With the deepening of power market reform, small and medium-sized users have gradually been included in the agency power purchase mechanism. The contradiction between price fluctuation risks, load uncertainty and cost control faced by these users has become increasingly prominent. These users are numerous, but their loads are scattered and their risk tolerance is weak. The traditional static electricity price model is difficult to adapt to the dynamic changes of the market and the differentiated demands of users. Therefore, this paper proposes a dynamic adjustment framework for the agency power purchase electricity prices of small and medium-sized users, which includes market perception, risk quantification, optimization decision-making, and execution feedback. This framework is based on the price risk quantification model, multi-objective optimization function, and improved dynamic weight algorithm, integrating historical price trend weighting, risk cost trade-off, and real-time load response feedback, to achieve the collaborative optimization of power purchase costs, risk losses, and user satisfaction. Experimental results show that this method performs well in three typical scenarios of stability, fluctuation, and extreme conditions: the price risk loss is reduced by 32.7% to 41.2%, the average power purchase cost is stable at 0.38 yuan/kWh to 0.42 yuan/kWh, user satisfaction reaches 92.3%, and the comprehensive performance is significantly superior to the traditional fixed electricity price method, single cost optimization method, and static risk control method. In scenarios of price sudden change and large load peak-valley difference, the risk loss is still lower than 8.5%, the response delay is controlled within 50 ms, demonstrating good robustness and real-time performance. This research provides an efficient and adaptive electricity price adjustment technical path for agency power purchase of small and medium-sized users, which can be applied to various types of power market environments, differentiated user groups, and dynamic load demands.

The increasing integration of distributed energy resources (DERs) introduces significant uncertainty for Virtual Power Plants (VPPs) in electricity spot markets, leading to market risks and potential deviation penalties. This paper presents a unified framework that addresses these challenges by forecasting probabilistic tradable capacity of VPPs using a Bayesian Patch Temporal Shift Transformer (Bayes-PatchTST) to capture the dynamic flexibility of wind, solar, energy storage, and electric vehicle resources; Implementing a two-stage risk-aware bidding strategy for day-ahead and intra-day markets incorporating Conditional Value at Risk (CVaR) to explicitly control market risk; Introducing a hybrid benefit allocation mechanism combining Shapley value and Nash bargaining to ensure incentive-compatible revenue distribution among heterogeneous resources. Case studies demonstrate that the proposed method improves market revenue, reduces deviation penalties, and enhances internal coalition stability, providing a practical and systematic approach for translating physical uncertainty into economically optimized and fair market participation.

This work presents an advanced particle swarm optimization algorithm featuring two decoding schemes, namely PSODS-I and PSODS-II, for solving the Vehicle Routing Problem with Time Windows (VRPTW). Both schemes begin by generating a customer precedence list, followed by the formation of a vehicle precedence list and subsequent route construction. To further enhance solution quality and reduce transportation cost, an intelligent local search mechanism is developed based on a heuristic improvement strategy. The proposed schemes are evaluated on Solomon's benchmark instances with 25 and100 customers using key performance indicators, including total travelled distance and number of vehicles. In addition, the consistency and robustness of the solutions are assessed and compared with the best known solutions and several state-of-the-art methodologies. Comparative analysis demonstrates that the proposed approach, particularly PSODSII, is highly competitive in terms of both fleet size and total distance travelled for solving the VRPTW.

There are many uncertainties in the process of technology projects "Unveiling and Commanding" systems which can easily lead to the risk of failing to achieve the expected effect of technology quality. In order to ensure that the implementation of the system can achieve the expected results, the indicator system for risk assessment of technology projects "Unveiling and Commanding" system is established on the basis of expert interviews and field research. The improved game theory multiple combination weighting method is used to weight the indicators to reduce error risk of the weight results. This paper makes a comprehensive risk assessment of technology projects "Unveiling and Commanding" systems from the two dimensions of risk possibility and risk impact degree based on two-dimensional cloud model, so as to reduce the subjectivity, randomness and fuzziness of assessment results. The example verifies that the model is reasonable and effective, and has a certain guiding role in the improvement of technology projects "Unveiling and Commanding" system.

