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Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

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Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

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dc.contributor.author Mundi, I. es_ES
dc.contributor.author Alemany Díaz, María Del Mar es_ES
dc.contributor.author Poler, R. es_ES
dc.contributor.author Fuertes-Miquel, Vicente S. es_ES
dc.date.accessioned 2020-02-08T21:02:06Z
dc.date.available 2020-02-08T21:02:06Z
dc.date.issued 2019 es_ES
dc.identifier.issn 0020-7543 es_ES
dc.identifier.uri http://hdl.handle.net/10251/136500
dc.description.abstract [EN] Lack of homogeneity in the product (LHP) appears in some production processes that confer heterogeneity in the characteristics of the products obtained. Supply chains with this issue have to classify the product in different homogeneous subsets, whose quantity is uncertain during the production planning process. This paper proposes a generic framework for reviewing in a unified way the literature about production planning models dealing with LHP uncertainty. This analysis allows the identification of similarities among sectors to transfer solutions between them and gaps existing in the literature for further research. The results of the review show: (1) sectors affected by LHP inherent uncertainty, (2) the inherent LHP uncertainty types modelled, and (3) the approaches for modelling LHP uncertainty most widely employed. Finally, we suggest a conceptual model reflecting the aspects to be considered when modelling the production planning in sectors with LHP in an uncertain environment. es_ES
dc.description.sponsorship This research was initiated within the framework of the project funded by the Ministerio de Economía y Competitividad [Ref. DPI2011-23597] entitled ‘Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity’ (PLANGES-FHP) already finished. After, the project leading to this application has received funding from the European Union’s research and innovation programme under the H2020 Marie Skłodowska-Curie Actions with the grant agreement No 691249, Project entitled ’Enhancing and implementing Knowledge based ICT solutions within high Riskand Uncertain Conditions for Agriculture Production Systems’ (RUC-APS). 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 Reserva de todos los derechos es_ES
dc.subject Planning es_ES
dc.subject Optimisation es_ES
dc.subject Production es_ES
dc.subject Uncertainty es_ES
dc.subject Mathematical modelling es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.subject.classification MECANICA DE FLUIDOS es_ES
dc.title Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1080/00207543.2019.1566665 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/691249/EU/Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//DPI2011-23597/ES/METODOS Y MODELOS PARA LA PLANIFICACION DE OPERACIONES Y GESTION DE PEDIDOS EN CADENAS DE SUMINISTRO CARACTERIZADAS POR LA FALTA DE HOMOGENEIDAD EN EL PRODUCTO/ 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.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Mundi, I.; Alemany Díaz, MDM.; Poler, R.; Fuertes-Miquel, VS. (2019). Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model. International Journal of Production Research. 57(15-16):5239-5283. https://doi.org/10.1080/00207543.2019.1566665 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1080/00207543.2019.1566665 es_ES
dc.description.upvformatpinicio 5239 es_ES
dc.description.upvformatpfin 5283 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 57 es_ES
dc.description.issue 15-16 es_ES
dc.relation.pasarela S\376758 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.description.references Ahumada, O., Rene Villalobos, J., & Nicholas Mason, A. (2012). Tactical planning of the production and distribution of fresh agricultural products under uncertainty. Agricultural Systems, 112, 17-26. doi:10.1016/j.agsy.2012.06.002 es_ES
dc.description.references Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1-20. doi:10.1016/j.ejor.2008.02.014 es_ES
dc.description.references Alarcón, F., Alemany, M. M. E., Lario, F. C., & Oltra, R. F. (2011). La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación del inventario. Boletín de la Sociedad Española de Cerámica y Vidrio, 50(1), 49-58. doi:10.3989/cyv.072011 es_ES
