Hybrid meta-heuristic algorithms for a supply chain network considering different carbon emission regulations using big data characteristics
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[1] Mohammad Bagher Fakhrzad,et al. A new multi-objective mathematical model for a Citrus supply chain network design: Metaheuristic algorithms , 2021 .
[2] Ajith Abraham,et al. A biobjective home health care logistics considering the working time and route balancing: a self-adaptive social engineering optimizer , 2020, J. Comput. Des. Eng..
[3] R. Chierici,et al. Supply chain management in the era of circular economy: the moderating effect of big data , 2020 .
[4] Hamed Fazlollahtabar,et al. Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry , 2020, Comput. Ind. Eng..
[5] Hamed Fazlollahtabar,et al. An integrated fuzzy-genetic failure mode and effect analysis for aircraft wing reliability , 2020, Soft Computing.
[6] Jurgita Antucheviciene,et al. A new soft computing approach for green supplier selection problem with interval type-2 trapezoidal fuzzy statistical group decision and avoidance of information loss , 2020, Soft Comput..
[7] Jesús Muñuzuri,et al. A multi-objective pharmaceutical supply chain network based on a robust fuzzy model: A comparison of meta-heuristics , 2020, Appl. Soft Comput..
[8] H. Fazlollahtabar,et al. A robust fuzzy stochastic programming for sustainable procurement and logistics under hybrid uncertainty using big data , 2020 .
[9] Arun Kumar Sangaiah,et al. Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem , 2020, Soft Comput..
[10] Naoufel Cheikhrouhou,et al. A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment , 2020, Applied Soft Computing.
[11] Sami Kara,et al. Manufacturing big data ecosystem: A systematic literature review , 2020, Robotics Comput. Integr. Manuf..
[12] Kim Hua Tan,et al. An analytic infrastructure for harvesting big data to enhance supply chain performance , 2020, Eur. J. Oper. Res..
[13] Navid Sahebjamnia,et al. Optimization of Multi-period Three-echelon Citrus Supply Chain Problem , 2020 .
[14] Xifan Yao,et al. Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems , 2020, Appl. Soft Comput..
[15] Salih O. Duffuaa,et al. A tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks , 2020, Expert Syst. Appl..
[16] Duc Truong Pham,et al. Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time , 2020, Int. J. Prod. Res..
[17] Angappa Gunasekaran,et al. Big data-driven supply chain performance measurement system: a review and framework for implementation , 2019, Int. J. Prod. Res..
[18] Hao Wang,et al. Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies , 2019, Enterp. Inf. Syst..
[19] M. Fakhrzad,et al. A Multi-objective Sustainable Medicine Supply Chain Network Design Using a Novel Hybrid Multi-objective Metaheuristic Algorithm , 2020 .
[20] Mostafa Hajiaghaei-Keshteli,et al. Determination of the optimal sales level of perishable goods in a two-echelon supply chain network , 2020, Comput. Ind. Eng..
[21] Mohammad Mahdavi Mazdeh,et al. Applying meta-heuristic algorithms for an integrated production-distribution problem in a two level supply chain , 2020 .
[22] Sadegh Niroomand,et al. Adaptive meta-heuristic algorithms for flexible supply chain network design problem with different delivery modes , 2019, Comput. Ind. Eng..
[23] Shivam Gupta,et al. Big data in humanitarian supply chain management: a review and further research directions , 2017, Annals of Operations Research.
[24] Limin Wang,et al. Fast artificial bee colony algorithm with complex network and naive bayes classifier for supply chain network management , 2019, Soft Comput..
[25] Shahriar Akter,et al. Big data and disaster management: a systematic review and agenda for future research , 2017, Annals of Operations Research.
[26] Yogesh Kumar Dwivedi,et al. Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda , 2019, Int. J. Inf. Manag..
[27] M. Ben-Daya,et al. Internet of things and supply chain management: a literature review , 2019, Int. J. Prod. Res..
