Hybrid meta-heuristic algorithms for optimising a sustainable agricultural supply chain network considering CO₂ emissions and water consumption

References

• 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. https://doi.org/10.1016/j.ejor.2008.02.014
   Web of Science ®Google Scholar
• Ahumada, O., Villalobos, J. R., & Mason, A. N. (2012). Tactical planning of the production and distribution of fresh agricultural products under uncertainty. Agricultural Systems, 112, 17–26. https://doi.org/10.1016/j.agsy.2012.06.002
   Web of Science ®Google Scholar
• Allaoui, H., Guo, Y., Choudhary, A., & Bloemhof, J. (2018). Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Computers & Operations Research, 89, 369–384. https://doi.org/10.1016/j.cor.2016.10.012
   Web of Science ®Google Scholar
• Aloui, A., Hamani, N., Derrouiche, R., & Delahoche, L. (2021). Assessing the benefits of horizontal collaboration using an integrated planning model for two-echelon energy efficiency-oriented logistics networks design. International Journal of Systems Science: Operations & Logistics, 1–22. https://doi.org/10.1080/23302674.2021.1887397
   Google Scholar
• Amorim, P., Günther, H.-O., & Almada-Lobo, B. (2012). Multi-objective integrated production and distribution planning of perishable products. International Journal of Production Economics, 138(1), 89–101. https://doi.org/10.1016/j.ijpe.2012.03.005
   Web of Science ®Google Scholar
• Arigoni, A. (2016). Optimization techniques in coal markets: A global cost minimization and a multi-stage procurement strategy. Colorado School of Mines.
   Google Scholar
• Asgari, N., Farahani, R. Z., Rashidi-Bajgan, H., & Sajadieh, M. S. (2013). Developing model-based software to optimise wheat storage and transportation: A real-world application. Applied Soft Computing, 13(2), 1074–1084. https://doi.org/10.1016/j.asoc.2012.10.002
   Web of Science ®Google Scholar
• Audsley, E., & Sandars, D. L. (2009). A review of the practice and achievements from 50 years of applying OR to agricultural systems in Britain. OR Insight, 22(1), 2–18. https://doi.org/10.1057/ori.2008.1
   Google Scholar
• Blum, C., & Roli, A. (2008). Hybrid metaheuristics: An introduction. In Hybrid metaheuristics (pp. 1–30). Springer.
   Google Scholar
• Bortolini, M., Faccio, M., Ferrari, E., Gamberi, M., & Pilati, F. (2016). Fresh food sustainable distribution: Cost, delivery time and carbon footprint three-objective optimization. Journal of Food Engineering, 174, 56–67. https://doi.org/10.1016/j.jfoodeng.2015.11.014
   Web of Science ®Google Scholar
• Bottani, E., Murino, T., Schiavo, M., & Akkerman, R. (2019). Resilient food supply chain design: Modelling framework and meta-heuristic solution approach. Computers & Industrial Engineering, 135, 177–198. https://doi.org/10.1016/j.cie.2019.05.011
   Web of Science ®Google Scholar
• Camacho-Vallejo, J. F., López-Vera, L., Smith, A. E., & González-Velarde, J. L. (2021). A tabu search algorithm to solve a Green logistics bi-objective bi-level problem. Annals of Operations Research, 12(4), 1–27. https://link.springer.com/article/10.1007/s10479-021-04195-w
   Google Scholar
• Che, Z. H. (2012). A particle swarm optimization algorithm for solving unbalanced supply chain planning problems. Applied Soft Computing, 12(4), 1279–1287. https://doi.org/10.1016/j.asoc.2011.12.006
   Web of Science ®Google Scholar
• Cheraghalipour, A., Paydar, M. M., & Hajiaghaei-Keshteli, M. (2018). A bi-objective optimization for citrus closed-loop supply chain using Pareto-based algorithms. Applied Soft Computing, 69, 33–59. https://doi.org/10.1016/j.asoc.2018.04.022
   Web of Science ®Google Scholar
• Cheraghalipour, A., Paydar, M. M., & Hajiaghaei-Keshteli, M. (2019). Designing and solving a bi-level model for rice supply chain using the evolutionary algorithms. Computers and Electronics in Agriculture, 162, 651–668. https://doi.org/10.1016/j.compag.2019.04.041
   Web of Science ®Google Scholar
• Chouhan, V. K., Khan, S. H., & Hajiaghaei-Keshteli, M. (2021). Metaheuristic approaches to design and address multi-echelon sugarcane closed-loop supply chain network. Soft Computing, 25(16), 11377–11404. https://doi.org/10.1007/s00500-021-05943-7
   Web of Science ®Google Scholar
• Delfani, F., Kazemi, A., SeyedHosseini, S. M., & Niaki, S. T. A. (2020). A novel robust possibilistic programming approach for the hazardous waste location-routing problem considering the risks of transportation and population. International Journal of Systems Science: Operations & Logistics, 8(4), 383–395. https://doi.org/10.1080/23302674.2020.1781954
