Fouad Jawab

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.

Background: Smart urban logistics has emerged as a key element of sustainable city development, with direct effects on economic performance, environmental quality, and urban livability. Issues with traffic, pollutants, infrastructure strain, and last-mile delivery efficiency have become more pressing due to rapid urbanization and the expansion of e-commerce. In this regard, underground or enclosed corridor-based tube-based freight transit systems have surfaced as a viable smart infrastructure option for automated and low-impact commodities delivery. Methods: This study adopts an analytical literature review complemented by a structured case study analysis to examine the potential role of tube-based freight transport systems in future urban logistics. Key technological concepts, including pneumatic tubes, automated capsule transport, and integration with digital platforms, the Physical Internet, and smart city management systems, are examined through a structured analytical review of the literature. Results: The outcome of the reviewed studies indicates that tube-based systems can contribute to congestion alleviation, emission reduction, and improved delivery reliability by shifting selected freight flows away from surface transport networks. However, governance frameworks, infrastructure integration, and institutional coordination mechanisms continue to have a significant impact on claimed performance outcomes. Conclusions: Tube-based freight systems represent a promising but conditional pathway toward smarter and more sustainable urban logistics. Their large-scale deployment is forced by high capital costs, standardization challenges, regulatory uncertainty, and social acceptance issues. Coordinated investment plans, encouraging legal frameworks, and integrated urban planning techniques in line with smart city goals are needed to overcome these obstacles.

This chapter, per the authors, explores how artificial intelligence is revolutionizing modern supply chain logistics by enhancing operational efficiency, decision-making, and real-time responsiveness. It examines key AI technologies including machine learning, predictive analytics, and IoT integration, and highlights their application in demand forecasting, inventory control, and last-mile delivery. The chapter discusses transformative practices adopted by global organizations such as DHL and Amazon Robotics, and evaluates the impact of autonomous vehicles, smart warehouses, and blockchain on logistics systems. Emphasis is placed on sustainability and how AI-driven logistics contribute to greener operations and resilient supply chains. Through case studies and industry insights, this chapter offers a comprehensive view of how AI is reshaping the future of supply chain management.

Pharmaceutical supply chains face regulatory, logistical, and transparency challenges. AI integration offers transformative solutions, enhancing demand forecasting, inventory management, and logistics efficiency. While AI improves operational resilience and risk mitigation, adoption barriers—data privacy, regulatory complexity, and costs—persist. Collaboration among stakeholders (pharma firms, tech providers, policymakers) is vital to address technical, ethical, and compliance gaps. Prioritizing scalable AI tools and partnerships can overcome these hurdles, fostering agile, transparent supply networks. Strategic AI adoption strengthens global medication access, healthcare outcomes, and supply chain sustainability. This study highlights AI's urgency in building resilient pharma operations amid evolving market demands and patient needs.

Virtual reality (VR) serves as a transformative tool in modern supply chain management by enhancing efficiency, improving decision-making, and facilitating immersive training environments. Through applications in goods receipt, warehousing, order picking, shipping, and inventory planning, VR streamlines operations and reduces errors by providing interactive simulations and real-time data visualization. VR enables operators to optimize layouts, track inventory, and follow step-by-step instructions for complex logistical tasks, thus minimizing downtime and increasing productivity. Additionally, the integration of VR into logistics contributes to safer working environments by allowing employees to simulate hazardous situations, fostering risk mitigation and accident prevention. As supply chains face increasing complexity, VR emerges as a key technology to promote innovation, reduce costs, and build more resilient, adaptable, and sustainable supply chain networks.

The accelerated growth of e-commerce and ongoing urban expansion have intensified the challenges associated with last-mile delivery, making it a critical issue in sustainable urban logistics. Therefore, our paper presents a scoping review to systematically delineate the current state of research on smart and sustainable last-mile delivery systems. We explore both innovative technologies—such as artificial intelligence, autonomous vehicles, the Internet of Things, and digital twins—and human-centered dimensions, including urban design, policy development, and collaborative stakeholder engagement. Using the PRISMA-ScR-based methodology, 140 peer-reviewed articles (2015–2025) have been analyzed to highlight key trends, gaps, and prospective directions. The study underlines how the technologies of Industry 4.0 have improved visibility and operational efficiency, but holistic thinking that incorporates environmental, human, and policy factors remains undeveloped. Based on these findings, this article provides a conceptual framework for smart and sustainable last-mile delivery, focusing on the intersection of digital and simulation tools and human-centric governance to achieve optimized efficiency, environmental performance, and equity. This framework helps both academics and decision-makers to advance data-driven, resilient, and integrative city logistic ecosystems.

Inter-hospital patient transport services involve transfers between hospitals for certain hospitalization needs. Despite the diversity of patients' illnesses, this type of transport is a common service utilized by the majority of patients. The aim of this study is to develop a time-driven activity-based costing (TD-ABC) model for calculating the costs of inter-hospital patient transport. This model helps to understand the real costs of hospital care services. It also helps decision-makers make smarter decisions about how to allocate and use resources. The qualitative research approach followed in this study enabled us to understand inter-hospital patient transport services and processes. The resulting model adapts flexibly to the various inter-hospital patient transport routes by Basic Life Support (BLS) ambulance. The implementation of this model in Morocco (184.56 MAD), with a well-defined round trip (from Hospital A to Hospital B and back to Hospital A), demonstrates its validity for cost calculation. However, the TD-ABC, when applied in the context of inter-hospital patient transport, encounters several limitations, notably process mapping and time estimation, which are the primary keys to allocating the resources consumed. The results show that TD ABC provides a pricing model that considers the cost of inter-hospital transport services. It makes the value of inter-hospital transfer costs visible in the complete patient care pathway. Indeed, when the model is used for all inter-hospital journeys, it enables decision-makers to assess the distribution of costs for each patient category and the variation in costs between similar services but with different journeys.

This chapter is organized around three main questions related to the concept of the green Supply Chain, 4.0 technologies and the use of these technologies to ensure sustainability. Our main goal is to provide answers to the question: how and to what extent can sustainable supply chains be successfully implemented economically, socially and ecologically with the help of 4.0 technologies? Methodologically, we took an exploratory approach supported by qualitative and quantitative data extracted from literature, reports and the websites of the companies involved. This has allowed the focus to be on the cutting-edge practices and experiences of companies of different sizes and activities, as well as on the successes in reducing CO2 emissions and respecting social ethics.

The supply chain is a complex and dynamic field that requires effective planning, coordination, and execution to meet customer demands while minimizing costs. In recent years, artificial intelligence (AI) has emerged as a disruptive technology with the potential to transform various aspects of supply chain management. This chapter provides a comprehensive analysis of the role played by AI in supply chain management, highlighting its applications, benefits, challenges, and future prospects. This chapter explains how AI can improve supply chain visibility, demand forecasting, inventory management, logistics optimization, and risk mitigation. In addition, it explores the impact of AI on decision-making processes and collaboration within the supply chain network. The chapter also discusses the potential challenges and ethical considerations associated with the adoption of AI in supply chain management. By taking a deep dive into the current state of AI in Supply Chain and analyzing its potential future developments, this chapter aims to provide valuable insights.