10 research outputs found

    Assessing and Selecting Sustainable and Resilient Suppliers in Agri-Food Supply Chains Using Artificial Intelligence: A Short Review

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    [EN] The supplier evaluation and selection process is critical to increase the sustainability and resilience of the agri-food supply chain. Therefore, in this sector, it is necessary to consider sustainability and resilience criteria in the supplier evaluation and selection process. The use of arti¿cial intelligence techniques allows managing of a lot of information and the reduction of uncertainty for decision making. The objective of this article is to analyze articles that address the selection of suppliers in agrifood supply chains that pursue to increase their sustainability and resilience by using arti¿cial intelligence techniques to analyze the techniques and criteria used and draw conclusions.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.Zavala-Alcívar, A.; Verdecho Sáez, MJ.; Alfaro Saiz, JJ. (2020). Assessing and Selecting Sustainable and Resilient Suppliers in Agri-Food Supply Chains Using Artificial Intelligence: A Short Review. IFIP Advances in Information and Communication Technology. 598:501-510. https://doi.org/10.1007/978-3-030-62412-5_41S501510598Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S.: Quantitative models for sustainable supply chain management: developments and directions. Eur. J. Oper. Res. 233, 299–312 (2014)Ocampo, L.A., Abad, G.K.M., Cabusas, K.G.L., Padon, M.L.A., Sevilla, N.C.: Recent approaches to supplier selection: a review of literature within 2006–2016. Int. J. Integr. Supply Manage. 12, 22–68 (2018)Valipour, S., Safaei, A.: A resilience approach for supplier selection: using Fuzzy analytic network process and grey VIKOR techniques. J. Clean. Prod. 161, 431–451 (2017)Amindoust, A.: A resilient-sustainable based supplier selection model using a hybrid intelligent method. Comput. Ind. Eng. 126, 122–135 (2018)Zavala-Alcívar, A., Verdecho, M.-J., Alfaro-Saiz, J.-J.: A conceptual framework to manage resilience and increase sustainability in the supply chain. Sustainability 12(16), 6300 (2020)Villalobos, J.R., Soto-Silva, W.E., González-Araya, M.C., González-Ramirez, R.G.: Research directions in technology development to support real-time decisions of fresh produce logistics: A review and research agenda. Comput. Electron. Agric. 167, 105092 (2019)Ristono, A., Santoso, P.B., Tama, I.P.: A literature review of design of criteria for supplier selection. J. Ind. Eng. Manage. 11, 680–696 (2018)Torres-Ruiz, A., Ravindran, A.R.: Multiple criteria framework for the sustainability risk assessment of a supplier portfolio. J. Clean. Prod. 172, 4478–4493 (2018)Setak, M., Sharifi, S., Alimohammadian, A.: Supplier selection and order allocation models in supply chain management: a review. World Appl. Sci. J. 18, 55–72 (2012)Ravindran, A.R., Warsing, D.P.: Supplier selection models and methods. In: Supply Chain Engineering: Models and Applications. Taylor and Francis Group, Boca Raton, Florida (2013)De Boer, L., Labro, E., Morlacchi, P.: A review of methods supporting supplier selection. Eur. J. Purch. Supply Manage. 7, 75–89 (2011)De Felice, F., Deldoost, M.H., Faizollahi, M., Petrillo, A.: Performance measurement model for the supplier selection based on AHP. Int. J. Eng. Bus. Manag. 7, 1–13 (2015)Zimmer, K., Fröhling, M., Schultmann, F.: Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. Int. J. Prod. Res. 54, 1412–1442 (2016)Christopher, M., Peck, H.: Building the resilient supply chain. Int. J. Logist. Manag. 15, 1–14 (2014)Ali, A., Mahfouz, A., Arisha, A.: Analysing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review. Supply Chain Manage. 22, 16–39 (2017)Verdecho, M., Alarcón-Valero, F., Pérez-Perales, D., et al.: A methodology to select suppliers to increase sustainability within supply chains. Cent. Eur. J. Oper. Res. (2020). https://doi.org/10.1007/s10100-019-00668-3Rabelo, L., Bhide, S., Gutierrez, E.: Artificial Intelligence: Advances in Research and Applications. Nova Science Publishers, Inc., Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States (2017)Denyer, D., Tranfield, D.: Producing a systematic review. In: The Sage Handbook of Organizational Research Methods. SAGE Publications Ltd., pp. 671–689 (2019)Chen, Y.