Butterfly Algorithm for Sustainable Lot Size Optimization

Zoubida Benmamoun (), Widad Fethallah, Mustapha Ahlaqqach, Ikhlef Jebbor, Mouad Benmamoun and Mariam Elkhechafi
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Zoubida Benmamoun: Faculty of Engineering, Liwa College of Technology, Abu Dhabi 41009, United Arab Emirates
Widad Fethallah: National School for Applied Sciences, Abdelmalek Essaadi University, Tangier 93000, Morocco
Mustapha Ahlaqqach: LARILE ENSEM, Hassan II University of Casablanca, Casablanca 20202, Morocco
Ikhlef Jebbor: GS Laboratory, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
Mouad Benmamoun: ANISSE, Faculty of Sciences, Mohammed V University in Rabat, Rabat 10170, Morocco
Mariam Elkhechafi: ISCAE, Casablanca 27182, Morocco

Sustainability, 2023, vol. 15, issue 15, 1-21

Abstract: The challenges faced by classical supply chain management affect efficiency with regard to business. Classical supply chain management is associated with high risks due to a lack of accountability and transparency. The use of optimization algorithms is considered decision-making support to improve the operations and processes in green manufacturing. This paper suggests a solution to the green lot size optimization problem using bio-inspired algorithms, specifically, the butterfly algorithm. For this, our methodology consisted of first collecting the real data, then the data were expressed with a simple function with several constraints to optimize the total costs while reducing the CO 2 emission, serving as input for the butterfly algorithm BA model. The BA model was then used to find the optimal lot size that balances cost-effectiveness and sustainability. Through extensive experiments, we compared the results of BA with those of other bio-inspired algorithms, showing that BA consistently outperformed the alternatives. The contribution of this work is to provide an efficient solution to the sustainable lot-size optimization problem, thereby reducing the environmental impact and optimizing the supply chain well. Conclusions: BA has shown that it can achieve the best results compared to other existing optimization methods. It is also a valuable chainsaw tool.

Keywords: lot size optimization; supply chain optimization; butterfly algorithm; metaheuristics; green lot size (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:15:p:11761-:d:1206895

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