dc.description.references Albornoz, V. M., M. González-Araya, M. C. Gripe, and S. V. Rodrıguez. 2014. “A Mixed Integer Linear Program for Operational Planning in a Meat Packing Plant.” Accessed January 15, 2015. http://www.researchgate.net/profile/Victor_Albornoz/publication/268687089_A_Mixed_Integer_Linear_Program_for_Operational_Planning_in_a_Meat_Packing_Plant/links/547382bf0cf29afed60f55c7.pdf. es_ES
dc.description.references José Alem, D., & Morabito, R. (2012). Production planning in furniture settings via robust optimization. Computers & Operations Research, 39(2), 139-150. doi:10.1016/j.cor.2011.02.022 es_ES
dc.description.references Alemany, M. M. E., Lario, F.-C., Ortiz, A., & Gómez, F. (2013). Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: An illustration of a ceramic case. Applied Mathematical Modelling, 37(5), 3380-3398. doi:10.1016/j.apm.2012.07.022 es_ES
dc.description.references Alemany, M., Ortiz, A., & Fuertes-Miquel, V. S. (2018). A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company. Computers & Industrial Engineering, 122, 219-234. doi:10.1016/j.cie.2018.05.040 es_ES
dc.description.references Alfalla-Luque, R., Medina-Lopez, C., & Dey, P. K. (2012). Supply chain integration framework using literature review. Production Planning & Control, 24(8-9), 800-817. doi:10.1080/09537287.2012.666870 es_ES
dc.description.references Al-Othman, W. B. E., Lababidi, H. M. S., Alatiqi, I. M., & Al-Shayji, K. (2008). Supply chain optimization of petroleum organization under uncertainty in market demands and prices. European Journal of Operational Research, 189(3), 822-840. doi:10.1016/j.ejor.2006.06.081 es_ES
dc.description.references Al-Shammari, A., & Ba-Shammakh, M. S. (2011). Uncertainty Analysis for Refinery Production Planning. Industrial & Engineering Chemistry Research, 50(11), 7065-7072. doi:10.1021/ie200313r es_ES
dc.description.references Amaro, A. C. S., & Barbosa-Póvoa, A. P. F. D. (2009). The effect of uncertainty on the optimal closed-loop supply chain planning under different partnerships structure. Computers & Chemical Engineering, 33(12), 2144-2158. doi:10.1016/j.compchemeng.2009.06.003 es_ES
dc.description.references ARAS, N., BOYACI, T., & VERTER, V. (2004). The effect of categorizing returned products in remanufacturing. IIE Transactions, 36(4), 319-331. doi:10.1080/07408170490279561 es_ES
dc.description.references Aydin, R., Kwong, C. K., Geda, M. W., & Okudan Kremer, G. E. (2017). Determining the optimal quantity and quality levels of used product returns for remanufacturing under multi-period and uncertain quality of returns. The International Journal of Advanced Manufacturing Technology, 94(9-12), 4401-4414. doi:10.1007/s00170-017-1141-0 es_ES
dc.description.references Bakhrankova, K., Midthun, K. T., & Uggen, K. T. (2014). Stochastic optimization of operational production planning for fisheries. Fisheries Research, 157, 147-153. doi:10.1016/j.fishres.2014.03.018 es_ES
dc.description.references Banasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. A. J. (2017). Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain. International Journal of Production Economics, 183, 409-420. doi:10.1016/j.ijpe.2016.08.012 es_ES
dc.description.references Beaudoin, D., LeBel, L., & Frayret, J.-M. (2007). Tactical supply chain planning in the forest products industry through optimization and scenario-based analysis. Canadian Journal of Forest Research, 37(1), 128-140. doi:10.1139/x06-223 es_ES
dc.description.references Begen, M. A., & Puterman, M. L. (2003). Development Of A Catch Allocation Tool Design For Production Planning At Js Mcmillan Fisheries. INFOR: Information Systems and Operational Research, 41(3), 235-244. doi:10.1080/03155986.2003.11732678 es_ES
dc.description.references Benedito, E., & Corominas, A. (2010). Optimal manufacturing and remanufacturing capacities of systems with reverse logistics and deterministic uniform demand. Journal of Industrial Engineering and Management, 3(1). doi:10.3926/jiem.2010.v3n1.p33-53 es_ES
dc.description.references Bertrand, J. W. ., & Rutten, W. G. M. . (1999). Evaluation of three production planning procedures for the use of recipe flexibility. European Journal of Operational Research, 115(1), 179-194. doi:10.1016/s0377-2217(98)00166-0 es_ES
