This review critically examines hydrogen energy systems, highlighting their capacity to transform the global energy framework and mitigate climate change. Hydrogen showcases a high energy density of 120 MJ/kg, providing a robust alternative to fossil fuels. Adoption at scale could decrease global CO₂ emissions by up to 830 million tonnes annually. Despite its potential, the expansion of hydrogen technology is curtailed by the inefficiency of current electrolysis methods and high production costs. Presently, electrolysis efficiencies range between 60 % and 80 %, with hydrogen production costs around $5 per kilogram. Strategic advancements are necessary to reduce these costs below $2 per kilogram and push efficiencies above 80 %. Additionally, hydrogen storage poses its own challenges, requiring conditions of up to 700 bar or temperatures below -253 °C. These storage conditions necessitate the development of advanced materials and infrastructure improvements. The findings of this study emphasize the need for comprehensive strategic planning and interdisciplinary efforts to maximize hydrogen's role as a sustainable energy source. Enhancing the economic viability and market integration of hydrogen will depend critically on overcoming these technological and infrastructural challenges, supported by robust regulatory frameworks. This comprehensive approach will ensure that hydrogen energy can significantly contribute to a sustainable and low-carbon future.

The range of applications of RPAs in various industries indicates that their increased usage could reduce operational costs and time. Remotely piloted aircraft systems (RPASs) can be deployed quickly and effectively in numerous distribution systems and even during a crisis by eliminating existing problems in ground transport due to their structure and flexibility. Moreover, they can also be useful in data collection in damaged areas by correctly defining the condition of flight trajectories. Hence, defining a framework and model for better regulation and management of RPAS-based systems appears necessary; a model that could accurately predict what will happen in practice through the real simulation of the circumstances of distribution systems. Therefore, this study attempts to propose a multi-objective location-routing optimization model by specifying time window constraints, simultaneous pick-up and delivery demands, and the possibility of recharging the used batteries to reduce, firstly, transport costs, secondly, delivery times, and thirdly, estimated risks. Furthermore, the delivery time of the model has been optimized to increase its accuracy based on the uncertain conditions of possible traffic scenarios. It is also imperative to note that the assessment of risk indicators was conducted based on the Specific Operations Risk Assessment (SORA) standard to define the third objective function, which was conducted in a few previous studies. Finally, it shows how the developed NSGA-II algorithm in this study performed successfully and reduced the objective function by 31%. Comparing the obtained results using an NSGA-II meta-heuristic approach, through the rigorous method GAMS, indicates that the results are valid and reliable.

This paper presents and assesses the feasibility and potential of a novel concept: the operation of multiple Unmanned Aerial Vehicles (UAVs) commanded and supported by a manned “Tender” air vehicle carrying a pilot and flight manager(s). The “Tender” is equipped to flexibly and economically monitor and manage multiple diverse UAVs over otherwise inaccessible terrain through wireless communication. The proposed architecture enables operations and analysis supported by the means to detect, assess, and accommodate change and hazards on the spot with effective human observation and coordination. Further, this paper seeks to find the optimal trajectories for UAVs to collect data from sensors in a predefined continuous space. We formulate the path-planning problem for a cooperative, and a diverse swarm of UAVs tasked with optimizing multiple objectives simultaneously with the goal of maximizing accumulated data within a given flight time within cloud data processing constraints as well as minimizing the probable imposed risk during UAVs mission. The risk assessment model determines risk indicators using an integrated Specific Operation Risk Assessment—Bayesian belief network approach, while its resultant analysis is weighted through the analytic hierarchy process ranking model. To this end, as the problem is formulated as a convex optimization model, and we propose a low complexity multi-objective reinforcement learning (MORL) algorithm with a provable performance guarantee to solve the problem efficiently. We show that the MORL architecture can be successfully trained and allows each UAV to map each observation of the network state to an action to make optimal movement decisions. This proposed network architecture enables the UAVs to balance multiple objectives. Estimated MSE measures show that the algorithm produced decreasing errors in the learning process with increasing epoch number.

To address the complexities of managing networks of unmanned aerial vehicles (UAVs) and Just-in-Time problem solving, this study introduces a cutting-edge multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, significantly enhancing the efficiency of UAV operations. It reduces battery consumption and transportation costs while optimizing delivery times and reducing operational risks. The model improves the refinement of delivery schedules by accounting for uncertain traffic scenarios, thereby increasing its accuracy and reliability in dynamic environments. Additionally, a Bayesian belief networks approach for risk assessment introduces a new layer to operational risk management. The model’s performance and its trade-offs are demonstrated through advanced data visualizations such as 3D Pareto fronts, pair plots, and network graphs, with validation via the NSGA-II approach confirming its reliability and practical applicability. This research represents a major leap forward in drone routing strategies, focusing on efficiency, adaptability, and risk management in UAV logistics and provides a comprehensive framework that bridges the gap between theoretical exploration and practical application.

Purpose: This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located. Design/methodology/approach: The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms. Findings: According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO. Research limitations/implications: In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered. Practical implications: The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs. Originality/value: This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.