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    A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain

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    [EN] The challenges of global economies foster supply chains to have to increase their processes of collaboration and dependence between their nodes, generating an increase in the level of vulnerability to possible impacts and interruptions in their operations that may affect their sustainability. This has developed an emerging area of interest in supply chain management, considering resilience management as a strategic capability of companies, and causing an increase in this area of research. Additionally, supply chains should deal with the three dimensions of sustainability (economic, environmental, and social dimensions) by incorporating the three types of objectives in their strategy. Thus, there is a need to integrate both resilience and sustainability in supply chain management to increase competitiveness. In this paper, a systematic literature review is undertaken to analyze resilience management and its connection to increase supply chain sustainability. In the review, 232 articles published from 2000 to February 2020 in peer-reviewed journals in the Scopus and ScienceDirect databases are analyzed, classified, and synthesized. With the results, this paper develops a conceptual framework that integrates the fundamental elements for analyzing, measuring, and managing resilience to increase sustainability in the supply chain. Finally, conclusions, limitations, and future research lines are exposed.This study was supported by the Valencian Government in Spain (Project AEST/2019/019).Zavala-AlcĂ­var, A.; Verdecho SĂĄez, MJ.; Alfaro Saiz, JJ. (2020). A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain. Sustainability. 12(16):1-38. https://doi.org/10.3390/su12166300S1381216Roberta Pereira, C., Christopher, M., & Lago Da Silva, A. (2014). Achieving supply chain resilience: the role of procurement. Supply Chain Management: An International Journal, 19(5/6), 626-642. doi:10.1108/scm-09-2013-0346Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). ENSURING SUPPLY CHAIN RESILIENCE: DEVELOPMENT OF A CONCEPTUAL FRAMEWORK. Journal of Business Logistics, 31(1), 1-21. doi:10.1002/j.2158-1592.2010.tb00125.xPettit, T. J., Croxton, K. L., & Fiksel, J. (2013). Ensuring Supply Chain Resilience: Development and Implementation of an Assessment Tool. Journal of Business Logistics, 34(1), 46-76. doi:10.1111/jbl.12009Ponis, S. T., & Koronis, E. (2012). Supply Chain Resilience: Definition Of Concept And Its Formative Elements. Journal of Applied Business Research (JABR), 28(5), 921. doi:10.19030/jabr.v28i5.7234Seuring, S., & MĂŒller, 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.2008.04.020Qorri, A., Mujkić, Z., & Kraslawski, A. (2018). A conceptual framework for measuring sustainability performance of supply chains. Journal of Cleaner Production, 189, 570-584. doi:10.1016/j.jclepro.2018.04.073Verdecho, M.-J., AlarcĂłn-Valero, F., PĂ©rez-Perales, D., Alfaro-Saiz, J.-J., & RodrĂ­guez-RodrĂ­guez, R. (2020). A methodology to select suppliers to increase sustainability within supply chains. Central European Journal of Operations Research, 29(4), 1231-1251. doi:10.1007/s10100-019-00668-3Edgeman, R., & Wu, Z. (2016). Supply chain criticality in sustainable and resilient enterprises. Journal of Modelling in Management, 11(4), 869-888. doi:10.1108/jm2-10-2014-0078Marchese, D., Reynolds, E., Bates, M. E., Morgan, H., Clark, S. S., & Linkov, I. (2018). Resilience and sustainability: Similarities and differences in environmental management applications. Science of The Total Environment, 613-614, 1275-1283. doi:10.1016/j.scitotenv.2017.09.086Ahern, J. (2012). Urban landscape sustainability and resilience: the promise and challenges of integrating ecology with urban planning and design. Landscape Ecology, 28(6), 1203-1212. doi:10.1007/s10980-012-9799-zRamezankhani, M. J., Torabi, S. A., & Vahidi, F. (2018). Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach. Computers & Industrial Engineering, 126, 531-548. doi:10.1016/j.cie.2018.09.054Shashi, Centobelli, P., Cerchione, R., & Ertz, M. (2019). Managing supply chain resilience to pursue business and environmental strategies. Business Strategy and the Environment, 29(3), 1215-1246. doi:10.1002/bse.2428Ivanov, D. (2017). Revealing interfaces of supply chain resilience and sustainability: a simulation study. International Journal of Production Research, 56(10), 3507-3523. doi:10.1080/00207543.2017.1343507Fahimnia, B., & Jabbarzadeh, A. (2016). Marrying supply chain sustainability and resilience: A match made in heaven. Transportation Research Part E: Logistics and Transportation Review, 91, 306-324. doi:10.1016/j.tre.2016.02.007Ruiz-Benitez, R., LĂłpez, C., & Real, J. C. (2019). Achieving sustainability through the lean and resilient management of the supply chain. International Journal of Physical Distribution & Logistics Management, 49(2), 122-155. doi:10.1108/ijpdlm-10-2017-0320Pavlov, A., Ivanov, D., Pavlov, D., & Slinko, A. (2019). Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics. Annals of Operations Research. doi:10.1007/s10479-019-03182-6Khot, S. B., & Thiagarajan, S. (2019). Resilience and sustainability of supply chain management in the Indian automobile industry. International Journal of Data and Network Science, 339-348. doi:10.5267/j.ijdns.2019.4.002Roostaie, S., Nawari, N., & Kibert, C. J. (2019). Sustainability and resilience: A review of definitions, relationships, and their integration into a combined building assessment framework. Building and Environment, 154, 132-144. doi:10.1016/j.buildenv.2019.02.042Davoudabadi, R., Mousavi, S. M., & Sharifi, E. (2020). An integrated weighting and ranking model based on entropy, DEA and PCA considering two aggregation approaches for resilient supplier selection problem. Journal of Computational Science, 40, 101074. doi:10.1016/j.jocs.2019.101074Carvalho, H., Duarte, S., & Cruz Machado, V. (2011). Lean, agile, resilient and green: divergencies and synergies. International Journal of Lean Six Sigma, 2(2), 151-179. doi:10.1108/20401461111135037Wang, Z., & Zhang, J. (2019). Agent-based evaluation of humanitarian relief goods supply capability. International Journal of Disaster Risk Reduction, 36, 101105. doi:10.1016/j.ijdrr.2019.101105Alikhani, R., Torabi, S. A., & Altay, N. (2019). Strategic supplier selection under sustainability and risk criteria. International Journal of Production Economics, 208, 69-82. doi:10.1016/j.ijpe.2018.11.018Zahiri, B., Zhuang, J., & Mohammadi, M. (2017). Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study. Transportation Research Part E: Logistics and Transportation Review, 103, 109-142. doi:10.1016/j.tre.2017.04.009Aboah, J., Wilson, M. M. J., Rich, K. M., & Lyne, M. C. (2019). Operationalising resilience in tropical agricultural value chains. Supply Chain Management: An International Journal, 24(2), 271-300. doi:10.1108/scm-05-2018-0204Statsenko, L., & Corral de Zubielqui, G. (2020). Customer collaboration, service firms’ diversification and innovation performance. Industrial Marketing Management, 85, 180-196. doi:10.1016/j.indmarman.2019.09.013Duong, L. N. K., & Chong, J. (2020). Supply chain collaboration in the presence of disruptions: a literature review. International Journal of Production Research, 58(11), 3488-3507. doi:10.1080/00207543.2020.1712491Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: the concept, a literature review and future directions. International Journal of Production Research, 49(18), 5375-5393. doi:10.1080/00207543.2011.563826Heckmann, 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.2014.10.004Hohenstein, N.-O., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the phenomenon of supply chain resilience. International Journal of Physical Distribution & Logistics Management, 45(1/2), 90-117. doi:10.1108/ijpdlm-05-2013-0128Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171, 116-133. doi:10.1016/j.ijpe.2015.10.023Ali, A., Mahfouz, A., & Arisha, A. (2017). Analysing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review. Supply Chain Management: An International Journal, 22(1), 16-39. doi:10.1108/scm-06-2016-0197Umar, M., Wilson, M., & Heyl, J. (2017). Food Network Resilience Against Natural Disasters: A Conceptual Framework. SAGE Open, 7(3), 215824401771757. doi:10.1177/2158244017717570Stone, J., & Rahimifard, S. (2018). Resilience in agri-food supply chains: a critical analysis of the literature and synthesis of a novel framework. Supply Chain Management: An International Journal, 23(3), 207-238. doi:10.1108/scm-06-2017-0201Colicchia, C., Creazza, A., NoĂš, C., & Strozzi, F. (2019). Information sharing in supply chains: a review of risks and opportunities using the systematic literature network analysis (SLNA). Supply Chain Management: An International Journal, 24(1), 5-21. doi:10.1108/scm-01-2018-0003Annarelli, A., & Nonino, F. (2016). Strategic and operational management of organizational resilience: Current state of research and future directions. Omega, 62, 1-18. doi:10.1016/j.omega.2015.08.004Behzadi, G., O’Sullivan, M. J., Olsen, T. L., & Zhang, A. (2018). Agribusiness supply chain risk management: A review of quantitative decision models. Omega, 79, 21-42. doi:10.1016/j.omega.2017.07.005Kochan, C. G., & Nowicki, D. R. (2018). Supply chain resilience: a systematic literature review and typological framework. International Journal of Physical Distribution & Logistics Management, 48(8), 842-865. doi:10.1108/ijpdlm-02-2017-0099Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285-307. doi:10.1016/j.tre.2019.03.001Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14(3), 207-222. doi:10.1111/1467-8551.00375Rousseau, D. M., Manning, J., & Denyer, D. (2008). 