-J.: Structured methodology for supplier selection and evaluation in a supply chain. Inf. Sci. (Ny) 181, 1651–1670 (2011)Hamdi, F., Ghorbel, A., Masmoudi, F., Dupont, L.: Optimization of a supply portfolio in the context of supply chain risk management: literature review. J. Intell. Manuf. 29(4), 763–788 (2015). https://doi.org/10.1007/s10845-015-1128-3Kumar, V., Srinivasan, S., Das, S.: Optimal solution for supplier selection based on SMART fuzzy case base approach. In: 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems. SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems. ISIS 2014, Institute of Electrical and Electronics Engineers Inc., Department of Computer Science, IISJ Yokohama, Tokai Chiba, Japan, pp. 386–391 (2014)Jahani, A., Murad, M.A.A., bin Sulaiman, M.N., Selamat, M.H.: An agent-based supplier selection framework: Fuzzy case-based reasoning perspective. Strateg. Outsourcing 8, 180–205 (2015)Wang, Q.: Hybrid knowledge-based flexible supplier selection. In: 8th International Conference on Management of e-Commerce and e-Government. ICMeCG 2014. Institute of Electrical and Electronics Engineers Inc., Department of Information Management, Shanghai Finance University, Shanghai, China, pp. 235–239 (2014)Bai, C., Sarkis, J.: Green supplier development: analytical evaluation using rough set theory. J. Clean. Prod. 18, 1200–1210 (2010)Bai, C., Sarkis, J.: Integrating sustainability into supplier selection with grey system and rough set methodologies. Int. J. Prod. Econ. 124, 252–264 (2010)Guo, F., Lu, Q.: Partner selection optimization model of agricultural enterprises in supply chain. Adv. J. Food Sci. Technol. 5, 1285–1291 (2013)Azadnia, A.H., Saman, M.Z.M., Wong, K.Y.: Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process. Int. J. Prod. Res. 53, 383–408 (2015)Miranda-Ackerman, M.A., Azzaro-Pantel, C., Aguilar-Lasserre, A.A.: A green supply chain network design framework for the processed food industry: application to the orange juice agrofood cluster. Comput. Ind. Eng. 109, 369–389 (2017)Hajikhani, A., Khalilzadeh, M., Sadjadi, S.J.: A fuzzy multi-objective multi-product supplier selection and order-allocation problem in supply chain under coverage and price considerations: an urban agricultural case study. Sci. Iran. 25, 431–449 (2018)Zhang, H., Cui, Y.: A model combining a Bayesian network with a modified genetic algorithm for green supplier selection. Simulation 95, 1165–1183 (2019)Yadav, S., Garg, D., Luthra, S.: Selection of third-party logistics services for internet of things-based agriculture supply chain management. Int. J. Logist. Syst. Manage. 35, 204–230 (2020)Yazdani, M., Wang, Z.X., Chan, F.T.S.: A decision support model based on the combined structure of DEMATEL, QFD and fuzzy values. Soft. Comput. 24(16), 12449–12468 (2020). https://doi.org/10.1007/s00500-020-04685-2Zhang, H., Feng, H., Cui, Y., Wang, Y.: A fuzzy Bayesian network model for quality control in O2O e-commerce. Int. J. Comput. Commun. Control 15(1), (2020). article number 1003. https://doi.org/10.15837/ijccc.2020.1.3783Amiri, S.A.H.S., Zahedi, A., Kazemi, M., Soroor, J., Hajiaghaei-Keshteli, M.: Determination of the optimal sales level of perishable goods in a two-echelon supply chain network. Comput. Ind. Eng. 139, 106156 (2020)Roy, S., et al.: A framework for sustainable supplier selection with transportation criteria. Int. J. Sustain. Eng. 13(2), 77–92 (2020)Parkouhi, S.V., Ghadikolaei, A.S., Lajimi, H.F.: Resilient supplier selection and segmentation in grey environment. J. Clean. Prod. 207, 1123–1137 (2019)Camarinha-Matos, L.M., Afsarmanesh, H., Galeano, N., Molina, A.: Collaborative networked organizations – concepts and practice in manufacturing enterprises. Comput. Ind. Eng. 57, 46–60 (2009)Lezoche, M., Panetto, H., Kacprzyk, J., Hernandez, J., Díaz, M.A.: Agri-food 4.0: a survey of the supply chains and technologies for the future agriculture. Comput. Ind. 117, 103187 (2020)Alikhani, R., Torabi, S., Altay, N.: Strategic supplier selection under sustainability and risk criteria. Int. J. Prod. Econ. 208, 69–82 (2019

    Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events

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    [EN] Agri-food supply chains (AFSCs) are very vulnerable to high risks such as pandemics, causing economic and social impacts mainly on the most vulnerable population. Thus, it is a priority to implement resilient strategies that enable AFSCs to resist, respond and adapt to new market challenges. At the same time, implementing resilient strategies impact on the social, economic and environmental dimensions of sustainability. The objective of this paper is twofold: analyze resilient strategies on AFSCs in the literature and identify how these resilient strategies applied in the face of high risks affect the achievement of sustainability dimensions. The analysis of the articles is carried out in three points: consequences faced by agri-food supply chains due to high risks, strategies applicable in AFSCs, and relationship between resilient strategies and the achievement of sustainability dimensions.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.Zavala-Alcívar, A.; Verdecho Sáez, MJ.; Alfaro Saiz, JJ. (2020). Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events. IFIP Advances in Information and Communication Technology. 598:560-570. https://doi.org/10.1007/978-3-030-62412-5_46S560570598Gray, R.: Agriculture, transportation, and the COVID-19 crisis. Can. J. Agric. Econ. 68, 239–243 (2020)Queiroz, M.M., Ivanov, D., Dolgui, A., Fosso Wamba, S.: Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-020-03685-7Hobbs, J.: Food supply chains during the COVID-19 pandemic. Can. J. Agric. Econ. 68, 171–176 (2020)Shashi, P., Centobelli, P., Cerchione, R., Ertz, M.: Managing supply chain resilience to pursue business and environmental strategies. Bus. Strateg. Environ. 29(3), 1215–1246 (2019)Ivanov, D.: Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp. Res. Part E Logist. Transp. Rev. 136, 101922 (2020)Mamani, H., Chick, S.E., Simchi-Levi, D.: A game-theoretic model of international influenza vaccination coordination. Manage. Sci. 59(7), 1650–1670 (2013)Liu, M., Zhang, D.: A dynamic logistics model for medical resources allocation in an epidemic control with demand forecast updating. J. Oper. Res. Soc. 67, 841–852 (2016)Hessel, L.: Pandemic influenza vaccines: meeting the supply, distribution and deployment challenges. Influenza Other Respir. Viruses 3, 165–170 (2009)Orenstein, W., Schaffner, W.: Lessons learned: role of influenza vaccine production, distribution, supply, and demand—what it means for the provider. Am. J. Med. 121, S22–S27 (2008)Büyüktahtakın, I., Des-Bordes, E., Kıbış, E.: A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa. Eur. J. Oper. Res. 26, 1046–1063 (2018)Anparasan, A., Lejeune, M.: Analyzing the response to epidemics: concept of evidence-based Haddon matrix. J. Humanit. Logist. Supply Chain Manag. 7, 266–283 (2017)Anparasan, A.A., Lejeune, M.A.: Data laboratory for supply chain response models during epidemic outbreaks. Ann. Oper. Res. 270, 53–64 (2018). https://doi.org/10.1007/s10479-017-2462-yAnparasan, A., Lejeune, M.: Resource deployment and donation allocation for epidemic outbreaks. Ann. Oper. Res. 283, 9–32 (2019). https://doi.org/10.1007/s10479-016-2392-0Ivanov, D., Dolgui, A.: Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. Int. J. Prod. Res. 58, 2904–2915 (2020)Ivanov, D.: Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-020-03640-6Ekici, A., Keskinocak, P., Swann, J.: Modeling influenza pandemic and planning food distribution. Manuf. Serv. Oper. Manag. 16, 11–27 (2014)Miranda, R., Schaffner, D.: Virus risk in the food supply chain. Curr. Op. Food Sci. 30, 43–48 (2019)Magalhães, A., Rossi, A., Zattar, I., Marques, M., Seleme, R.: Food traceability technologies and foodborne outbreak occurrences. Br. Food J. 121, 3362–3379 (2019)Denyer, D., Tranfield, D.: Producing a systematic review. In: Buchanan, D., Bryman, A. (eds.) The Sage Handbook of Organizational Research Methods, pp. 671–689. SAGE Publications Ltd., London (2009)Christopher, M., Peck, H.: Building the resilient supply chain. Int. J. Logist. Manag. 