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Abstract 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.

This research examines the intricate correlation between speed variation (celeration), a metric of driver behavior associated with vehicle control, and occurrences of running red lights. The study is based on a thorough analysis of a large dataset that includes a variety of parameters, such as exceeding speed limits, driver age, passenger count, weather, road condition, and temporal factors. Using cutting-edge machine learning methods like AdaBoost and Bagging, predictive models for red-light violations are painstakingly built, achieving remarkable validation accuracies of 90.4% and 90.1%, respectively. The study acknowledges the dataset’s limitations in capturing real-world traffic complexities while focusing on the effectiveness and trade-offs inherent in these methodologies. This emphasizes how important it is to have synchronized and thorough data sources to guarantee accurate representation. The research field is enhancing predictive modeling techniques and improving transportation safety by connecting celebration, speed variation patterns over time, with instances of red-light violations.

E-scooters are gaining popularity for short-distance travel, but their recharging presents challenges. To reduce their downtime, we propose a Hybrid K-Means/Particle Swarm Optimisation (PSO) approach, optimizing charging routes using machine learning and meta-heuristics. The research in this paper attempts to determine if a combination of a meta-heuristic such as PSO and a machine learning algorithm for clustering such as K-Means, would be effective at solving the vehicle routing problem for e-scooters. We compared this method with other algorithms and found that Tabu Search excelled in over 95% of tests. While Hybrid K-Means/PSO led in only approximately 52% of scenarios, it was also the only one to provide an output that surpassed Tabu Search in one of the scenarios. The core difference in efficiency is due to traditional meta-heuristic methods providing routes that while optimal, may also travel from locations relatively far from each other, while Hybrid K-Means/PSO will provide routes between locations that are clustered and in local groups. This results in Hybrid K-Means/PSO being slightly less efficient but may be more practical for charging personnel as they can operate in designated areas close to each other rather than a more optimal route with nodes further apart. This research underscores the effectiveness of Tabu Search and the potential of our Hybrid K-Means/PSO approach for optimizing e-scooter charging routes.

Nowadays, with the advancement of technological innovations and wide implementation of modern mining equipment, research topics on mining equipment management are attracting more and more attention from both academic scholars and industrial practitioners. With this background, this paper comprehensively reviews recent publications in the field of mining equipment management. By analysing the characteristics of open-pit mine production and haulage equipment types, problem definitions, formulation models and solution approaches in the relevant literature, the reviewed papers are classified into three main categories, i.e., shovel–truck (ST); in-pit crushing–conveying (IPCC); and hybrid IPCC-ST systems. Research progress and characteristics in each categorized mining equipment system are discussed and evaluated, respectively. With a thorough assessment of recent research agendas, the significance of developing state-of-the-art mining equipment scheduling/timetabling methodologies is indicated, based on the application of classical continuous-time machine scheduling theory. Promising future research directions and hotspots are also provided for researchers and practitioners in the mining industry.

Abstract Beef production drives a significant proportion of Australia's agricultural economy, representing AUD11 billion in agricultural production value annually. The profitability of cattle farms across seasons varies greatly and is particularly influenced by weather and associated uncertainty through pasture growth. Furthermore, producers must be careful in managing stocking rates to avoid overgrazing which has a significant environmental and economic impact. Predictive modelling of pasture and cattle liveweight can help producers to better manage this difficult balancing act. In this paper, we integrate a model for pasture growth and cattle weight gain to formulate predictions of liveweight production volume and pasture biomass. Monte‐Carlo simulation was used to understand the impact of uncertainty in model inputs relating to weather. Through such an approach, a distribution of possible pasture outcomes and beef production is formed which can be inspected to determine, for example expected production volume and risk of overgrazing. The methodology is demonstrated and validated through application to case study data sourced from Australian beef producers. Results demonstrate that it is possible to obtain reasonable predictions of liveweight gain and highlight the importance of considering the associated uncertainty. The accuracy of the proposed models has been examined by calculating statistical parameters MSE, MAE and R2. This approach can provide insight into the distribution of possible production outcomes given the long‐term rainfall outlook. The use of such predictive models and simulations by producers will lead to better‐informed planning of stocking rates, with benefits to productivity and sustainable utilisation of pasture.