11 Evidence in Management and Organizational Science: Assembling the Field’s Full Weight of Scientific Knowledge Through Syntheses. Academy of Management Annals, 2(1), 475-515. doi:10.5465/19416520802211651Zimmer, K., Fröhling, M., & Schultmann, F. (2015). Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. International Journal of Production Research, 54(5), 1412-1442. doi:10.1080/00207543.2015.1079340Natarajarathinam, M., Capar, I., & Narayanan, A. (2009). Managing supply chains in times of crisis: a review of literature and insights. International Journal of Physical Distribution & Logistics Management, 39(7), 535-573. doi:10.1108/09600030910996251Tang, C., & Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks. International Journal of Production Economics, 116(1), 12-27. doi:10.1016/j.ijpe.2008.07.008Kleindorfer, P. R., & Saad, G. H. (2009). Managing Disruption Risks in Supply Chains. Production and Operations Management, 14(1), 53-68. doi:10.1111/j.1937-5956.2005.tb00009.xChristopher, M., & Peck, H. (2004). Building the Resilient Supply Chain. The International Journal of Logistics Management, 15(2), 1-14. doi:10.1108/09574090410700275Wu, T., Huang, S., Blackhurst, J., Zhang, X., & Wang, S. (2013). Supply Chain Risk Management: An Agent-Based Simulation to Study the Impact of Retail Stockouts. IEEE Transactions on Engineering Management, 60(4), 676-686. doi:10.1109/tem.2012.2190986Fang, H., & Xiao, R. (2013). Resilient closed-loop supply chain network design based on patent protection. International Journal of Computer Applications in Technology, 48(1), 49. doi:10.1504/ijcat.2013.055566Gong, J., Mitchell, J. E., Krishnamurthy, A., & Wallace, W. A. (2014). An interdependent layered network model for a resilient supply chain. Omega, 46, 104-116. doi:10.1016/j.omega.2013.08.002Mari, S., Lee, Y., & Memon, M. (2014). Sustainable and Resilient Supply Chain Network Design under Disruption Risks. Sustainability, 6(10), 6666-6686. doi:10.3390/su6106666Bueno-Solano, A., & Cedillo-Campos, M. G. (2014). Dynamic impact on global supply chains performance of disruptions propagation produced by terrorist acts. Transportation Research Part E: Logistics and Transportation Review, 61, 1-12. doi:10.1016/j.tre.2013.09.005Costantino, F., Gravio, G. D., Shaban, A., & Tronci, M. (2014). Replenishment policy based on information sharing to mitigate the severity of supply chain disruption. International Journal of Logistics Systems and Management, 18(1), 3. doi:10.1504/ijlsm.2014.062119Kristianto, Y., Gunasekaran, A., Helo, P., & Hao, Y. (2014). A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path. Expert Systems with Applications, 41(1), 39-49. doi:10.1016/j.eswa.2013.07.009Raj, R., Wang, J. W., Nayak, A., Tiwari, M. K., Han, B., Liu, C. L., & Zhang, W. J. (2015). Measuring the Resilience of Supply Chain Systems Using a Survival Model. IEEE Systems Journal, 9(2), 377-381. doi:10.1109/jsyst.2014.2339552LOH, H. S., & THAI, V. V. (2015). Cost Consequences of a Port-Related Supply Chain Disruption. The Asian Journal of Shipping and Logistics, 31(3), 319-340. doi:10.1016/j.ajsl.2015.09.001Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 79, 22-48. doi:10.1016/j.tre.2015.03.005Cardoso, S. R., Paula Barbosa-PĂłvoa, A., Relvas, S., & Novais, A. Q. (2015). Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty. Omega, 56, 53-73. doi:10.1016/j.omega.2015.03.008Salehi Sadghiani, N., Torabi, S. A., & Sahebjamnia, N. (2015). Retail supply chain network design under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 75, 95-114. doi:10.1016/j.tre.2014.12.015Dixit, V., Seshadrinath, N., & Tiwari, M. K. (2016). Performance measures based optimization of supply chain network resilience: A NSGA-II + Co-Kriging approach. Computers & Industrial Engineering, 93, 205-214. doi:10.1016/j.cie.2015.12.029Liu, F., Song, J.