The green supplier selection presents numerous challenges, from initial assessment to final selection, which specialists in this field of supplier management often encounter. Among the techniques that aim to meet these challenges that are continually progressing is the creation and development of high-quality decision-making tools. In this study, the assessment of suppliers hinges on both traditional and environmental factors. A framework of multi-criteria decision-making (MCDM) is announced in order to appraise green supplier selection. This framework integrates Fuzzy Analytical Hierarchy Process (AHP) combined with two additional methods: WASPAS (“Weighted Aggregated Sum-Product Assessment”) and TOPSIS (“Technique for Order Preference by Similarity to Ideal Solution”). At the outset, there were five environmental criteria for green supplier selection: “Health and Safety”, “Sustainable Product Design”, “ISO 14001 Certification”, “Investment Recovery”, and “Green Packaging”, along with three conventional criteria including quality, price, and delivery, were pinpointed through a review of literature and expert input to facilitate the MCDM approach. As part of demonstrating the pertinency of the recommended framework, a practical case study of the automotive industry in Morocco is discussed. The results demonstrate that the utilized Fuzzy hybrid methods yield consistent rankings for green suppliers. Additionally, among the four green suppliers, number three obtained the best ranking, which indicates the robustness and performance of the chosen models. Furthermore, this study offers a unified platform for selecting green suppliers under a Fuzzy environment. Therefore, the chosen strategy and its analysis provide relevant data and information to decision-makers for the choice and selection of suppliers. It can also provide and help procurement departments and decision-makers to choose and select the efficient ecological supplier in the global market supply chain.

For any industry to improve and expand, the proper evaluation and selection of suppliers is essential. In order to establish whether a supplier is appropriate for working with a company, a system for selecting green suppliers is required. A variety of Decision-Making (DM) models have been created by researchers to address the problems associated with evaluating and choosing green suppliers. In order to address the Green Supplier Evaluation and Selection (GSES) challenge, we did a thorough investigation of ten works of literature, in order to find out which approach is the most widely used and which is more efficient. This study primarily focuses on the findings of ten reviews that examined 1098 research publications from academic journals between 1990 and 2020. 271 DM models examined that were broken down into 170 individual models and 101 combination models, our analysis only looked at the single models. The method of Analytic Hierarchy Process (AHP) is the dominant model used by 160 articles, 122 studies used Data Envelopment Analysis (DEA), and finally 101 research works that utilized the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model. In addition, we found that the biggest percentage “62%” of studied articles used multi-criteria decision-making (MCDM) models. As a result, the most widely utilized Decision Making models to address the evaluation and selection of green supplier were found to be AHP, DEA, and TOPSIS.

Urban freight transport is an essential component of urban mobility, playing a key role in supporting the economic and social development of cities. However, the growth of freight and delivery activities can intensify energy consumption and increase pressure on urban transportation systems. In this context, the Energy-Efficient Vehicle Routing Problem (EE-VRP) has emerged as an important research challenge, requiring a balance between routing complexity and non-linear operational dynamics. From this perspective, data-driven and intelligent optimization approaches can support more efficient decision-making in urban transportation systems by integrating operational efficiency with energy performance. To address this challenge, this paper proposes the Prediction-Optimization Bridge (POB) model, a hybrid Artificial Intelligence and Multi-Objective Optimization framework that seamlessly integrates supervised machine learning with multi-objective evolutionary optimization. The proposed model combines a Random Forest surrogate model with the NSGA-II evolutionary algorithm to identify efficient vehicle routes. First, we used the RF surrogate to capture complex non-linear relationships and minimize total fleet energy consumption. Then, the predicted energy values are subsequently integrated into the NSGA-II multi-objective evolutionary algorithm, which explores alternative routing solutions. To enhance operational realism, a dynamic payload updating mechanism adjusts vehicle load after each delivery, reflecting the progressive reduction in transported goods along the route. As a result, the Random Forest surrogate achieved a predictive accuracy of R² = 0.9960. Furthermore, the proposed framework attained an average energy reduction of 12.47% ± 2.32% compared with a conventional distance-greedy routing heuristic, while achieving a normalized Hypervolume of 0.6305 ± 0.0277 over five independent experimental runs. Feature importance analysis based on Gini metrics identified vehicle load as the most influential factor affecting energy consumption (approximately 70%). Overall, the results demonstrate that integrating vectorized machine learning surrogates with evolutionary multi-objective optimization provides an efficient, robust, and reproducible approach for developing energy-efficient and sustainable fleet routing strategies.