dc.description.references Björheden, R., & Helstad, K. (2005). Raw Material Procurement in Sawmills’ Business Level Strategy-A Contingency Perspective. International Journal of Forest Engineering, 16(2), 47-56. doi:10.1080/14942119.2005.10702513 es_ES
dc.description.references Bohle, C., Maturana, S., & Vera, J. (2010). A robust optimization approach to wine grape harvesting scheduling. European Journal of Operational Research, 200(1), 245-252. doi:10.1016/j.ejor.2008.12.003 es_ES
dc.description.references Cai, X., Lai, M., Li, X., Li, Y., & Wu, X. (2014). Optimal acquisition and production policy in a hybrid manufacturing/remanufacturing system with core acquisition at different quality levels. European Journal of Operational Research, 233(2), 374-382. doi:10.1016/j.ejor.2013.07.017 es_ES
dc.description.references Carneiro, M. C., Ribas, G. P., & Hamacher, S. (2010). Risk Management in the Oil Supply Chain: A CVaR Approach. Industrial & Engineering Chemistry Research, 49(7), 3286-3294. doi:10.1021/ie901265n es_ES
dc.description.references Chakraborty, M., & Chandra, M. K. (2005). Multicriteria decision making for optimal blending for beneficiation of coal: a fuzzy programming approach. Omega, 33(5), 413-418. doi:10.1016/j.omega.2004.07.005 es_ES
dc.description.references LUO, C., & RONG, G. (2009). A Strategy for the Integration of Production Planning and Scheduling in Refineries under Uncertainty. Chinese Journal of Chemical Engineering, 17(1), 113-127. doi:10.1016/s1004-9541(09)60042-2 es_ES
dc.description.references Davoli, G., Gallo, S., Collins, M., & Melloni, R. (2011). A stochastic simulation approach for production scheduling and investment planning in the tile industry. International Journal of Engineering, Science and Technology, 2(9). doi:10.4314/ijest.v2i9.64006 es_ES
dc.description.references Denizel, M., Ferguson, M., & Souza, G. (2010). Multiperiod Remanufacturing Planning With Uncertain Quality of Inputs. IEEE Transactions on Engineering Management, 57(3), 394-404. doi:10.1109/tem.2009.2024506 es_ES
dc.description.references Dong, M., Lu, S., & Han, S. (2011). Production Planning for Hybrid Remanufacturing and Manufacturing System with Component Recovery. Advances in Electrical Engineering and Electrical Machines, 511-518. doi:10.1007/978-3-642-25905-0_66 es_ES
dc.description.references Dubois, D., Fargier, H., & Fortemps, P. (2003). Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge. European Journal of Operational Research, 147(2), 231-252. doi:10.1016/s0377-2217(02)00558-1 es_ES
dc.description.references DUENYAS, I., & TSAI, C.-Y. (2000). Control of a manufacturing system with random product yield and downward substitutability. IIE Transactions, 32(9), 785-795. doi:10.1080/07408170008967438 es_ES
dc.description.references Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. doi:10.1080/00207543.2018.1447706 es_ES
dc.description.references French, M. L., & LaForge, R. L. (2005). Closed-loop supply chains in process industries: An empirical study of producer re-use issues. Journal of Operations Management, 24(3), 271-286. doi:10.1016/j.jom.2004.07.012 es_ES
dc.description.references Gallo, M., R. Grisi, G. Guizzi, and E. Romano. 2009. “A Comparison of Production Policies in Remanufacturing Systems,” Proceedings of the 8th WSEAS International Conference on System Science and Simulation in Engineering, ICOSSSE ‘09, pp. 334. es_ES
dc.description.references Goodfellow, R., & Dimitrakopoulos, R. (2017). Simultaneous Stochastic Optimization of Mining Complexes and Mineral Value Chains. Mathematical Geosciences, 49(3), 341-360. doi:10.1007/s11004-017-9680-3 es_ES
dc.description.references Graves, S. C. (2010). Uncertainty and Production Planning. Planning Production and Inventories in the Extended Enterprise, 83-101. doi:10.1007/978-1-4419-6485-4_5 es_ES
dc.description.references Grillo, H., Alemany, M. M. E., Ortiz, A., & Fuertes-Miquel, V. S. (2017). Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Applied Mathematical Modelling, 49, 255-278. doi:10.1016/j.apm.2017.04.037 es_ES