-S., & Tong, J. D. (2016). Building Supply Chain Resilience through Virtual Stockpile Pooling. Production and Operations Management, 25(10), 1745-1762. doi:10.1111/poms.12573Fahimnia, B., Jabbarzadeh, A., & Sarkis, J. (2018). Greening versus resilience: A supply chain design perspective. Transportation Research Part E: Logistics and Transportation Review, 119, 129-148. doi:10.1016/j.tre.2018.09.005Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation Research Part E: Logistics and Transportation Review, 87, 20-52. doi:10.1016/j.tre.2015.12.009Azhmyakov, V., FernĂĄndez-GutiĂ©rrez, J. P., Gadi, S. K., & Pickl, S. (2016). A Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimization. IFAC-PapersOnLine, 49(31), 137-142. doi:10.1016/j.ifacol.2016.12.175Ivanov, D., Sokolov, B., Solovyeva, I., Dolgui, A., & Jie, F. (2016). Dynamic recovery policies for time-critical supply chains under conditions of ripple effect. International Journal of Production Research, 54(23), 7245-7258. doi:10.1080/00207543.2016.1161253Jabbarzadeh, A., Fahimnia, B., Sheu, J.-B., & Moghadam, H. S. (2016). Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transportation Research Part B: Methodological, 94, 121-149. doi:10.1016/j.trb.2016.09.004Babich, V., Burnetas, A. N., & Ritchken, P. H. (2007). Competition and Diversification Effects in Supply Chains with Supplier Default Risk. Manufacturing & Service Operations Management, 9(2), 123-146. doi:10.1287/msom.1060.0122Bogataj, D., Aver, B., & Bogataj, M. (2016). Supply chain risk at simultaneous robust perturbations. International Journal of Production Economics, 181, 68-78. doi:10.1016/j.ijpe.2015.09.009Wang, X., Herty, M., & Zhao, L. (2015). Contingent rerouting for enhancing supply chain resilience from supplier behavior perspective. International Transactions in Operational Research, 23(4), 775-796. doi:10.1111/itor.12151Zeng, B., & Yen, B. P.-C. (2017). Rethinking the role of partnerships in global supply chains: A risk-based perspective. International Journal of Production Economics, 185, 52-62. doi:10.1016/j.ijpe.2016.12.004LĂŒcker, F., & Seifert, R. W. (2017). Building up Resilience in a Pharmaceutical Supply Chain through Inventory, Dual Sourcing and Agility Capacity. Omega, 73, 114-124. doi:10.1016/j.omega.2017.01.001Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2017). Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transportation Research Part E: Logistics and Transportation Review, 101, 176-200. doi:10.1016/j.tre.2017.02.004Kırılmaz, O., & Erol, S. (2017). A proactive approach to supply chain risk management: Shifting orders among suppliers to mitigate the supply side risks. Journal of Purchasing and Supply Management, 23(1), 54-65. doi:10.1016/j.pursup.2016.04.002Li, H., Pedrielli, G., Lee, L. H., & Chew, E. P. (2016). Enhancement of supply chain resilience through inter-echelon information sharing. Flexible Services and Manufacturing Journal, 29(2), 260-285. doi:10.1007/s10696-016-9249-3Otto, C., Willner, S. N., Wenz, L., Frieler, K., & Levermann, A. (2017). Modeling loss-propagation in the global supply network: The dynamic agent-based model acclimate. Journal of Economic Dynamics and Control, 83, 232-269. doi:10.1016/j.jedc.2017.08.001Rezapour, S., Farahani, R. Z., & Pourakbar, M. (2017). Resilient supply chain network design under competition: A case study. European Journal of Operational Research, 259(3), 1017-1035. doi:10.1016/j.ejor.2016.11.041Ledwoch, A., Yasarcan, H., & Brintrup, A. (2018). The moderating impact of supply network topology on the effectiveness of risk management. International Journal of Production Economics, 197, 13-26. doi:10.1016/j.ijpe.2017.12.013Al-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.2006.06.081Ivanov, D., Dolgui, A., & Sokolov, B. (2017). Scheduling of recovery actions in the supply chain with resilience analysis considerations. International Journal of Production Research, 56(19), 6473-6490. doi:10.1080/00207543.2017.1401747Das, K. (2019). Integrating Lean, Green, and Resilience Criteria in a Sustainable Food Supply Chain Planning Model. International Journal of Mathematical, Engineering and Management Sciences, 4(2), 259-275. doi:10.33889/ijmems.2019.4.2-022Das, K. (2018). Integrating resilience in a supply chain planning model. International Journal of Quality & Reliability Management, 35(3), 570-595. doi:10.1108/ijqrm-08-2016-0136Arora, V., & Ventresca, M. (2018). Modeling topologically resilient supply chain networks. Applied Network Science, 3(1). doi:10.1007/s41109-018-0070-7Almeida, J. F. de F., Conceição, S. V., Pinto, L. R., de Camargo, R. S., & JĂșnior, G. de M. (2018). Flexibility evaluation of multiechelon supply chains. PLOS ONE, 13(3), e0194050. doi:10.1371/journal.pone.0194050Mancheri, N. A., Sprecher, B., Deetman, S., Young, S. B., Bleischwitz, R., Dong, L., 