Red light running at signalised intersections is a growing road safety issue worldwide, leading to the rapid development of advanced intelligent transportation technologies and countermeasures. However, existing studies have yet to summarise and present the effect of these technology-based innovations in improving safety. This paper represents a comprehensive review of red-light running behaviour prediction methodologies and technology-based countermeasures. Specifically, the major focus of this study is to provide a comprehensive review on two streams of literature targeting red-light running and stop-and-go behaviour at signalised intersection – (1) studies focusing on modelling and predicting the red-light running and stop-and-go related driver behaviour and (2) studies focusing on the effectiveness of different technology-based countermeasures which combat such unsafe behaviour. The study provides a systematic guide to assist researchers and stakeholders in understanding how to best identify red-light running and stop-and-go associated driving behaviour and subsequently implement countermeasures to combat such risky behaviour and improve the associated safety.

The Global Navigation Satellite System (GNSS) is unreliable in some situations. To mend the poor GNSS signal, an autonomous vehicle can self-localize by matching a ground image against a database of geotagged aerial images. However, this approach has challenges because of the dramatic differences in the viewpoint between aerial and ground views, harsh weather and lighting conditions, and the lack of orientation information in training and deployment environments. In this paper, it is shown that previous models in this area are complementary, not competitive, and that each model solves a different aspect of the problem. There was a need for a holistic approach. An ensemble model is proposed to aggregate the predictions of multiple independently trained state-of-the-art models. Previous state-of-the-art (SOTA) temporal-aware models used heavy-weight network to fuse the temporal information into the query process. The effect of making the query process temporal-aware is explored and exploited by an efficient meta block: naive history. But none of the existing benchmark datasets was suitable for extensive temporal awareness experiments, a new derivative dataset based on the BDD100K dataset is generated. The proposed ensemble model achieves a recall accuracy R@1 (Recall@1: the top most prediction) of 97.74% on the CVUSA dataset and 91.43% on the CVACT dataset (surpassing the current SOTA). The temporal awareness algorithm converges to R@1 of 100% by looking at a few steps back in the trip history.

In this study, we propose a general method for tackling the Pickup and Drop-off Problem (PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our aim is to reduce the overall tour length traveled by an agent while remaining within the truck's capacity restrictions and adhering to the node-to-node relationship. In such instances, the agent does not allow any drop-off points to be serviced if the truck is empty; conversely, if the vehicle is full, the agent does not allow any products to be picked up from pickup points. In our approach, this challenge is solved using machine learning-based models. Using HPNs as our primary model allows us to gain insight and tackle more complicated node interactions, which simplified our objective to obtaining state-of-art outcomes. Our experimental results demonstrate the effectiveness of the proposed neural network, as we achieve the state-of-art results for this problem as compared with the existing models. We deal with two types of demand patterns in a single type commodity problem. In the first pattern, all demands are assumed to sum up to zero (i.e., we have an equal number of backup and drop-off items). In the second pattern, we have an unequal number of backup and drop-off items, which is close to practical application, such as bike sharing system rebalancing. Our data, models, and code are publicly available at Solving Pickup and Dropoff Problem Using Hybrid Pointer Networks with Deep Reinforcement Learning.

Due to emerging requirements and pressures related to environmental protection, manufacturing enterprises have expressed growing concern for adopting various energy-saving strategies. However, environmental criteria were usually not considered in traditional production scheduling problems. To overcome this deficiency, energy-saving scheduling has drawn more and more attention from academic scholars and industrial practitioners. In this paper, an energy-saving flexible job shop scheduling problem (EFJSP) is introduced in accordance with the criterion of optimizing power consumption and processing costs simultaneously. Since the classical FJSP is strongly NP-hard, an Improved Sparrow Search Algorithm (ISSA) is developed for efficiently solving the EFJSP. In the ISSA, a Hybrid Search (HS) method is used to produce an initial high-quality population; a Quantum Rotation Gate (QRG) and a Sine–Cosine Algorithm (SCA) are integrated to intensify the ability of the ISSA to coordinate exploration and exploitation; the adaptive adjustment strategy and Variable Neighborhood Search (VNS) are applied to strengthen diversification of the ISSA to move away from local optima. Extensive computational experiments validate that the ISSA outperforms other existing algorithms in solving the EFJSP due to the advantages of intensification and diversification mechanisms in the ISSA.