   Google Scholar
• Dwivedi, A., Jha, A., Prajapati, D., Sreenu, N., & Pratap, S. (2020). Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem. Modern Supply Chain Research and Applications, 2(3), 161–177. https://doi.org/10.1108/MSCRA-04-2020-0007
   Google Scholar
• Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. MHS’95, Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan (pp. 39–43). https://doi.org/10.1109/MHS.1995.494215
   Google Scholar
• Fakhrzad, M. B., & Goodarzian, F. (2021). A new multi-objective mathematical model for a citrus supply chain network design: Metaheuristic algorithms. Journal of Optimization in Industrial Engineering, 14(2), 127–144. https://doi.org/10.22094/JOIE.2020.570636.1571
   Google Scholar
• Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 45, 92–118. https://doi.org/10.1016/j.omega.2013.08.006
   Web of Science ®Google Scholar
• Fatemi Ghomi, S. M. T., Karimi, B., Behnamian, J., & Firoozbakht, J. (2021). A multi-objective particle swarm optimization based on pareto archive for integrated production and distribution planning in A Green supply chain. Applied Artificial Intelligence, 35(2), 133–153. https://doi.org/10.1080/08839514.2020.1840197
   Web of Science ®Google Scholar
• Ferrer, J. C., Mac Cawley, A., Maturana, S., Toloza, S., & Vera, J. (2008). An optimization approach for scheduling wine grape harvest operations. International Journal of Production Economics, 112(2), 985–999. https://doi.org/10.1016/j.ijpe.2007.05.020
   Web of Science ®Google Scholar
• Ghasemzadeh, Z., Sadeghieh, A., & Shishebori, D. (2021). A stochastic multi-objective closed-loop global supply chain concerning waste management: A case study of the tire industry. Environment, Development and Sustainability, 23(4), 5794–5821. https://doi.org/10.1007/s10668-020-00847-2
   Web of Science ®Google Scholar
• Ghosh, P. K., Manna, A. K., Dey, J. K., & Kar, S. (2021). Supply chain coordination model for green product with different payment strategies: A game theoretic approach. Journal of Cleaner Production, 290, 125734. https://doi.org/10.1016/j.jclepro.2020.125734
   Web of Science ®Google Scholar
• Glover, F., & Taillard, E. (1993). A user’s guide to tabu search. Annals of Operations Research, 41(1), 1–28. https://doi.org/10.1007/BF02078647
   Google Scholar
• González-Araya, M. C., Soto-Silva, W. E., & Espejo, L. G. A. (2015). Harvest planning in apple orchards using an optimization model. In Handbook of operations research in agriculture and the agri-food industry (pp. 79–105). Springer. https://link.springer.com/chapter/10.1007/978-1-4939-2483-7_4
   Google Scholar
• Goodarzian, F., & Hosseini-Nasab, H. (2021). Applying a fuzzy multi-objective model for a production–distribution network design problem by using a novel self-adoptive evolutionary algorithm. International Journal of Systems Science: Operations & Logistics, 8(1), 1–22. https://doi.org/10.1080/23302674.2019.1607621
   Google Scholar
• Goodarzian, F., Kumar, V., & Abraham, A. (2021). Hybrid meta-heuristic algorithms for a supply chain network considering different carbon emission regulations using big data characteristics. Soft Computing, 25(11), 7527–7557. https://doi.org/10.1007/s00500-021-05711-7
   Web of Science ®Google Scholar
• Goodarzian, F., Kumar, V., & Ghasemi, P. (2021). A set of efficient heuristics and meta-heuristics to solve a multi-objective pharmaceutical supply chain network. Computers & Industrial Engineering, 158, 107389. https://doi.org/10.1016/j.cie.2021.107389
   Web of Science ®Google Scholar
• Goodarzian, F., Taleizadeh, A. A., Ghasemi, P., & Abraham, A. (2021). An integrated sustainable medical supply chain network during COVID-19. Engineering Applications of Artificial Intelligence, 100, 104188. https://doi.org/10.1016/j.engappai.2021.104188
   PubMed Web of Science ®Google Scholar
• Heard, B. R., Taiebat, M., Xu, M., & Miller, S. A. (2018). Sustainability implications of connected and autonomous vehicles for the food supply chain. Resources, Conservation and Recycling, 128, 22–24. https://doi.org/10.1016/j.resconrec.2017.09.021
   Web of Science ®Google Scholar
• Holland, J. H. (1992). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. The Quarterly Review of Biology, 1(1), 211.