 Tukker, A. (2018). Resilience in the tantalum supply chain. Resources, Conservation and Recycling, 129, 56-69. doi:10.1016/j.resconrec.2017.10.018Namdar, J., Li, X., Sawhney, R., & Pradhan, N. (2017). Supply chain resilience for single and multiple sourcing in the presence of disruption risks. International Journal of Production Research, 56(6), 2339-2360. doi:10.1080/00207543.2017.1370149Rozhkov, M., & Ivanov, D. (2018). CONTINGENCY PRODUCTION-INVENTORY CONTROL POLICY FOR CAPACITY DISRUPTIONS IN THE RETAIL SUPPLY CHAIN WITH PERISHABLE PRODUCTS. IFAC-PapersOnLine, 51(11), 1448-1452. doi:10.1016/j.ifacol.2018.08.311Sabouhi, F., Pishvaee, M. S., & Jabalameli, M. S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain. Computers & Industrial Engineering, 126, 657-672. doi:10.1016/j.cie.2018.10.001Zavitsas, K., Zis, T., & Bell, M. G. H. (2018). The impact of flexible environmental policy on maritime supply chain resilience. Transport Policy, 72, 116-128. doi:10.1016/j.tranpol.2018.09.020Mitra, K., Gudi, R. D., Patwardhan, S. C., & Sardar, G. (2009). Towards resilient supply chains: Uncertainty analysis using fuzzy mathematical programming. Chemical Engineering Research and Design, 87(7), 967-981. doi:10.1016/j.cherd.2008.12.025LĂŒcker, F., Seifert, R. W., & Biçer, I. (2018). Roles of inventory and reserve capacity in mitigating supply chain disruption risk. International Journal of Production Research, 57(4), 1238-1249. doi:10.1080/00207543.2018.15041

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

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    [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.This research was initiated within the framework of the project funded by the Ministerio de EconomĂ­a 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 SkƂodowska-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).Mundi, I.; Alemany DĂ­az, 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/10.1080/00207543.2019.1566665S523952835715-16Ahumada, 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.2012.06.002Ahumada, 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.2008.02.014AlarcĂłn, F., Alemany, M. M. E., Lario, F. C., & Oltra, R. F. (2011). La falta de homogeneidad del producto (FHP) en las empresas cerĂĄmicas y su impacto en la reasignaciĂłn del inventario. BoletĂ­n de la Sociedad Española de CerĂĄmica y Vidrio, 50(1), 49-58. doi:10.3989/cyv.072011Albornoz, V. M., M. GonzĂĄlez-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.JosĂ© 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.2011.02.022Alemany, M. M. E., Lario, F.-C., Ortiz, A., & GĂłmez, 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.2012.07.022Alemany, 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.2018.05.040Alfalla-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:10.1080/09537287.2012.666870Al-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.2006.06.081Al-Shammari, A., & Ba-Shammakh, M. S. (2011). Uncertainty Analysis for Refinery Production Planning. Industrial & Engineering Chemistry Research, 50(11), 7065-7072. doi:10.1021/ie200313rAmaro, A. C. S., & Barbosa-PĂłvoa, 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.2009.06.003ARAS, N., BOYACI, T., & VERTER, V. (2004). The effect of categorizing returned products in remanufacturing. IIE Transactions, 36(4), 319-331. doi:10.1080/07408170490279561Aydin, 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/s00170-017-1141-0Bakhrankova, 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.2014.03.018Banasik, 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.2016.08.012Beaudoin, 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/x06-223Begen, 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:10.1080/03155986.2003.11732678Benedito, 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.p33-53Bertrand, 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)00166-0Björheden, 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:10.1080/14942119.2005.10702513Bohle, 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.2008.12.003Cai, 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.2013.07.017Carneiro, 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/ie901265nChakraborty, 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.2004.07.005LUO, 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)60042-2Davoli, 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.v2i9.64006Denizel, 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.2009.2024506Dong, 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_66Dubois, 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)00558-1DUENYAS, I., & TSAI, C.