dc.description.references Guan, Z., & Philpott, A. B. (2011). A multistage stochastic programming model for the New Zealand dairy industry. International Journal of Production Economics, 134(2), 289-299. doi:10.1016/j.ijpe.2009.11.003 es_ES
dc.description.references Guide, V. D. R. (2000). Production planning and control for remanufacturing: industry practice and research needs. Journal of Operations Management, 18(4), 467-483. doi:10.1016/s0272-6963(00)00034-6 es_ES
dc.description.references Gupta, V., & Grossmann, I. E. (2011). Solution strategies for multistage stochastic programming with endogenous uncertainties. Computers & Chemical Engineering, 35(11), 2235-2247. doi:10.1016/j.compchemeng.2010.11.013 es_ES
dc.description.references Gupta, S., and Z. Nan. 2006. “‘Multiperiod Planning of Refinery Operations Under Market Uncertainty,’ AIChE Annual Meeting.” Conference Proceedings. es_ES
dc.description.references Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk – Definition, measure and modeling. Omega, 52, 119-132. doi:10.1016/j.omega.2014.10.004 es_ES
dc.description.references Heydari, J., & Ghasemi, M. (2018). A revenue sharing contract for reverse supply chain coordination under stochastic quality of returned products and uncertain remanufacturing capacity. Journal of Cleaner Production, 197, 607-615. doi:10.1016/j.jclepro.2018.06.206 es_ES
dc.description.references Hovelaque, V., Duvaleix-Tréguer, S., & Cordier, J. (2009). Effects of constrained supply and price contracts on agricultural cooperatives. European Journal of Operational Research, 199(3), 769-780. doi:10.1016/j.ejor.2008.08.005 es_ES
dc.description.references Hsieh, S., & Chiang, C.-C. (2001). Manufacturing-to-Sale Planning Model for Fuel Oil Production. The International Journal of Advanced Manufacturing Technology, 18(4), 303-311. doi:10.1007/s001700170070 es_ES
dc.description.references Igarashi, M., de Boer, L., & Fet, A. M. (2013). What is required for greener supplier selection? A literature review and conceptual model development. Journal of Purchasing and Supply Management, 19(4), 247-263. doi:10.1016/j.pursup.2013.06.001 es_ES
dc.description.references Jamshidi, M., & Osanloo, M. (2019). Reliability analysis of production schedule in multi-element deposits under grade-tonnage uncertainty with multi-destinations for the run of mine material. International Journal of Mining Science and Technology, 29(3), 483-489. doi:10.1016/j.ijmst.2018.04.016 es_ES
dc.description.references Jin, X., Hu, S. J., Ni, J., & Xiao, G. (2013). Assembly Strategies for Remanufacturing Systems With Variable Quality Returns. IEEE Transactions on Automation Science and Engineering, 10(1), 76-85. doi:10.1109/tase.2012.2217741 es_ES
dc.description.references Jindal, A., & Sangwan, K. S. (2016). Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors. Annals of Operations Research, 257(1-2), 95-120. doi:10.1007/s10479-016-2219-z es_ES
dc.description.references Johnson, P., G. Evatt, P. Duck, and S. Howell. 2010. “The Derivation and Impact of an Optimal Cut-off Grade Regime Upon Mine Valuations,” Proceedings of the World Congress on Engineering 2010 Vol I. es_ES
dc.description.references Junior, M. L., & Filho, M. G. (2011). Production planning and control for remanufacturing: literature review and analysis. Production Planning & Control, 23(6), 419-435. doi:10.1080/09537287.2011.561815 es_ES
dc.description.references Kamrad, B., & Ernst, R. (2001). An Economic Model for Evaluating Mining and Manufacturing Ventures with Output Yield Uncertainty. Operations Research, 49(5), 690-699. doi:10.1287/opre.49.5.690.10610 es_ES
dc.description.references Kannegiesser, M., Günther, H.-O., van Beek, P., Grunow, M., & Habla, C. (2008). Value chain management for commodities: a case study from the chemical industry. OR Spectrum, 31(1), 63-93. doi:10.1007/s00291-008-0124-9 es_ES
dc.description.references Karabuk, S. (2008). Production planning under uncertainty in textile manufacturing. Journal of the Operational Research Society, 59(4), 510-520. doi:10.1057/palgrave.jors.2602370 es_ES
dc.description.references Khor, C. S., Elkamel, A., & Douglas, P. L. (2008). Stochastic Refinery Planning with Risk Management. Petroleum Science and Technology, 26(14), 1726-1740. doi:10.1080/10916460701287813 es_ES