With the deepening of power market reform, small and medium-sized users have gradually been included in the agency power purchase mechanism. The contradiction between price fluctuation risks, load uncertainty and cost control faced by these users has become increasingly prominent. These users are numerous, but their loads are scattered and their risk tolerance is weak. The traditional static electricity price model is difficult to adapt to the dynamic changes of the market and the differentiated demands of users. Therefore, this paper proposes a dynamic adjustment framework for the agency power purchase electricity prices of small and medium-sized users, which includes market perception, risk quantification, optimization decision-making, and execution feedback. This framework is based on the price risk quantification model, multi-objective optimization function, and improved dynamic weight algorithm, integrating historical price trend weighting, risk cost trade-off, and real-time load response feedback, to achieve the collaborative optimization of power purchase costs, risk losses, and user satisfaction. Experimental results show that this method performs well in three typical scenarios of stability, fluctuation, and extreme conditions: the price risk loss is reduced by 32.7% to 41.2%, the average power purchase cost is stable at 0.38 yuan/kWh to 0.42 yuan/kWh, user satisfaction reaches 92.3%, and the comprehensive performance is significantly superior to the traditional fixed electricity price method, single cost optimization method, and static risk control method. In scenarios of price sudden change and large load peak-valley difference, the risk loss is still lower than 8.5%, the response delay is controlled within 50 ms, demonstrating good robustness and real-time performance. This research provides an efficient and adaptive electricity price adjustment technical path for agency power purchase of small and medium-sized users, which can be applied to various types of power market environments, differentiated user groups, and dynamic load demands.

Large earthquakes often disrupt road networks, severely hindering the timely delivery of emergency supplies. This paper studies a truck–UAV collaborative emergency routing problem under road blockage conditions. We develop a mixed-integer programming model that coordinates truck and UAV operations to minimize total emergency response time while accounting for payload, endurance, demand satisfaction, and road repair constraints. To solve the problem efficiently, we propose an improved Variable Neighborhood Search algorithm with greedy initialization (VNS-G), together with a benchmark variant based on random initialization (VNS-R). Computational experiments on instances of different sizes are conducted to evaluate the proposed approach. The results show that VNS-G can obtain high-quality solutions close to those of CPLEX on small-scale instances. On medium-scale instances, it provides substantial computational savings while maintaining competitive solution quality, reducing computation time from 682–3496 s for CPLEX to 95–340 s in the tested cases. For large-scale instances, where exact optimization becomes computationally impractical, the proposed heuristic remains effective in generating feasible solutions within operationally meaningful time. A case study of the earthquake-prone Ya'an region further illustrates the model's practical applicability. Sensitivity analysis reveals a mechanism-based managerial insight: UAV endurance primarily affects reachability, whereas UAV payload more directly improves response efficiency by reducing the need for repeated sorties. This suggests that, once reachability is ensured, improving payload capacity is likely to generate greater operational benefits than further extending endurance.

Optical film cutting is a critical upstream process in LCD and electronics manufacturing, characterized by high material costs, diverse order specifications, and frequent demand fluctuations that complicate production planning. In practice, manufacturers must coordinate cutting methods, manage semi-finished inventory across multiple periods, and handle recyclable waste simultaneously. However, these decisions are often made independently, resulting in excessive material usage, unstable inventory levels, and avoidable waste. This study develops a multi-period optimization framework for LCD optical film cutting that integrates two production routes, namely slitting and miter cutting, with inventory control and waste recycling. The model captures key operational trade-offs, including process selection, timing of intermediate production, and recycling decisions. It first minimizes total costs, incorporating material, processing, inventory, and waste-related costs, and is further extended into a bi-objective formulation that jointly considers operational cost and waste generation. The primary contribution of this research is to provide an integrated decision framework that reflects real production conditions in electronics manufacturing. By linking cutting, inventory, and recycling decisions across multiple periods, the model enables more coordinated planning and improved resource utilization. A case study with large-scale order data shows that the proposed approach can reduce waste, stabilize inventory, and improve the balance between cost efficiency and environmental performance. The analysis also reveals that cutting assignments tend to concentrate around several preferred angle configurations under realistic production conditions, suggesting the existence of practical process preferences and recurring operational patterns in optical film manufacturing. Overall, this study offers a practical planning tool for optical film converting operations and is applicable to other high-value, roll-based materials in precision manufacturing environments where inventory linkage, process flexibility, and recyclable waste are critical considerations.

