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 CO₂ 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.

Considering the main pillars of sustainable development, this paper proposes a new multi-objective model to design a sustainable closed-loop location routing supply chain network taking the pharmaceutical products life cycle as a case study. This model aims at generating economic gains, increasing the social responsibility of companies in terms of job creation and reducing the risk arising from the transport of end of Life products (medical waste from the expiry of pharmaceutical products and their use in the healthcare centres). The multi-objective model expressed as mixed integer linear program was solved by an exact approach, this resolution allowed us to take strategic and operational decisions. Besides, the tuning of objective’s weight helps us to analyse the trade-off possibility between the different objectives and to highlight the impact of social responsibility on the design of closed-loop routing supply chain networks.

This review investigates the application of Artificial Intelligence (AI), deep learning (DL), and the Internet of Things (IoT) in water resource management, focusing on distribution optimization, demand prediction, and water quality enhancement. The study synthesizes findings from 2015 to 2024, encompassing experimental and applied research published in English or French in recognized scientific outlets. By analyzing the prevalent algorithms, IoT technologies, and their impacts, this systematic review highlights research gaps and proposes directions for future work. The results show significant advancements in predictive analytics and real-time monitoring through AI and the IoT. However, challenges remain in scalability, interdisciplinary integration, and contextual adaptation.

Le traitement des déchets médicaux est qualifié actuellement au Maroc comme étant une bombe à retardement. C’est pour cette raison que gouvernement et professionnels cherchent des solutions pour une meilleure professionnalisation du traitement des déchets médicaux tout en respectant les standards internationaux en vigueur et assurer un intérêt économique pour le privé. À travers ce papier, les auteurs ont proposé un modèle de centralisation des déchets collectés des CHU et centres hospitaliers, permettant à la fois d’optimiser les tournées en amont et en aval de l’entrepôt central, tout en respectant les contraintes de capacité, les fenêtres de temps et l’hétérogénéité de la flotte. La robustesse du modèle, exprimé sous forme d’un programme linéaire mixte, a été vérifiée via l’expérimentation de plusieurs instances. Cette expérimentation a révélé aussi comment un tel modèle servira au développement durable dans un secteur aussi important que celui du traitement des déchets médicaux.

This paper presents an optimization approach for supply chain management that incorporates goal programming (GP), dependent chance constraints (DCC), and the hunger games search algorithm (HGSA). The model acknowledges uncertainty by embedding uncertain parameters that promote resilience and efficiency. It focuses on minimizing costs while maximizing on-time deliveries and optimizing key decision variables such as production setups, quantities, inventory levels, and backorders. Extensive simulations and numerical results confirm the model’s effectiveness in providing robust solutions to dynamically changing supply chain problems when compared to conventional models. However, the integrated model introduces substantial computational complexity, which may pose challenges in large-scale real-world applications. Additionally, the model’s reliance on precise probabilistic and fuzzy parameters may limit its applicability in environments with insufficient or imprecise data. Despite these limitations, the proposed approach has the potential to significantly enhance supply chain resilience and efficiency, offering valuable insights for both academia and industry.