3 research outputs found

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

    Full text link
    [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.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/s10100-020-00687-5S1289131529Araujo 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/s10732-007-9011-9ASIC (2018) Clúster de cálculo: Rigel. http://www.upv.es/entidades/ASIC/catalogo/857893normalc.html. Accessed date 22 July 2018Baker 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.xBarrett 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-VBehnamian 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-yBillington 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.29.10.1126Blackburn 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.xCao Y (2015) Long-distance procurement planning in global sourcing. Ecole Centrale Paris. https://tel.archives-ouvertes.fr/tel-01154871/. Accessed 15 Dec 2017Carlson 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.xChand 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.4.1.25.287Coronado-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/10251/61467de 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.2017.01.003DeYong 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.2015.11.006Federgruen 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.42.3.456Fisher 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.47.5.679.10485Garcia-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.2009.5223806Garcia-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-zGarcia-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.2013.052572Hair JF, Prentice E, Cano D (1999) Análisis multivariante. Prentice-Hall, Upper Saddle RiverHozak K, Hill JA (2009) Issues and opportunities regarding replanning and rescheduling frequencies. Int J Prod Res 47(18):4955–4970. https://doi.org/10.1080/00207540802047106Hsu 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.2016.2616366Jans 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.1080.0283Karimi 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)00059-8Kimms A (1997) Multi-level lot sizing and scheduling, vol 53. Physica-Verlag, Heidelberg. https://doi.org/10.1007/978-3-642-50162-3Kleindorfer P, Kunreuther H (1978) Stochastic horizons for the aggregate planning problem. Manag Sci 24(5):485–497. https://doi.org/10.1287/mnsc.24.5.485Kumar BK, Nagaraju D, Narayanan S (2016) Supply chain coordination models: a literature review. Indian J Sci Technol. https://doi.org/10.17485/ijst/2016/v9i38/86938Lalami 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/10.1080/00207543.2017.1315192Lee 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.43.4.546Lee 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.2006.81Lv 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/app7010023Maheut 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/10251/29290Maheut 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 45:46–57Maheut 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/10.1007/978-1-4471-2321-7Meindl 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 2016Meyr 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)00373-3Narayanan 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.2010.04.038Nedaei 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/10.1080/00207543.2014.884732Newman M (2005) Power laws, Pareto distributions and Zipf’s law. Contemp Phys 46(5):323–351. https://doi.org/10.1080/00107510500052444Omar 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/10.1080/00207540802130803Pérez C (2002) Estadística práctica con Statgraphics®. PEARSON EDUCACIÓN, S. A, MadridPoler 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.2011.05.072Prasad 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/s0017010180520Rafiei 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, MontrealRafiei 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/10.1080/00207543.2013.869631Ramezanian 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-zRodriguez 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.2017.03.006Sahin 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/10.1080/00207543.2013.775523Sethi S, Sorger G (1991) A theory of rolling horizon decision making. Ann Oper Res 29(1):387–415. https://doi.org/10.1007/BF02283607Simpson 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)00015-0Stadtler 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.46.2.318.11924Stadtler 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.51.3.487.14949Tiacci 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.2012.02.007Trigeiro WW (1987) A dual-cost heuristic for the capacitated lot sizing problem. IIE Trans 19(1):67–72. https://doi.org/10.1080/07408178708975371Wolsey 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.48.12.1587.442Xie 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)00262-1Yıldırım 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/s00291-005-0203-0Zhao 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/10.1080/095372898234109Zoller K, Robrade A (1988) Efficient heuristics for dynamic lot sizing. Int J Prod Res 26(2):249–265. https://doi.org/10.1080/00207548808947857Zulkafli 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.2017.08.15
    corecore