dc.description.references Kumral, M. (2004). Genetic algorithms for optimization of a mine system under uncertainty. Production Planning & Control, 15(1), 34-41. doi:10.1080/09537280310001654844 es_ES
dc.description.references Lalmazloumian, M., and K. Y. Wong. 2012. “A Review of Modelling Approaches for Supply Chain Planning Under Uncertainty,” Service Systems and Service Management (ICSSSM), 2012 9th International Conference on, pp. 197. es_ES
dc.description.references Leiras, A., Ribas, G., Hamacher, S., & Elkamel, A. (2013). Tactical and Operational Planning of Multirefinery Networks under Uncertainty: An Iterative Integration Approach. Industrial & Engineering Chemistry Research, 52(25), 8507-8517. doi:10.1021/ie302835n es_ES
dc.description.references Liao, H., Deng, Q., & Wang, Y. (2017). Optimal Acquisition and Production Policy for End-of-Life Engineering Machinery Recovering in a Joint Manufacturing/Remanufacturing System under Uncertainties in Procurement and Demand. Sustainability, 9(3), 338. doi:10.3390/su9030338 es_ES
dc.description.references Loomba, A. P. S., & Nakashima, K. (2011). Enhancing value in reverse supply chains by sorting before product recovery. Production Planning & Control, 23(2-3), 205-215. doi:10.1080/09537287.2011.591652 es_ES
dc.description.references Macedo, P. B., Alem, D., Santos, M., Junior, M. L., & Moreno, A. (2015). Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs. The International Journal of Advanced Manufacturing Technology, 82(5-8), 1241-1257. doi:10.1007/s00170-015-7445-z es_ES
dc.description.references Martinez, L. 2009. “Why Accounting for Uncertainty and Risk Can Improve Final Decision-Making in Strategic Open Pit Mine Evaluation.” Project Evaluation Conference, Melbourne, pp. 1. es_ES
dc.description.references Matamoros, M. E. V., & Dimitrakopoulos, R. (2016). Stochastic short-term mine production schedule accounting for fleet allocation, operational considerations and blending restrictions. European Journal of Operational Research, 255(3), 911-921. doi:10.1016/j.ejor.2016.05.050 es_ES
dc.description.references Meredith, J. (1993). Theory Building through Conceptual Methods. International Journal of Operations & Production Management, 13(5), 3-11. doi:10.1108/01443579310028120 es_ES
dc.description.references Miller, W. A., Leung, L. C., Azhar, T. M., & Sargent, S. (1997). Fuzzy production planning model for fresh tomato packing. International Journal of Production Economics, 53(3), 227-238. doi:10.1016/s0925-5273(97)00110-2 es_ES
dc.description.references Mitra, K. (2009). Multiobjective optimization of an industrial grinding operation under uncertainty. Chemical Engineering Science, 64(23), 5043-5056. doi:10.1016/j.ces.2009.08.012 es_ES
dc.description.references Moghaddam, K. S. (2015). Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert Systems with Applications, 42(15-16), 6237-6254. doi:10.1016/j.eswa.2015.02.010 es_ES
dc.description.references Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390. doi:10.1016/j.ejor.2009.09.008 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 MUNDI, I., ALEMANY, M. M. E., BOZA, A., & POLER, R. (2013). A Model-Driven Decision Support System for the Master Planning of Ceramic Supply Chains with Non-uniformity of Finished Goods. Studies in Informatics and Control, 22(2). doi:10.24846/v22i2y201305 es_ES
dc.description.references Mundi, M. I., Alemany, M. M. E., Poler, R., & Fuertes-Miquel, V. S. (2016). Fuzzy sets to model master production effectively in Make to Stock companies with Lack of Homogeneity in the Product. Fuzzy Sets and Systems, 293, 95-112. doi:10.1016/j.fss.2015.06.009 es_ES
dc.description.references Munhoz, J. R., & Morabito, R. (2014). Optimization approaches to support decision making in the production planning of a citrus company: A Brazilian case study. Computers and Electronics in Agriculture, 107, 45-57. doi:10.1016/j.compag.2014.05.016 es_ES
dc.description.references Olivetti, E. A., Gaustad, G. G., Field, F. R., & Kirchain, R. E. (2011). Increasing Secondary and Renewable Material Use: A Chance Constrained Modeling Approach To Manage Feedstock Quality Variation. Environmental Science & Technology, 45(9), 4118-4126. doi:10.1021/es103486s es_ES
