Performance assessment of the main metaheuristics for sustainable supply chains
Evolutionary Intelligence - Trang 1-8 - 2022
Tóm tắt
The sustainable supply chains optimization is a high-dimensional multi-objective optimization problem. The involved costs can be categorized as economic, environmental, and social. Metaheuristics can be used for tackling this kind of problem efficiently. This short note deals with a comparative analysis of the main metaheuristics (according to recent surveys) for a sustainable supply chain model. The considered techniques are genetic algorithm, particle swarm optimization, simulated annealing, and non-dominated sorted genetic algorithm. Moreover, two hybrid models are also included, i.e. genetic algorithm combined with simulated annealing and genetic algorithm combined with particle swarm optimization. The latter provides the best result with a constraint satisfaction rate equal to 0.9968.
Tài liệu tham khảo
Lambert D, Stock J, Ellram L (1998) Fundamentals of logistics management. Marketing & advertising series. McGraw-Hill, Irwin
D’Arienzo MP, Rarità L (2019) Management of Supply Chains for the Wine Production. In: AIP conference proceedings, Volume 2293, 24 November 2020, Article number 420042, International conference on numerical analysis and applied mathematics 2019 (ICNAAM 2019)
de Falco M, Mastrandrea N, Mansoor W, Rarità L (2018) Situation awareness and environmental factors: the EVO oil production. In: Daniele P, Scrimali L (eds) New trends in emerging complex real life problems, vol 1. AIRO Springer, Berlin, pp 209–217
Rarità L (2022) A genetic algorithm to optimize dynamics of supply chains. AIRO Springer Series 8:107–115
http://www.fao.org/energy/agrifood-chains/en/
WCED (1987) Our common future. World Commission on Environment and Development. Oxford University Press, Oxford
Barbosa-Povoa AP, da Silva C, Carvalho A (2018) Opportunities and challenges in sustainable supply chain: an operations research perspective. Eur J Oper Res 268(2):399–431
Naderi R, Nikabadi MS, Tabriz AA, Pishvaee MS (2021) Supply chain sustainability improvement using exergy analysis. Comput Ind Eng 154:107142
Motevalli-Taher F, Paydar MM, Emami S (2020) Wheat sustainable supply chain network design with forecasted demand by simulation. Comput Electron Agric 178:105763
Trunfio GA (2016) Metaheuristics for continuous optimization of high-dimensional problems: state of the art and perspectives. In: Emrouznejad A (ed) Big data optimization: recent developments and challenges. Studies in big data, vol 18. Springer, Cham
Keller A (2019) Multi-objective optimization in theory and practice II: metaheuristic algorithms. Bentham-Open
D’Aniello G, Gaeta M, Loia V, Orciuoli F (2015) An AmI-based software architecture enabling evolutionary computation in blended commerce: the shopping plan application. Mobile Inf Syst 2015, Article ID 936125
D’Aniello G, Orciuoli F, Parente M, Vitiello A (2014) Enhancing an AmI-based framework for U-commerce by applying memetic algorithms to plan shopping. Paper presented at the Proceedings—2014 international conference on intelligent networking and collaborative systems, IEEE INCoS 2014, pp 169-175
Faramarzi-Oghani S, Neghabadi PD, Talbi E-G, Tavakkoli-Moghaddam R (2022) Meta-heuristics for sustainable supply chain management: a review. Int J Prod Res https://doi.org/10.1080/00207543.2022.2045377
Jayarathna C, Agdas D, Dawes L, Yigitcanlar T (2021) Multi-objective optimization for sustainable supply chain and logistics: a review. Sustainability 13:12
Sharma D, Jamwal A, Agrawal R, Jain JK, Machado J (2023) Decision making models for sustainable supply chain in industry 4.0: opportunities and future research agenda. Lecture Notes in Mechanical Engineering, pp 175–185
Jianying F, Bianyu Y, Xin L, Dong T, Weisong M (2021) Evaluation on risks of sustainable supply chain based on optimized BP neural networks in fresh grape industry. Comput Electron Agric 183:105988
Tomasiello S, Uzair M, Loit E (2021) ANFIS with fractional regularization for supply chains cost and return evaluation. In: CEUR workshop proceedings, 3074
Ozkan-Ozen YD, Sezer D, Ozbiltekin-Pala M, Kazancoglu Y (2022) Risks of data-driven technologies in sustainable supply chain management. Manag Environ Qual Int J. https://doi.org/10.1108/MEQ-03-2022-0051
Charvadeh MM, Pourmousa S, Tajdini A, Tamjidi A, Safdari V (2022) Presenting a management model for a multiobjective sustainable supply chain in the cellulosic industry and its implementation by the NSGA-II meta-heuristic algorithm, Discrete Dyn Nat Soc Article ID 8794472, 14 pages
Luke S (2013) Essentials of metaheuristics, 2nd edn. Lulu. http://cs.gmu.edu/~sean/book/metaheuristics/
Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245–1287
Zhang G et al (2017) Constraint handling in NSGA-II for solving optimal testing resource allocation problems. IEEE Trans Reliab 66(4):1193–1212
Brahami M, Dahane M, Souier M, Sahnoun M (2022) Sustainable capacitated facility location/network design problem: a non-dominated sorting genetic algorithm based multiobjective approach. Ann Oper Res 311:821–852
Vala TM, Rajput VN, Geem ZW, Pandya KS, Vora SC (2021) Revisiting the performance of evolutionary algorithms. Expert Syst Appl 175:114819