[28] Fariba Goodarzian,et al. A Fuzzy multi-objective programming approach to develop a green closed-loop supply chain network design problem under uncertainty: Modifications of imperialist competitive algorithm , 2019, RAIRO Oper. Res..
[29] Fariba Goodarzian,et al. Applying a fuzzy multi-objective model for a production–distribution network design problem by using a novel self-adoptive evolutionary algorithm , 2019, International Journal of Systems Science: Operations & Logistics.
[30] Keqin Li,et al. Complex network oriented artificial bee colony algorithm for global bi-objective optimization in three-echelon supply chain , 2019, Appl. Soft Comput..
[31] Pan Liu. Pricing policies and coordination of low-carbon supply chain considering targeted advertisement and carbon emission reduction costs in the big data environment , 2019, Journal of Cleaner Production.
[32] Ravi Shankar,et al. Multi-criteria evaluation of real-time key performance indicators of supply chain with consideration of big data architecture , 2019, Comput. Ind. Eng..
[33] Bee Wah Yap,et al. The state of the art and taxonomy of big data analytics: view from new big data framework , 2019, Artificial Intelligence Review.
[34] Amir-Mohammad Golmohammadi,et al. Addressing a fixed charge transportation problem with multi-route and different capacities by novel hybrid meta-heuristics , 2019 .
[35] Young-bin Woo,et al. A genetic algorithm-based matheuristic for hydrogen supply chain network problem with two transportation modes and replenishment cycles , 2019, Comput. Ind. Eng..
[36] Morteza Rasti-Barzoki,et al. Mathematical programming and solution approaches for minimizing tardiness and transportation costs in the supply chain scheduling problem , 2019, Comput. Ind. Eng..
[37] G. Antoniou,et al. Supply chain risk management and artificial intelligence: state of the art and future research directions , 2018, Int. J. Prod. Res..
[38] M. G. Ravetti,et al. A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem , 2017, Eur. J. Oper. Res..
[39] C. Udaya Kiran,et al. Modeling Elastic Constants of Keratin-Based Hair Fiber Composite Using Response Surface Method and Optimization Using Grey Taguchi Method , 2019, Advanced Engineering Optimization Through Intelligent Techniques.
[40] Robiah Ahmad,et al. A New Modified Firefly Algorithm for Optimizing a Supply Chain Network Problem , 2018, Applied Sciences.
[41] Ali Tajdin,et al. A closed-loop supply chain robust optimization for disposable appliances , 2018, Neural Computing and Applications.
[42] F. Goodarzian,et al. Mathematical Formulation and Solving of Green Closed-loop Supply Chain Planning Problem with Production, Distribution and Transportation Reliability , 2018, International Journal of Engineering.
[43] Madjid Tavana,et al. Multi-stage supply chain network solution methods: hybrid metaheuristics and performance measurement , 2018 .
[44] Tsan-Ming Choi,et al. Big Data Analytics in Operations Management , 2018 .
[45] Reza Ramezanian,et al. An efficient hybrid genetic algorithm for multi-product competitive supply chain network design with price-dependent demand , 2018, Appl. Soft Comput..
[46] Michail N. Giannakos,et al. Big data analytics capabilities: a systematic literature review and research agenda , 2017, Information Systems and e-Business Management.
[47] Surya Prakash Singh,et al. Modeling big data enablers for operations and supply chain management , 2018 .
[48] Mohammad Hossein Fazel Zarandi,et al. Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks , 2018, J. Intell. Manuf..
[49] Iraj Mahdavi,et al. A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development , 2018 .
[50] Petros Ieromonachou,et al. Big data analytics in supply chain management: A state-of-the-art literature review , 2017, Comput. Oper. Res..
[51] Harpreet Kaur,et al. Heuristic modeling for sustainable procurement and logistics in a supply chain using big data , 2017, Comput. Oper. Res..
[52] Sunil Tiwari,et al. Big data analytics in supply chain management between 2010 and 2016: Insights to industries , 2018, Comput. Ind. Eng..
[53] Jaroslaw Wikarek,et al. A constraint-driven approach to food supply chain management , 2017, Ind. Manag. Data Syst..
