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.

Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several factors, including an increase in logistics activities in the urban core. Therefore, this paper aims to define the relationship between the logistics sprawl phenomenon and congestion level. In this sense, we explored the literature to summarize the phenomenon of logistics sprawl in different cities and defined the dependent and independent variables. Congestion level was defined as the dependent variable, while the increasing distance resulting from logistics sprawl, along with city and operational flow characteristics, was treated as independent variables. We compared the performance of several models, including decision tree, support vector machine, gradient boosting, k-nearest neighbor, logistic regression and random forest. Among all the models tested, we found that the random forest algorithm delivered the best performance in terms of prediction. We combined both logistic regression—for its interpretability—and random forest—for its predictive strength—to define, explain, and interpret the relationship between the studied variables. Subsequently, we collected data from the literature and various databases, including transit city sources. The resulting dataset, composed of secondary and open-source data, was then enhanced through standard augmentation techniques—SMOTE, mixup, Gaussian noise, and linear interpolation—to improve class balance and data quality and ensure the robustness of the analysis. Then, we developed a Python code and executed it in Colab. As a result, we deduced an equation that describes the relationship between the congestion level and the defined independent variables.

This paper aims to describe the phenomenon of logistics sprawl and its impact on freight transport activity. We explore its dimensions, drivers, and consequences through an exhaustive literature review following the PRISMA guidelines. Our analysis reveals that logistics sprawl is mainly driven by economic and organizational factors. Then, we confirm its impact on urban freight transport activity, as it leads to increasing traveled distances and environmental emissions, particularly CO2 emissions. This article contributes to the existing literature by providing a comprehensive structure and analysis of the logistics sprawl phenomenon. We have synthesized the primary research results and provided a systematic classification of logistics sprawl drivers and consequences, which was previously absent in the literature

Optimising vehicle routing is an important issue that relies on logistics flow management, resource allocation, and transport planning. However, it is influenced by a large number of variables, including freight volumes, distances, traffic conditions, vehicle performance, weather conditions, and infrastructure quality. In this sense, to understand the impact of using IoV on VRP, the authors reviewed the existing literature using the PRISMA method. As a result, they presented IoV and its contribution to vehicle routing optimisation, and more specifically, they deduced the impact of IoV use on economic, energy, environmental, and organisational performance. Finally, they confirmed that IoV aims to optimise vehicle routing performance, manage traffic conditions, and provide communications between vehicles and infrastructure.

Artificial intelligence has made great strides in various fields, especially in improving logistics operations and freight transportation. This chapter aims to highlight the importance of applying AI to manage logistics sprawl phenomenon. The research focused on analyzing the impact of AI use on the performance of urban freight transport under logistics sprawling conditions. To achieve this, the authors carried out a literature review to explore the use of different AI categories including machine learning, deep learning, natural language processing, visual data processing, reinforcement learning (RL), and specialized algorithms, to optimise freight transport activities within a logistics sprawl context.

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.