15, 1–14 (2004)Dolgui, A., Ivanov, D., Sokolov, B.: Ripple effect in the supply chain: an analysis and recent literature. Int. J. Prod. Res. 56, 414–430 (2018)Jüttner, U., Peck, H., Christopher, M.: Supply chain risk management: outlining an agenda for future research. Int. J. Logist. Res. 6, 197–210 (2003)Behzadi, G., O’Sullivan, M., Olsen, T., Zhang, A.: Agribusiness supply chain risk management: a review of quantitative decision models. Omega (United Kingdom) 79, 21–42 (2018)Kleindorfer, P., Saad, G.: Managing disruption risks in supply chains. Pr. Op. Man. 14, 53–68 (2005)Vishnu, C., Sridharan, R., Gunasekaran, A., Ram Kumar, P.: Strategic capabilities for managing risks in supply chains: current state and research futurities. J. Adv. Manag. Res. 17(2), 173–211 (2019)Deaton, B., Deaton, B.: Food security and Canada’s agricultural system challenged by COVID-19. Can. J. Agric. Econ. 68(2), 143–149 (2020)Richards, T., Rickard, B.: COVID-19 impact on fruit and vegetable markets. C. J. Ag. Ec. 68(2), 189–194 (2020)Larue, B.: Labor issues and COVID-19. Can. J. Agric. Econ. Can. d’agroeconomie (2020). https://doi.org/10.1111/cjag.12233Hollnagel, E.: Epilogue: RAG: the resilience analysis grid. In: Hollnagel, E., Paries, J., Woods, D., Wreathall, J. (eds.) Resilience Engineering in Practice: A Guidebook. Ashgate Pr., pp. 275–296 (2011)Ponomarov, S., Holcomb, M.: Understanding the concept of supply chain resilience. Int. J. Logist. Manag. 20, 124–143 (2009)Wu, T., Huang, S., Blackhurst, J., Zhang, X., Wang, S.: Supply chain risk management: an agent-based simulation to study the impact of retail stockouts. IEEE Trans. Eng. Manag. 60, 676–686 (2013)Schmitt, A., Singh, M.: A quantitative analysis of disruption risk in a multi-echelon supply chain. Int. J. Prod. Econ. 139, 22–32 (2012)Vroegindewey, R., Hodbod, J.: Resilience of agricultural value chains in developing country contexts: a framework and assessment approach. Sustainability 10, 916 (2018)Behzadi, G., O’Sullivan, M., Olsen, T., Scrimgeour, F., Zhang, A.: Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain. Int. J. Prod. Econ. 191, 207–220 (2017)Bottani, E., Murino, T., Schiavo, M., Akkerman, R.: Resilient food supply chain design: modelling framework and metaheuristic solution approach. Comput. Ind. Eng. 135, 177–198 (2019)Meuwissen, M., et al.: A framework to assess the resilience of farming systems. Agric. Syst. 176, 102656 (2019)Dutta, P., Shrivastava, H.: The design and planning of an integrated supply chain for perishable products under uncertainties: a case study in milk industry. J. Model. Manag. (2020). https://doi.org/10.1108/JM2-03-2019-0071Aboah, J., Wilson, M., Rich, M., Lyne, M.: Operationalising resilience in tropical agricultural value chains. Supply Chain Manag. 24, 271–300 (2019)Ravulakollu, A., Urciuoli, L., Rukanova, B., Tan, Y., Hakvoort, R.: Risk based framework for assessing resilience in a complex multi-actor supply chain domain. Supply Chain Forum 19, 266–281 (2018)Das, K.: Integrating lean, green, and resilience criteria in designing a sustainable food supply chain. Proc. Int. Conf. Ind. Eng. Oper. Manag. 2018, 462–473 (2018)Zhu, Q., Krikke, H.: Managing a sustainable and resilient perishable food supply chain (PFSC) after an outbreak. Sustainability 12, 5004 (2020)Rozhkov, M., Ivanov, D.: Contingency production-inventory control policy for capacity disruptions in the retail supply chain with perishable products. IFAC-PapersOnLine 51, 1448–1452 (2018)Yavari, M., Zaker, H.: Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks. Comput. Chem. Eng. 134, 106680 (2020)Ye, F., Hou, G., Li, Y., Fu, S.: Managing bioethanol supply chain resiliency: a risk-sharing model to mitigate yield uncertainty risk. Ind. Manag. Data Syst. 118, 1510–1527 (2018)Jabbarzadeh, A., Fahimnia, B., Sheu, J., Moghadam, H.: Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transp. Res. Part B Methodol. 94, 121–149 (2016)O’Leary, D.: Evolving information systems and technology research issues for COVID-19 and other pandemics. J. Organ. Comput. Electron. Commer. 30, 1–8 (2020)Zavala-Alcívar, A., Verdecho, M.-J., Alfaro-Saiz, J.