   Google Scholar
• Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. https://doi.org/10.1126/science.220.4598.671
   PubMed Web of Science ®Google Scholar
• Kresnanto, N. C., Putri, W. H., Lantarsih, R., & Harjiyatni, F. R. (2021, February). Challenges in transportation policy: Speeding up a sustainable agri-food supply chain. In IOP Conference Series: Earth and Environmental Science (Vol. 662, No. 1, p. 012006). IOP Publishing.
   Google Scholar
• Lamiae, D., Jabri, A., El Barkany, A., & Darcherif, A. M. (2021). Optimization of fresh food distribution route using genetic algorithm with the best selection technique. In Constraint handling in metaheuristics and applications (pp. 175–199). Springer. https://link.springer.com/chapter/10.1007/978-981-33-6710-4_8
   Google Scholar
• Manna, A. K., Akhtar, M., Shaikh, A. A., & Bhunia, A. K. (2021). Optimization of a deteriorated two-warehouse inventory problem with all-unit discount and shortages via tournament differential evolution. Applied Soft Computing, 107, 107388. https://doi.org/10.1016/j.asoc.2021.107388
   Web of Science ®Google Scholar
• Manna, A. K., Benerjee, T., Mondal, S. P., Shaikh, A. A., & Bhunia, A. K. (2021). Two-plant production model with customers’ demand dependent on warranty period of the product and carbon emission level of the manufacturer via different meta-heuristic algorithms. Neural Computing and Applications, 33, 14263–14281. https://doi.org/10.1007/s00521-021-06073-9
   Web of Science ®Google Scholar
• Manna, A. K., Dey, J. K., & Mondal, S. K. (2019). Controlling GHG emission from industrial waste perusal of production inventory model with fuzzy pollution parameters. International Journal of Systems Science: Operations & Logistics, 6(4), 368–393. https://doi.org/10.1080/23302674.2018.1479802
   Google Scholar
• Mirjalili, S. (2019). Genetic algorithm. In Evolutionary algorithms and neural networks (pp. 43–55). Springer. https://link.springer.com/chapter/10.1007/978-3-319-93025-1_4
   Google Scholar
• Mogale, D. G., Kumar, M., Kumar, S. K., & Tiwari, M. K. (2018). Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network. Transportation Research Part E: Logistics and Transportation Review, 111, 40–69. https://doi.org/10.1016/j.tre.2018.01.004
   Web of Science ®Google Scholar
• Mogale, D. G., Kumar, S. K., & Kumar Tiwari, M. (2016). Two stage Indian food grain supply chain network transportation-allocation model. IFAC-PapersOnLine, 49(12), 1767–1772. https://doi.org/10.1016/j.ifacol.2016.07.838
   Google Scholar
• Mogale, D. G., Kumar, S. K., Márquez, F. P. G., & Tiwari, M. K. (2017). Bulk wheat transportation and storage problem of public distribution system. Computers & Industrial Engineering, 104, 80–97. https://doi.org/10.1016/j.cie.2016.12.027
   Web of Science ®Google Scholar
• Mogale, D. G., Kumar, S. K., & Tiwari, M. K. (2018). An MINLP model to support the movement and storage decisions of the Indian food grain supply chain. Control Engineering Practice, 70, 98–113. https://doi.org/10.1016/j.conengprac.2017.09.017
   Web of Science ®Google Scholar
• Mosallanezhad, B., Chouhan, V. K., Paydar, M. M., & Hajiaghaei-Keshteli, M. (2021). Disaster relief supply chain design for personal protection equipment during the COVID-19 pandemic. Applied Soft Computing, 112, 107809. https://doi.org/10.1016/j.asoc.2021.107809
   PubMed Web of Science ®Google Scholar
• Muthuraman, S., & Venkatesan, V. P. (2017, February). A comprehensive study on hybrid meta-heuristic approaches used for solving combinatorial optimization problems. In 2017 world congress on computing and communication technologies (WCCCT) (pp. 185–190). IEEE.
