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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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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

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Título: Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model
Autor: Mundi, I. Alemany Díaz, María Del Mar Poler, R. Fuertes-Miquel, Vicente S.
Entidad UPV: Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses
Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Planning , Optimisation , Production , Uncertainty , Mathematical modelling
Derechos de uso: Reserva de todos los derechos
Fuente:
International Journal of Production Research. (issn: 0020-7543 )
DOI: 10.1080/00207543.2019.1566665
Editorial:
Taylor & Francis
Versión del editor: https://doi.org/10.1080/00207543.2019.1566665
Código del Proyecto:
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/
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/
Agradecimientos:
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 ...[+]
Tipo: Artículo

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

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

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 [+]
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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

Gupta, S., and Z. Nan. 2006. “‘Multiperiod Planning of Refinery Operations Under Market Uncertainty,’ AIChE Annual Meeting.” Conference Proceedings.

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

Kumral, M. (2004). Genetic algorithms for optimization of a mine system under uncertainty. Production Planning & Control, 15(1), 34-41. doi:10.1080/09537280310001654844

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.

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

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

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

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

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.

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

Meredith, J. (1993). Theory Building through Conceptual Methods. International Journal of Operations & Production Management, 13(5), 3-11. doi:10.1108/01443579310028120

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Pieter van Donk, D. (2000). Customer‐driven manufacturing in the food processing industry. British Food Journal, 102(10), 739-747. doi:10.1108/00070700010362176

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

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.

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

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.

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

Vorst, Jacobus Gerardus Adrianus Johannes van der. 2000. “Effective Food Supply Chains: Generating, Modelling and Evaluating Supply Chain Scenarios.” Doctoral thesis, Wageningen Universiteit.

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

Wang, W., and Y. Zhang. 2006. “An Approach of Production Planning for Agile Supply Chain Quality Management,” American Control Conference, 2006 IEEE, pp. 2545.

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

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

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

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

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

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

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

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

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

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

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

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

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

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