dc.description.references Osmani, A., & Zhang, J. (2013). Stochastic optimization of a multi-feedstock lignocellulosic-based bioethanol supply chain under multiple uncertainties. Energy, 59, 157-172. doi:10.1016/j.energy.2013.07.043 es_ES
dc.description.references Paksoy, T., Pehlivan, N. Y., & Özceylan, E. (2012). Application of fuzzy optimization to a supply chain network design: A case study of an edible vegetable oils manufacturer. Applied Mathematical Modelling, 36(6), 2762-2776. doi:10.1016/j.apm.2011.09.060 es_ES
dc.description.references Pauls-Worm, K. G. J., Hendrix, E. M. T., Haijema, R., & van der Vorst, J. G. A. J. (2014). An MILP approximation for ordering perishable products with non-stationary demand and service level constraints. International Journal of Production Economics, 157, 133-146. doi:10.1016/j.ijpe.2014.07.020 es_ES
dc.description.references Peidro, D., Mula, J., Alemany, M. M. E., & Lario, F.-C. (2012). Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. International Journal of Production Research, 50(11), 3011-3020. doi:10.1080/00207543.2011.588267 es_ES
dc.description.references Peidro, D., Mula, J., Jiménez, M., & del Mar Botella, M. (2010). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205(1), 65-80. doi:10.1016/j.ejor.2009.11.031 es_ES
dc.description.references Peidro, D., Mula, J., Poler, R., & Lario, F.-C. (2008). Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3-4), 400-420. doi:10.1007/s00170-008-1715-y es_ES
dc.description.references Pendharkar, P. C. (1997). A fuzzy linear programming model for production planning in coal mines. Computers & Operations Research, 24(12), 1141-1149. doi:10.1016/s0305-0548(97)00024-5 es_ES
dc.description.references Pendharkar, P. C. (2013). Scatter search based interactive multi-criteria optimization of fuzzy objectives for coal production planning. Engineering Applications of Artificial Intelligence, 26(5-6), 1503-1511. doi:10.1016/j.engappai.2013.01.001 es_ES
dc.description.references Pieter van Donk, D. (2000). Customer‐driven manufacturing in the food processing industry. British Food Journal, 102(10), 739-747. doi:10.1108/00070700010362176 es_ES
dc.description.references Pitty, S. S., Li, W., Adhitya, A., Srinivasan, R., & Karimi, I. A. (2008). Decision support for integrated refinery supply chains. Computers & Chemical Engineering, 32(11), 2767-2786. doi:10.1016/j.compchemeng.2007.11.006 es_ES
dc.description.references Poles, R., and F. Cheong. 2009. “A System Dynamics Model for Reducing Uncertainty in Remanufacturing Systems,” PACIS 2009–13th Pacific Asia Conference on Information Systems: IT Services in a Global Environment. es_ES
dc.description.references Pongsakdi, A., Rangsunvigit, P., Siemanond, K., & Bagajewicz, M. J. (2006). Financial risk management in the planning of refinery operations. International Journal of Production Economics, 103(1), 64-86. doi:10.1016/j.ijpe.2005.04.007 es_ES
dc.description.references Radulescu, M., G. Zbaganu, and C. Z. Radulescu. 2008. “Crop Planning in the Presence of Production Quotas (Invited Paper),” Computer Modeling and Simulation, 2008.UKSIM 2008. Tenth International Conference on, pp. 549. es_ES
dc.description.references Rajaram, K., & Karmarkar, U. S. (2002). Product Cycling With Uncertain Yields: Analysis and Application to the Process Industry. Operations Research, 50(4), 680-691. doi:10.1287/opre.50.4.680.2867 es_ES
dc.description.references Ramasesh, R. V., & Browning, T. R. (2014). A conceptual framework for tackling knowable unknown unknowns in project management. Journal of Operations Management, 32(4), 190-204. doi:10.1016/j.jom.2014.03.003 es_ES
dc.description.references Randhawa, S. U., & Bjarnason, E. T. (1995). A decision aid for coordinating fishing and fish processing. European Journal of Operational Research, 81(1), 62-75. doi:10.1016/0377-2217(93)e0132-h es_ES
dc.description.references Rastogi, A. P., Fowler, J. W., Matthew Carlyle, W., Araz, O. M., Maltz, A., & Büke, B. (2011). Supply network capacity planning for semiconductor manufacturing with uncertain demand and correlation in demand considerations. International Journal of Production Economics, 134(2), 322-332. doi:10.1016/j.ijpe.2009.11.006 es_ES
dc.description.references Ravi, V., & Reddy, P. J. (1998). Fuzzy linear fractional goal programming applied to refinery operations planning. Fuzzy Sets and Systems, 96(2), 173-182. doi:10.1016/s0165-0114(96)00294-1 es_ES