[54] Marleen Huysman,et al. Debating big data: A literature review on realizing value from big data , 2017, J. Strateg. Inf. Syst..
[55] Rui Yan,et al. Optimization approach for increasing revenue of perishable product supply chain with the Internet of Things , 2017, Ind. Manag. Data Syst..
[56] In Lee,et al. Big data: Dimensions, evolution, impacts, and challenges , 2017 .
[57] Sungzoon Cho,et al. Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data , 2017, Ind. Manag. Data Syst..
[58] Shib Sankar Sana,et al. A Hybrid Artificial Neural Network with Metaheuristic Algorithms for Predicting Stock Price , 2017, Cybern. Syst..
[59] Zhihan Lv,et al. Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics , 2017, IEEE Transactions on Industrial Informatics.
[60] S. H. Nasseri,et al. A metaheuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development , 2017 .
[61] Z. Irani,et al. Critical analysis of Big Data challenges and analytical methods , 2017 .
[62] Ardeshir Bahreininejad,et al. A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network , 2014, Journal of Intelligent Manufacturing.
[63] Mehdi Seifbarghy,et al. Solving a fuzzy multi objective model of a production–distribution system using meta-heuristic based approaches , 2017, J. Intell. Manuf..
[64] Çagri Koç,et al. An evolutionary algorithm for supply chain network design with assembly line balancing , 2017, Neural Computing and Applications.
[65] Athanasios V. Vasilakos,et al. Big data: From beginning to future , 2016, Int. J. Inf. Manag..
[66] Petri T. Helo,et al. Big data applications in operations/supply-chain management: A literature review , 2016, Comput. Ind. Eng..
[67] Hamidreza Maghsoudlou,et al. Bi-objective optimization of a three-echelon multi-server supply-chain problem in congested systems: Modeling and solution , 2016, Comput. Ind. Eng..
[68] Krishnendu Shaw,et al. Low carbon chance constrained supply chain network design problem: a Benders decomposition based approach , 2016, Comput. Ind. Eng..
[69] Mohammad Ranjbar,et al. A bi-objective model for integrated scheduling of production and distribution in a supply chain with order release date restrictions , 2016 .
[70] M. Sanei,et al. Solving a multi-stage multi-product solid supply chain network design problem by meta-heuristics , 2016 .
[71] A. Gunasekaran,et al. Big data analytics in logistics and supply chain management: Certain investigations for research and applications , 2016 .
[72] Qingyu Zhang,et al. Big data analytics with swarm intelligence , 2016, Ind. Manag. Data Syst..
[73] M. Hilbert,et al. Big Data for Development: A Review of Promises and Challenges , 2016 .
[74] Xu Chen,et al. Big data research for the knowledge economy: past, present, and future , 2015, Industrial management & data systems.
[75] Tsan-Ming Choi,et al. Managing disruption risk in express logistics via proactive planning , 2015, Ind. Manag. Data Syst..
[76] Ardeshir Bahreininejad,et al. Optimizing a location allocation-inventory problem in a two-echelon supply chain network: A modified fruit fly optimization algorithm , 2015, Comput. Ind. Eng..
[77] Kannan Govindan,et al. Dynamic supply chain network design with capacity planning and multi-period pricing ☆ , 2015 .
[78] Mehdi Seifbarghy,et al. A four-echelon supply chain network design with shortage: Mathematical modeling and solution methods , 2015 .
[79] Murtaza Haider,et al. Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..
[80] Alireza Alinezhad,et al. Presenting a Bi-objective Integrated Model for Production-Distribution Problem in a Multi-level Supply Chain Network , 2015 .
[81] Seyed Taghi Akhavan Niaki,et al. A bi-objective integrated procurement, production, and distribution problem of a multi-echelon supply chain network design: A new tuned MOEA , 2015, Comput. Oper. Res..
[82] Roger Zetter,et al. State of the art literature review , 2011 .
[83] Russell C. Eberhart,et al. A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.
[84] Kalyanmoy Deb,et al. Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..
[85] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.