   Google Scholar
• Nadal-Roig, E., & Plà-Aragonés, L. M. (2015). Optimal transport planning for the supply to a fruit logistic centre. In L. Plà-Aragonés (Ed.), Handbook of operations research in agriculture and the agri-food industry (pp. 163–177). Springer. https://doi.org/10.1007/978-1-4939-2483-7_7
   Google Scholar
• Nasr, N., Niaki, S. T. A., Kashan, A. H., & Seifbarghy, M. (2021). An efficient solution method for an agri-fresh food supply chain: Hybridization of lagrangian relaxation and genetic algorithm. Environmental Science and Pollution Research, 1–19. https://doi.org/10.1007/s11356-021-13718-8
   Web of Science ®Google Scholar
• Punyim, P., Karoonsoontawong, A., Unnikrishnan, A., & Xie, C. (2018). Tabu search for joint location–inventory problem with stochastic inventory capacity constraints (No. 18-00296).
   Google Scholar
• Rabbani, M., Hosseini-Mokhallesun, S. A. A., Ordibazar, A. H., & Farrokhi-Asl, H. (2020). A hybrid robust possibilistic approach for a sustainable supply chain location-allocation network design. International Journal of Systems Science: Operations & Logistics, 7(1), 60–75. https://doi.org/10.1080/23302674.2018.1506061
   Google Scholar
• Roghanian, E., & Cheraghalipour, A. (2019). Addressing a set of meta-heuristics to solve a multi-objective model for closed-loop citrus supply chain considering CO2 emissions. Journal of Cleaner Production, 239, 118081. https://doi.org/10.1016/j.jclepro.2019.118081
   Web of Science ®Google Scholar
• Sahebjamnia, N., Goodarzian, F., & Hajiaghaei-Keshteli, M. (2020). Optimization of multi-period three-echelon citrus supply chain problem. Journal of Optimization in Industrial Engineering, 13(1), 39–53.
   Google Scholar
• Salem, R. W., & Haouari, M. (2017). A simulation-optimization approach for supply chain network design under supply and demand uncertainties. International Journal of Production Research, 55(7), 1845–1861. https://doi.org/10.1080/00207543.2016.1174788
   Web of Science ®Google Scholar
• Shukla, M., & Jharkharia, S. (2013). Agri-fresh produce supply chain management: A state-of-the-art literature review. International Journal of Operations & Production Management, 33(2), 114–158. https://doi.org/10.1108/01443571311295608
   Web of Science ®Google Scholar
• Talbi, E. G. (2015). Hybrid metaheuristics for multi-objective optimization. Journal of Algorithms & Computational Technology, 9(1), 41–63. https://doi.org/10.1260/1748-3018.9.1.41
   Web of Science ®Google Scholar
• Tsolakis, N. K., Keramydas, C. A., Toka, A. K., Aidonis, D. A., & Iakovou, E. T. (2014). Agrifood supply chain management: A comprehensive hierarchical decision-making framework and a critical taxonomy. Biosystems Engineering, 120, 47–64. https://doi.org/10.1016/j.biosystemseng.2013.10.014
   Web of Science ®Google Scholar
• Uu Pauls-Worm, K. G., Hendrix, E. M., Haijema, R., & van der Vorst, J. G. (2014). An MILP approximation for ordering perishable products with non-stationary demand and service level constraints. International Journal of Production Economics, 157, 133–146. https://doi.org/10.1016/j.ijpe.2014.07.020
   Web of Science ®Google Scholar
• Yadav, A. S., Swami, A., Ahlawat, N., Arora, T. K., Chaubey, P. K., & Pathak, K. (2021). A study of COVID-19 pandemic on beer industry supply chain inventory management using travelling salesman problem for simulated annealing. Network, 6(3–4), 9–23.
   Google Scholar
• Zhang, W., & Wilhelm, W. E. (2011). OR/MS decision support models for the specialty crops industry: A literature review. Annals of Operations Research, 190(1), 131–148. https://doi.org/10.1007/s10479-009-0626-0
   Web of Science ®Google Scholar