dc.description.references Ribas, G. P., Hamacher, S., & Street, A. (2010). Optimization under uncertainty of the integrated oil supply chain using stochastic and robust programming. International Transactions in Operational Research, 17(6), 777-796. doi:10.1111/j.1475-3995.2009.00756.x es_ES
dc.description.references Rico-Ramirez, V., I. E. Grossmann, B. Tarhan, S. Hernandez-Castro, and J. G. Segovia-Hernandez. 2009. “An Approach to the Representation of Gradual Uncertainty Resolution in Stochastic Multiperiod Planning,” 19th European Symposium on Computer Aided Process Engineering, vol. 26, pp. 713–718. es_ES
dc.description.references Rocco, C. D., & Morabito, R. (2016). Production and logistics planning in the tomato processing industry: A conceptual scheme and mathematical model. Computers and Electronics in Agriculture, 127, 763-774. doi:10.1016/j.compag.2016.08.002 es_ES
dc.description.references Roitsch, M., & Meyr, H. (s. f.). Oil Industry. Supply Chain Management and Advanced Planning, 399-414. doi:10.1007/978-3-540-74512-9_22 es_ES
dc.description.references Rong, A., & Lahdelma, R. (2008). Fuzzy chance constrained linear programming model for optimizing the scrap charge in steel production. European Journal of Operational Research, 186(3), 953-964. doi:10.1016/j.ejor.2007.02.017 es_ES
dc.description.references Schütz, P., & Tomasgard, A. (2011). The impact of flexibility on operational supply chain planning. International Journal of Production Economics, 134(2), 300-311. doi:10.1016/j.ijpe.2009.11.004 es_ES
dc.description.references Sel, Ç., Bilgen, B., & Bloemhof-Ruwaard, J. (2017). Planning and scheduling of the make-and-pack dairy production under lifetime uncertainty. Applied Mathematical Modelling, 51, 129-144. doi:10.1016/j.apm.2017.06.002 es_ES
dc.description.references Sel, Ç., Pınarbaşı, M., Soysal, M., & Çimen, M. (2017). A green model for the catering industry under demand uncertainty. Journal of Cleaner Production, 167, 459-472. doi:10.1016/j.jclepro.2017.08.100 es_ES
dc.description.references Seth, N., Deshmukh, S. G., & Vrat, P. (2006). A conceptual model for quality of service in the supply chain. International Journal of Physical Distribution & Logistics Management, 36(7), 547-575. doi:10.1108/09600030610684971 es_ES
dc.description.references Seuring, S., & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699-1710. doi:10.1016/j.jclepro.2008.04.020 es_ES
dc.description.references Shi, J., Z. Liu, C. Zhu, and Y. Zhang. 2011. “Optimal Production Planning for a Closed Loop System with Quality Uncertainty of Used Product,” Proceedings – 2011 4th International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII 2011, pp. 394. es_ES
dc.description.references Su, T.-S., & Lin, Y.-F. (2015). Fuzzy multi-objective procurement/production planning decision problems for recoverable manufacturing systems. Journal of Manufacturing Systems, 37, 396-408. doi:10.1016/j.jmsy.2014.07.007 es_ES
dc.description.references Tan, B., & Çömden, N. (2012). Agricultural planning of annual plants under demand, maturation, harvest, and yield risk. European Journal of Operational Research, 220(2), 539-549. doi:10.1016/j.ejor.2012.02.005 es_ES
dc.description.references Tarhan, B., Grossmann, I. E., & Goel, V. (2011). Computational strategies for non-convex multistage MINLP models with decision-dependent uncertainty and gradual uncertainty resolution. Annals of Operations Research, 203(1), 141-166. doi:10.1007/s10479-011-0855-x es_ES
dc.description.references Tong, K., Feng, Y., & Rong, G. (2011). Planning under Demand and Yield Uncertainties in an Oil Supply Chain. Industrial & Engineering Chemistry Research, 51(2), 814-834. doi:10.1021/ie200194w es_ES
dc.description.references Van der Vorst, J. G. A. J., & Beulens, A. J. M. (2002). Identifying sources of uncertainty to generate supply chain redesign strategies. International Journal of Physical Distribution & Logistics Management, 32(6), 409-430. doi:10.1108/09600030210437951 es_ES
dc.description.references Van Wezel, W., Van Donk, D. P., & Gaalman, G. (2005). The planning flexibility bottleneck in food processing industries. Journal of Operations Management, 24(3), 287-300. doi:10.1016/j.jom.2004.11.001 es_ES
