Abstract: Sustainable freight transportation plays a pivotal role in addressing pressing environmental challenges while simultaneously fostering socio-economic development. Governmental entities worldwide are increasingly implementing strategic policy interventions to enhance the sustainability of freight transportation systems. A comprehensive understanding of the complex interactions and dynamics between these policy measures and transportation operations is essential for developing effective sustainable transportation strategies. This study aims to explore the impact of government intervention on pricing strategies, and energy-saving level determination in the transportation sector under conditions of fuzzy uncertainty. While the government looks into three distinct strategies, each with two decision variables, transportation enterprises are considering two alternate scenarios for decision-making. It means that twelve distinct scenarios are being considered by the government. Our analyses reveal that: (1) The government's goals of maximizing social welfare and energy saving cannot be aligned with the enterprises' goals of maximizing profits, regardless of whether decision-making is decentralized or centralized. (2) The carbon cap-and-trade mechanism emerges as the most effective strategy for governmental regulation, whereas transportation enterprises demonstrate optimal responsiveness to subsidy-based policy interventions. (3) Centralized decision-making by transportation enterprises yields superior outcomes across multiple dimensions, including LSSC profitability, social welfare enhancement, and energy conservation efficiency, when contrasted with decentralized decision-making paradigms. (4) The implementation of a carbon cap-and-trade policy by the government, combined with increased investments in environmental awareness and centralized decision-making by transportation enterprises, significantly advances both profit objectives and energy-saving targets.

Abstract: With the introduction of carbon emission policies, subsidy policies, and the promotion of "trade-in" programs worldwide, determining the optimal remanufacturing strategy under various policy environments has become a critical issue. We develop six models to evaluate the effects of three policy combinations—carbon trading alone, carbon trading with consumer subsidies, and carbon trading with remanufacturer subsidies—under authorization and outsourcing remanufacturing strategies. The results show that dual policy of carbon emission trading and government subsidies more effectively promotes remanufacturing than a single carbon trading policy. When consumer subsidies reach a certain threshold, all supply chain members can achieve a win-win outcome, regardless of whether the remanufacturing strategy is authorization or outsourcing. The environmental cost is primarily influenced by carbon emissions from new products. If emissions are high, remanufacturer subsidies should be prioritized; if emissions are lower, consumer subsidies are more effective. Without subsidies, authorization has pricing advantages with low emissions, while outsourcing is more economical with high emissions or under market uncertainty. High carbon trading prices and subsidies increase overall supply chain profits but exhibit diminishing returns as excessive carbon prices increase corporate costs and reduce consumer surplus and social welfare. Moderate subsidies can mitigate these negative effects.

Abstract: Drawing inspiration from manufacturing production processes like chemical and steel manufacturing, the distributed blocking flowshop scheduling problem with preventive maintenance and sequence-dependent setup times (DBFSP/PM/SDST) is studied. First, it is described by a mixed-integer linear programming model with the objective of minimizing the total flowtime. Second, we propose a Q-learning and learning mechanism co-driven approach, integrating it into the discrete grey wolf optimization algorithm (DGWO_Q). In the algorithm, the neighborhood search structure is adjusted using Q-learning based on dynamic feedback from the environment. The balance between exploration and exploitation can be improved by introducing learning mechanisms in the search phase that can guide the grey wolf as it approaches the prey. Furthermore, a differential hunting strategy is designed to prevent the algorithm from falling into local optima. Third, a heuristic that enhances the quality of the initial solution is proposed for the problem characteristics. Finally, the proposed DGWO_Q is compared with four conventional efficient algorithms in numerical experiments on 225 instances of different sizes. Experimental results show that the DGWO_Q algorithm demonstrates excellent performance across test cases of various scales, effectively reducing production cycle time, setup times and the impact of maintenance downtime on production efficiency. It provides an efficient intelligent optimization approach for solving the complex scheduling problem.

Abstract: Landslide prediction is a critical task for ensuring public safety and preventing economic loss in regions prone to such natural disasters. Traditional models for landslide prediction often lack accuracy and precision because of the intricate interactions between various factors that lead to landslide events. To tackle this issue, we introduce an innovative hybrid approach for landslide prediction that combines Time Series Analysis (TSA), Genetic Algorithm (GA), and Support Vector Machine (SVM). TSA decomposes landslide displacement data into trend, seasonal, and residual components, improving the clarity of the data. GA optimizes the hyperparameters of SVM, ensuring the most effective application of the SVM. Finally, the SVM is trained on detrended data, producing a model capable of accurately predicting future landslides. Our experimental outcomes manifest that the TSA-GA-SVM model we advanced performs far better than the individual TSA and SVM models when it comes to forecasting landslide displacement. The hybrid model achieved a mean absolute error of 0.15 m compared to 0.42 m for TSA and 0.38 m for SVM alone. Sensitivity analysis revealed that increasing GA population size improved model stability, while higher mutation rates led to more variable predictions. The model showed good generalization ability, performing well across different regions and under various geological and hydrological conditions. This research not only advances the state of the art in landslide prediction but also provides a practical tool for authorities to implement in their disaster prevention and management strategies.

Abstract: In this paper, we assess the performance of five metaheuristics for the single-machine under different time windows and sequence-dependent setup times, optimizing the total weighted earliness and tardiness: Iterated Greedy Algorithm (IGA), Artificial Bee Colony (ABC), Bat Algorithm (BA), Particle Swarm Optimization (PSO), and Fireworks Algorithm (FWA). Many real-world situations require delivery in a specific time interval, analogous to optimization problems with a time window in the Just-in-Time philosophy. Also, several practical situations require different time intervals to prepare the environment to process the activities depending on what was immediately done and what will be executed next, characterizing the sequence-dependent setup problem. These cases are common among operations handling materials of diverse colors, different temperatures, or high demands on sterilization requirements. Statistical results highlight the superiority of the FWA, with the best results in all the problem dimensions analyzed, especially in the larger-size instances, with only 1.23% average relative deviation against 61.18% of the known Iterated Greedy algorithm.

Abstract: To stimulate the new energy vehicle (NEV) market, China has raised the bar for NEV subsidies so that only NEVs with high endurance are eligible for subsidies. As a result, the NEV manufacturers may cooperate on power batteries, which makes their relationship shift from competition to downstream competition and upstream cooperation, i.e. co-opetition. Based on this, this paper investigates the cooperation conditions between the leading NEV manufacturer and the emerging NEV manufacturer on power batteries under the revised subsidy policy. By establishing a Cournot model, we first analyze the optimal decisions of the two manufacturers under government subsidy policy in competition and co-opetition scenarios, respectively. By comparing the profits of NEV manufacturers in these two scenarios, we derive the conditions under which they can cooperate on power batteries. The results show that whether the NEV manufacturers can cooperate depends on the power battery cost of the emerging NEV manufacturer.