In pursuit of reducing dependence on oil, the Kingdom of Saudi Arabia has made significant efforts to advance sustainability under the strategic framework known as Saudi Vision. This initiative encompasses a range of activities aimed at promoting sustainability within the energy sector. This paper aims to analyze the existing sustainable practices of companies, employees, and the broader community in the supply chain. The objective is to evaluate current progress toward the sustainability goals of the Kingdom and assess the competitiveness of firms in the energy sector based on the effectiveness of sustainability management in their supply chain. Based on data collected from Saudi energy companies, structural equation modeling (SEM) techniques are utilized to investigate sustainable supply chain management (SSCM) practices and their influence on CP in Saudi Arabian energy companies. This study examines the influence of several practices on competitiveness (CP), including environmental practices (EMPs), social practices for employees (SPEs), social practices for the community (SPCs), operational practices (OPs), and supply chain integration practices (SCIs). The study incorporates five distinct independent variables, namely, EMP, SPE, SPC, OP, and SCI, where the dependent variable under investigation is denoted as CP. The findings from the SEM analysis show that three variables—SPE, SPC, and OP—demonstrate statistical significance, while EMP and SCI do not. These findings enhance the continuing academic discussion about balancing economic growth and environmental preservation through sustainable practices in the energy sector.

Red-light running at signalised intersections poses a significant safety risk, necessitating advanced predictive technologies to predict red-light violation behaviour, especially for advanced red-light warning (ARLW) systems. This research leverages Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to forecast the red-light running and stopping behaviours of drivers in connected vehicles. We utilised data from the Ipswich Connected Vehicle Pilot (ICVP) in Queensland, Australia, which gathered naturalistic driving data from 355 connected vehicles at 29 signalised intersections. These vehicles broadcast Cooperative Awareness Messages (CAM) within the Cooperative Intelligent Transport Systems (C-ITS), providing kinematic inputs such as vehicle speed, speed limits, longitudinal and lateral accelerations, and yaw rate. These variables were monitored at 100-millisecond intervals for durations from 1 to 4 s before reaching various distances from the stop line. Our results indicate that the LSTM model outperforms the GRU in predicting both red-light running and stopping behaviours with high accuracy. However, the pre-trained GRU model performs better in predicting red-light running specifically, making it valuable in applications requiring early violation prediction. Implementing these models can enhance red-light violation countermeasures, such as dynamic all-red extension (DARE), decreasing the likelihood of severe collisions and enhancing road users’ safety.

E-scooters have become a popular mode of transportation for first and last-mile excursions in recent years. Their usage as a short-distance public transit system is commonly regarded as an efficient solution to minimise carbon emissions while also being handy for individuals on the go. The need for charging, however, has become problematic as a result of the popularity of e-scooters because it effectively renders the scooter that is presently being charged inoperable. A relatively new method being used involves swapping batteries in e-scooters rather than transferring full scooters to be recharged, reducing out-of-service time to a few minutes rather than hours. To reduce trip distances and maximise fuel economy for battery swapping operators, a system for determining the most effective path to switch the e-scooters’ batteries will be required. This paper aims to do this through the use of Tabu Search (TS) algorithm to determine the optimal number of battery swapping operators for an area and then to ascertain the most efficient routes for each operator. This method will then be compared to Simulated Annealing in order to determine which method is the most optimal for this scenario. The data used to evaluate this method was obtained from the 2019 Chicago pilot program . The results showed an adapted tabu search in the total distance travelled, leading to shorter charging trips comparing to simulated annealing.

In Robotic Mobile Fulfillment Systems (RMFS), the tight coupling between Task Allocation and Sequencing (TAS) and Conflict-free Path Planning (CPP) poses substantial complexities for operational-level coordination. This paper presents a Bi-Level Programming (BLP) model that jointly captures the interdependent decisions of TAS and CPP. The upper level allocates tasks to Automated Guided Vehicles (AGV) to improve the efficiency and balance local workload, while the lower level generates dynamically collision-free routes that respect real-world movement constraints. To efficiently solve this complicated BLP model, we develop a hybrid metaheuristic algorithm (GA-A*-CP) that integrates a Genetic Algorithm (GA), an improved A* algorithm and a collision-avoidance prediction (CP) mechanism into a unified framework. A key feature of the proposed approach is its iterative closed-loop optimization structure, where TAS decisions guide the generation of CPP results, while the resulting execution feedback capturing spatial constraints and agent interactions is recursively used to refine TAS decisions. This bidirectional coupling enables the RMFS to adapt dynamically congestion and coordination complexity for enhancing operational interaction and coordination. Extensive computational experiments under varying task intensities and AGV configurations show that the proposed BLP approach consistently achieves lower execution costs and better responsiveness in comparison to conventional decoupled approaches. These results show that integrating data-driven feedback across decision layers enables the system to dynamically adapt its planning and allocation strategies in response to execution results. The proposed BLP approach advances the design of a more responsive and structurally coherent architecture for multi-agent logistics systems.