Centralized alliance procurement exerts a profound impact on pharmaceutical enterprises innovation, which is an essential pathway for pharmaceuticals to enter hospitals in China. In order to explore whether alliance procurement inhibits or promotes innovation transformation of pharmaceutical enterprises, this study constructs an evolutionary game model between local government and pharmaceutical enterprises. Based on the data of some provinces' centralized procurement platforms, online pharmacies and China Drug Administration, the model parameters were assigned, and the dynamic evolution and influencing factors of pharmaceutical enterprises' innovation behavior under alliance procurement were studied through simulation. The results show that the buyer power generated by alliance procurement affects the innovation decision of pharmaceutical enterprises, and the increase of buyer power promotes the innovation of pharmaceutical enterprises to a certain extent. When buyer power exceeds the threshold value, pharmaceutical enterprises tend to innovate in the short term, but stabilize at a non-innovation strategy in the long term. At the same time, research and development cost, initial willingness to innovate, innovation subsidies and other factors affect the speed of enterprises to stabilize the strategy. This study provides insights for the government to promote the innovation transformation of pharmaceutical enterprises under centralized procurement.

AI technology has become a key means to improve product quality information disclosure, alleviating information asymmetry in supply chains. This study investigates a two-echelon supply chain in which a single supplier interacts with two competing retailers that are differentiated in terms of product quality. Four AI adoption strategies are considered, including non-adoption of AI by both retailers (NN), AI adoption only by the high-quality retailer (AN), AI adoption only by the low-quality retailer (NA), and AI adoption by both retailers (AA). We further analyze how key factors, such as quality competition intensity, AI efficiency coefficient, AI investment level and information asymmetry degree, affect supply chain decisions and optimal AI adoption strategies of all supply chain members. The results reveal that the high-quality retailer always benefits from AI adoption and gains the highest price premium under the AA strategy. In contrast, the low-quality retailer can achieve positive profits only when the fixed cost of AI platforms is below a critical threshold. The supplier achieves optimal profits under the AA strategy with moderate competition and under the NA strategy with intense competition. Core parameters such as the AI efficiency coefficient and information asymmetry jointly influence corporate AI adoption decisions, and the NN strategy is preferred amid severe information asymmetry.

This study presents a Reinforcement Learning (RL)-assisted Genetic Algorithm (GA) framework for the Multi-Objective Capacitated Vehicle Routing Problem with Time Windows (MOCVRPTW). In this problem, a set of homogeneous vehicles depart from a depot, visit all customers exactly once, and return back to the depot. The routes of the vehicles are constructed by considering three objectives: minimizing the total travel time, minimizing the number of vehicles, and maximizing the satisfaction obtained from the customers who are visited within their time windows. We propose using an NSGA-II-based approach that is assisted by Q-learning-based operator selection methods for this problem. Unlike traditional GAs that use fixed operators, the proposed approach enables learning-based selection of each operator (crossover and mutation) considering the current performance of solutions. We make tests with five different Q-learning-based operator selection strategies and compare their results to using fixed or randomly selected operators by nonparametric statistical methods. The results show that all Q-learning-based operator selection strategies outperform the fixed-operator approach, whereas the random selection strategy is outperformed by four. In addition, when the best operator for each state of solutions is found considering all solution approaches and used in NSGA-II throughout the algorithm, it consistently results in the best performance among all. Overall, the results demonstrate that the proposed RL-supported GA framework provides a competitive alternative in terms of Pareto-front solution quality for MOCVRPTW, and learning-based operator selection can be an effective mechanism for adaptively controlling the evolutionary search process.