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

[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

Differential evolution algorithm for multi-commodity and multi-level of service hub covering location problem

The hub location problem involves a network of origins and destinations over which transportation takes place. There are many studies associated with finding the location of hub nodes and the allocation of demand nodes to these located hub nodes to transfer the only one kind of commodity under one level of service. However, in this study, carrying different commodity types from origin to destination under various levels of services (e.g. price, punctuality, reliability or transit time) is studied. Quality of services experienced by users such as speed, convenience, comfort and security of transportation facilities and services is considered as the level of service. In each system, different kinds of commodities with various levels of services can be transmitted. The appropriate level of service that a commodity can be transmitted through is chosen by customer preferences and the specification of the commodity. So, a mixed integer programming formulation for single allocation hub covering location problem, which is based on the idea of transferring multi commodity flows under multi levels of service is presented. These two are applied concepts, multi-commodity and multi-level of service, which make the model's assumptions closer to the real world problems. In addition, a differential evolution algorithm is designed to find near-optimal solutions. The obtained solutions using differential evolution (DE) algorithm (upper bound), where its parameters are tuned by response surface methodology, are compared with exact solutions and computed lower bounds by linear relaxation technique to prove the efficiency of proposed DE algorithm