dc.description.references Vorst, Jacobus Gerardus Adrianus Johannes van der. 2000. “Effective Food Supply Chains: Generating, Modelling and Evaluating Supply Chain Scenarios.” Doctoral thesis, Wageningen Universiteit. es_ES
dc.description.references Wang, J., & Shu, Y.-F. (2005). Fuzzy decision modeling for supply chain management. Fuzzy Sets and Systems, 150(1), 107-127. doi:10.1016/j.fss.2004.07.005 es_ES
dc.description.references Wang, W., and Y. Zhang. 2006. “An Approach of Production Planning for Agile Supply Chain Quality Management,” American Control Conference, 2006 IEEE, pp. 2545. es_ES
dc.description.references Wang, H.-F., & Zheng, K.-W. (2009). An optimal run-mode scheduling for oil refinery production–a case study on Taiwan CPC Corporation. Production Planning & Control, 21(3), 301-318. doi:10.1080/09537280903382845 es_ES
dc.description.references Wazed, M. A., Ahmed, S., & Nukman, Y. B. (2011). Mathematical models for process commonality under quality and resources breakdown in multistage production. Journal of Zhejiang University-SCIENCE A, 12(11), 837-848. doi:10.1631/jzus.a1000477 es_ES
dc.description.references Wu, C.-H., Lin, J. T., & Wu, H.-H. (2009). Robust production and transportation planning in thin film transistor-liquid crystal display (TFT-LCD) industry under demand and price uncertainties. International Journal of Production Research, 48(20), 6037-6060. doi:10.1080/00207540903176697 es_ES
dc.description.references Xiao, R., Cai, Z., & Zhang, X. (2012). A production optimization model of supply-driven chain with quality uncertainty. Journal of Systems Science and Systems Engineering, 21(2), 144-160. doi:10.1007/s11518-011-5184-8 es_ES
dc.description.references Yazıcı, E., Büyüközkan, G., & Baskak, M. (2016). A New Extended MILP MRP Approach to Production Planning and Its Application in the Jewelry Industry. Mathematical Problems in Engineering, 2016, 1-18. doi:10.1155/2016/7915673 es_ES
dc.description.references Kazemi Zanjani, M., Ait-Kadi, D., & Nourelfath, M. (2010). Robust production planning in a manufacturing environment with random yield: A case in sawmill production planning. European Journal of Operational Research, 201(3), 882-891. doi:10.1016/j.ejor.2009.03.041 es_ES
dc.description.references Zanjani, M. K., Kadi, D. A., & Nourelfath, M. (2013). A stochastic programming approach for sawmill production planning. International Journal of Mathematics in Operational Research, 5(1), 1. doi:10.1504/ijmor.2013.050604 es_ES
dc.description.references Zanjani, M. K., Ait-Kadi, D., & Nourelfath, M. (2013). An accelerated scenario updating heuristic for stochastic production planning with set-up constraints in sawmills. International Journal of Production Research, 51(4), 993-1005. doi:10.1080/00207543.2012.661088 es_ES
dc.description.references Kazemi Zanjani, M., Nourelfath, M., & Ait-Kadi, D. (2009). A multi-stage stochastic programming approach for production planning with uncertainty in the quality of raw materials and demand. International Journal of Production Research, 48(16), 4701-4723. doi:10.1080/00207540903055727 es_ES
dc.description.references Kazemi Zanjani, M., Nourelfath, M., & Ait-Kadi, D. (2011). Production planning with uncertainty in the quality of raw materials: a case in sawmills. Journal of the Operational Research Society, 62(7), 1334-1343. doi:10.1057/jors.2010.30 es_ES
dc.description.references Zeballos, L. J., Gomes, M. I., Barbosa-Povoa, A. P., & Novais, A. Q. (2012). Addressing the uncertain quality and quantity of returns in closed-loop supply chains. Computers & Chemical Engineering, 47, 237-247. doi:10.1016/j.compchemeng.2012.06.034 es_ES
dc.description.references Zhang, J., Wen, Y., & Xu, Q. (2012). Simultaneous Optimization of Crude Oil Blending and Purchase Planning with Delivery Uncertainty Consideration. Industrial & Engineering Chemistry Research, 51(25), 8453-8464. doi:10.1021/ie102499p es_ES
dc.description.references Zimberg, B., & Testuri, C. E. (2006). Stochastic analysis of crude oil procurement and processing under uncertain demand for bunker fuel oil. International Transactions in Operational Research, 13(5), 387-402. doi:10.1111/j.1475-3995.2006.00554.x es_ES


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