-J.: A conceptual framework to manage resilience and increase sustainability in the supply chain. Sustainability 12(16), 6300 (2020)Fahimni, B., Jabbarzadeh, A.: Marrying supply chain sustainability and resilience: a match made in heaven. Transp. Res. Part E Logist. Transp. Rev. 91, 306–324 (2016)Verdecho, M.-J., Alarcón-Valero, F., Pérez-Perales, D., Alfaro-Saiz, J.-J., Rodríguez-Rodríguez, R.: A methodology to select suppliers to increase sustainability within supply chains. CEJOR (2020). https://doi.org/10.1007/s10100-019-00668-3Bai, C., Sarkis, J.: Integrating sustainability into supplier selection with grey system and rough set methodologies. Int. J. Prod. Econ. 124(1), 252–264 (2010)Bai, C., Sarkis, J.: Green supplier development: analytical evaluation using rough set theory. J. Clean. Prod. 18, 1200–1210 (2010)Valipour, S., Safaei, A., Fallah, H.: Resilient supplier selection and segmentation in grey environment. J. Clean. Prod. 207, 1123–1137 (2019)Zimmer, K., Fröhling, M., Schultmann, F.: Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. Int. J. Prod. Res. 54, 1412–1442 (2016)Yang, S., Xiao, Y., Kuo, Y.: The supply chain design for perishable food with stochastic demand. Sustainability 9, 1195 (2017)Zahiri, B., Zhuang, J., Mohammadi, M.: Toward an integrated sustainable-resilient supply chain: a pharmaceutical case study. Transp. Res. Part E Logist. Transp. Rev. 103, 109–142 (2017)Duong, L., Chong, J.: Supply chain collaboration in the presence of disruptions: a literature review. Int. J. Prod. Res. 58, 3488–3507 (2020

    Sexo y dolor: la satisfacción sexual y la función sexual en una muestra de pacientes con dolor crónico benigno no pélvico

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    Introduction: Most of research are conclusive when stating chronic pain and decreased life's quality relationship. However, sexual factor is not usually analysed in a specific path. Desing and methods: The aim of this study is to undertake a bibliographic review divided by analgesic groups in order to determine in a certain way the painkiller influence over the sexual response, and in a second place, to describe the connection between pain and sexual response in not-oncological chronic pain patients. Results: Results show that there is a high prevalence of sexual difficulties in patients of the Pain Units. These difficulties are related to psychological alterations, patients' type of pain and age. Conclusions: These results suggest that a multidisciplinary intervention, centred on the exhaustive evaluation of this problem, health education and sexual counselling, could contribute to the improvement of the quality of life of our patients.Introducción: La mayoría de los estudios son concluyentes al trazar la relación entre dolor crónico y disminución significativa de la calidad de vida. No obstante, no suele analizarse el factor sexual de una forma específica. Material y método: El objetivo de este estudio consiste en realizar una revisión bibliográfica pormenorizada por grupos analgésicos para determinar de modo concreto la influencia del fármaco sobre la respuesta sexual y, en segundo lugar, describir la asociación entre dolor y respuesta sexual en pacientes con dolor crónico no oncológico. Resultados: Los resultados reflejan que hay una alta prevalencia de dificultades sexuales en pacientes de las Unidades de Dolor. Estas dificultades están relacionadas con alteraciones psicológicas, con la tipología del dolor, con la edad y con el sexo de los pacientes. Conclusiones: Estos resultados sugieren que desde las Unidades de Dolor se podría realizar una intervención multidisciplinar centrada en la valoración exhaustiva de esta problemática, educación sanitaria y asesoramiento en materia sexual, que contribuyese a la mejora de la calidad de vida de nuestros pacientes

    Desarrollo y validación de un sistema de posturografía online empleando plataformas de presión de bajo coste

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    Noé, E.; Llorens Rodríguez, R.; Colomer, C.; Grau Latorre, J.; Verdecho, I.; Baldoví, A.; Rodríguez, C.... (2016). Desarrollo y validación de un sistema de posturografía online empleando plataformas de presión de bajo coste. Revista de Neurologia. 62(5):235-235. http://hdl.handle.net/10251/81834S23523562
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