-Y. (2000). Control of a manufacturing system with random product yield and downward substitutability. IIE Transactions, 32(9), 785-795. doi:10.1080/07408170008967438Esteso, 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:10.1080/00207543.2018.1447706French, 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.2004.07.012Gallo, 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/s11004-017-9680-3Graves, 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_5Grillo, 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.2017.04.037Guan, 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.2009.11.003Guide, 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)00034-6Gupta, 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.2010.11.013Gupta, 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.2014.10.004Heydari, 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.2018.06.206Hovelaque, V., Duvaleix-TrĂ©guer, 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.2008.08.005Hsieh, 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/s001700170070Igarashi, 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.2013.06.001Jamshidi, 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.2018.04.016Jin, 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.2012.2217741Jindal, 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-zJohnson, 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:10.1080/09537287.2011.561815Kamrad, 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.49.5.690.10610Kannegiesser, M., GĂŒnther, 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/s00291-008-0124-9Karabuk, S. (2008). Production planning under uncertainty in textile manufacturing. Journal of the Operational Research Society, 59(4), 510-520. doi:10.1057/palgrave.jors.2602370Khor, C. S., Elkamel, A., & Douglas, P. L. (2008). Stochastic Refinery Planning with Risk Management. Petroleum Science and Technology, 26(14), 1726-1740. doi:10.1080/10916460701287813Kumral, M. (2004). Genetic algorithms for optimization of a mine system under uncertainty. Production Planning & Control, 15(1), 34-41. doi:10.1080/09537280310001654844Lalmazloumian, 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/ie302835nLiao, 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/su9030338Loomba, 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:10.1080/09537287.2011.591652Macedo, 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-zMartinez, 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.2016.05.050Meredith, J. (1993). Theory Building through Conceptual Methods. International Journal of Operations & Production Management, 13(5), 3-11. doi:10.1108/01443579310028120Miller, 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)00110-2Mitra, K. (2009). Multiobjective optimization of an industrial grinding operation under uncertainty. Chemical Engineering Science, 64(23), 5043-5056. doi:10.1016/j.ces.2009.08.012Moghaddam, 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.2015.02.010Mula, J., Peidro, D., DĂ­az-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.2009.09.008Mula, 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.2010.06.007MUNDI, 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/v22i2y201305Mundi, 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.2015.06.009Munhoz, 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.2014.05.016Olivetti, 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/es103486sOsmani, 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.2013.07.043Paksoy, 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.2011.09.060Pauls-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.2014.07.020Peidro, 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:10.1080/00207543.2011.588267Peidro, D., Mula, J., JimĂ©nez, 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.2009.11.031Peidro, 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-yPendharkar, 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)00024-5Pendharkar, 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.2013.01.001Pieter van Donk, D. (2000). Customer‐driven manufacturing in the food processing industry. British Food Journal, 102(10), 739-747. doi:10.1108/00070700010362176Pitty, 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.2007.11.006Poles, 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.2005.04.007Radulescu, 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.50.4.680.2867Ramasesh, R. V., &

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