES
dc.description.references Nedaei H, Mahlooji H (2014) Joint multi-objective master production scheduling and rolling horizon policy analysis in make-to-order supply chains. Int J Prod Res 52(9):2767–2787. https://doi.org/10.1080/00207543.2014.884732 es_ES
dc.description.references Newman M (2005) Power laws, Pareto distributions and Zipf’s law. Contemp Phys 46(5):323–351. https://doi.org/10.1080/00107510500052444 es_ES
dc.description.references Omar MK, Bennell JA (2009) Revising the master production schedule in a HPP framework context. Int J Prod Res 47(20):5857–5878. https://doi.org/10.1080/00207540802130803 es_ES
dc.description.references Pérez C (2002) Estadística práctica con Statgraphics®. PEARSON EDUCACIÓN, S. A, Madrid es_ES
dc.description.references Poler R, Mula J (2011) Forecasting model selection through out-of-sample rolling horizon weighted errors. Expert Syst Appl 38(12):14778–14785. https://doi.org/10.1016/j.eswa.2011.05.072 es_ES
dc.description.references Prasad PSS, Krishnaiah Chetty OV (2001) Multilevel lot sizing with a genetic algorithm under fixed and rolling horizons. Int J Adv Manuf Technol 18(7):520–527. https://doi.org/10.1007/s0017010180520 es_ES
dc.description.references Rafiei R, Gaudreault J, Bouchard M, Santa-Eulalia L (2012) A reactive planning approach for demand-driven wood remanufacturing industry: a real-scale application, vol 71. CIRRELT, Montreal es_ES
dc.description.references Rafiei R, Nourelfath M, Gaudreault J, Santa-Eulalia LA, Bouchard M (2014) A periodic re-planning approach for demand-driven wood remanufacturing industry: a real-scale application. Int J Prod Res 52(14):4198–4215. https://doi.org/10.1080/00207543.2013.869631 es_ES
dc.description.references Ramezanian R, Fallah Sanami S, Shafiei Nikabadi M (2017) A simultaneous planning of production and scheduling operations in flexible flow shops: case study of tile industry. Int J Adv Manuf Technol 88(9–12):2389–2403. https://doi.org/10.1007/s00170-016-8955-z es_ES
dc.description.references Rodriguez MA, Montagna JM, Vecchietti A, Corsano G (2017) Generalized disjunctive programming model for the multi-period production planning optimization: an application in a polyurethane foam manufacturing plant. Comput Chem Eng 103:69–80. https://doi.org/10.1016/j.compchemeng.2017.03.006 es_ES
dc.description.references Sahin F, Narayanan A, Robinson EP (2013) Rolling horizon planning in supply chains: review, implications and directions for future research. Int J Prod Res 51(18):5413–5436. https://doi.org/10.1080/00207543.2013.775523 es_ES
dc.description.references Sethi S, Sorger G (1991) A theory of rolling horizon decision making. Ann Oper Res 29(1):387–415. https://doi.org/10.1007/BF02283607 es_ES
dc.description.references Simpson NC (2001) Questioning the relative virtues of dynamic lot sizing rules. Comput Oper Res 28(9):899–914. https://doi.org/10.1016/S0305-0548(00)00015-0 es_ES
dc.description.references Stadtler H (2000) Improved rolling schedules for the dynamic single-level lot-sizing problem. Manag Sci 46(2):318–326. https://doi.org/10.1287/mnsc.46.2.318.11924 es_ES
dc.description.references Stadtler H (2003) Multilevel lot sizing with setup times and multiple constrained resources: internally rolling schedules with lot-sizing windows. Oper Res 51(3):487–502. https://doi.org/10.1287/opre.51.3.487.14949 es_ES
dc.description.references Tiacci L, Saetta S (2012) Demand forecasting, lot sizing and scheduling on a rolling horizon basis. Int J Prod Econ 140:803–814. https://doi.org/10.1016/j.ijpe.2012.02.007 es_ES
dc.description.references Trigeiro WW (1987) A dual-cost heuristic for the capacitated lot sizing problem. IIE Trans 19(1):67–72. https://doi.org/10.1080/07408178708975371 es_ES
dc.description.references Wolsey LA (2002) Solving multi-item lot-sizing problems with an MIP solver using classification and reformulation. Manage Sci 48(12):1587–1602. https://doi.org/10.1287/mnsc.48.12.1587.442 es_ES
dc.description.references Xie J, Zhao X, Lee TS (2003) Freezing the master production schedule under single resource constraint and demand uncertainty. Int J Prod Econ 83(1):65–84. https://doi.org/10.1016/S0925-5273(02)00262-1 es_ES
dc.description.references Yıldırım I, Tan B, Karaesmen F (2005) A multiperiod stochastic production planning and sourcing problem with service level constraints. OR Spectrum 27(2–3):471–489. https://doi.org/10.1007/s00291-005-0203-0 es_ES
dc.description.references Zhao X, Xie J (1998) Multilevel lot-sizing heuristics and freezing the master production schedule in material requirements planning systems. Prod Plan Control 9(4):371–384. https://doi.org/10.1080/095372898234109 es_ES
dc.description.references Zoller K, Robrade A (1988) Efficient heuristics for dynamic lot sizing. Int J Prod Res 26(2):249–265. https://doi.org/10.1080/00207548808947857 es_ES
dc.description.references Zulkafli NI, Kopanos GM (2017) Integrated condition-based planning of production and utility systems under uncertainty. J Clean Prod 167:776–805. https://doi.org/10.1016/j.jclepro.2017.08.152 es_ES


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