The assumption of traditional revenue management on passenger demand is independent and fails to fully account for passenger choice behavior. This paper discusses the revenue management and aircraft assignment problems considering passenger choice behavior. Observations from historical sales data reveal that passengers have a special preference for the lowest fare class. Therefore, the Spiked Multinomial Logit (Spiked -MNL) choice model is used in this paper to predict the potential real demand of passengers. Also, a joint model of revenue management and aircraft assignment considering passenger choice behavior is established, with the objective of maximizing airline profit. Case studies are conducted using actual sales transaction data from an airline to verify the feasibility of the proposed model and to evaluate its performance by comparison in different scenarios. Results show that the joint model yields an average profit of 5% higher than the independent model. In the comparison of different fleet sizes, the 5-aircraft joint model produces the highest profit, with an average profit of 6.6% higher than the 3-aircraft model and 7.3% higher than the 9-aircraft model. In addition, compared with traditional methods of predicting passenger demand using passenger choice models, the Spiked-MNL model can reflect actual passenger purchasing behavior more accurately, with an average profit of 0.7% higher than the Generalized Attraction Model (GAM) and 1.2% higher than the Multinomial Logit (MNL) model.

The non-stationarity and randomness of airport passenger arrivals result in uncertain and highly time-varying requirements for security checkpoints, increasing the difficulty of resource allocation. To address this issue, this paper proposes a method embedding a non-stationary queuing model into a genetic algorithm. Initially, a non-stationary queuing model for a single-stage service system with multiple parallel service desks is established, where the input and service processes are fitted to general distributions. Furthermore, an extended stationary backlog-carryover (SBC) approximation is proposed to quickly solve system performance metrics. Then, the extended SBC approximation is embedded into the genetic algorithm for optimization. Finally, experimental and optimization examples are designed. The accuracy of the extended SBC approximation and the effectiveness of the optimization method are verified by comparing the analytical results with the simulation results. The impact of non-stationarity on system performance and optimization results is also analyzed. Results of applying this method to a practical case show that the optimized allocation scheme reduces the number of security checkpoints by 14.80% compared to the actual allocation scheme, proving its effectiveness.

The study focuses on the scheduling issue of home healthcare workers, incorporating constraints related to mixed time windows and medical supplies into the model. A branch-and-price algorithm is designed for solving the problem, with the branch process and labeling algorithm are improved within the algorithm. In the numerical experiments, the proposed branch-and-price algorithm is compared with the Adaptive Large Neighborhood Search (ALNS) algorithm and CPLEX in terms of performance. The branch-and-price algorithm finds optimal solutions in over 80% of the cases, whereas CPLEX achieves this in less than 20% of the cases. Moreover, in large-scale instances, the branch-and-price algorithm show an improvement of over 15% compared with ALNS algorithm. Sensitivity analysis is conducted on key parameters in the model. Experimental results indicate that different parameters for patient-caregiver matching and penalties for time window violations both impact operational costs; mixed time windows are more efficient than soft and hard time windows, with the total cost under mixed time windows being about 2% lower than that under hard time windows, while also avoiding extreme scenarios; the constraint of carrying medical supplies can affect the total cost by up to 40%, making it a necessary consideration for this constraint in the model.

Integrated surgical scheduling refers to the coordination of various resources required for surgical patients, such as operating rooms, surgical staff, and hospital beds, during the scheduling process. The lack of intensive care resources, such as ICU beds equipped with monitoring equipment and staffed with medical personnel, poses a severe challenge to the healthcare system, such as delayed surgeries and postoperative health deterioration. The policies obtained using traditional surgical scheduling models often ignore the operating pressure of ICU resources. A bi-objective programming model was developed to simultaneously optimize the efficiency of the operating room and the operating pressure of the ICU. A modified genetic algorithm was proposed that enhances the flexibility of exploration by introducing diverse strategies to the mutation operator, and a repair operator was introduced to improve the feasibility of solutions under complex constraints. By employing a simulation-driven initialization approach, this study effectively overcomes the difficulty of generating a valid initial population in solving large-scale problems. Experiments based on real hospital data show that the algorithm obtains near-optimal solutions for small-scale arithmetic cases, and at the scale of 50 patients, the algorithm is able to reduce the maximum operating pressure of ICU by 10.00% to 28.56% compared to the surgery scheduling policy that only considers the operating room.

This study delves into the comprehensive optimization challenge of integrating production, operation, sales, and storage of the industrial chain when large-scale energy enterprises formulate their operational plans. To address the limitations of existing models, which typically cover only a single time period, ignore business conflicts, and treat transportation and inventory separately, this paper develops a multi-period inventory-transportation joint optimization model. The objective is to maximize the total profit and minimize the impact of business conflicts within the enterprise planning horizon. This model considers constraints such as inventory balance across different time periods and production-operation-sales activities, while business conflicts are also taken into account. Given the complexity of the model structure, numerous constraints, and substantial computation cost, this paper introduces a fast genetic algorithm with progressive enhancement of soft constraints. Compared to traditional genetic algorithms, this algorithm can accelerate computation by 5-10 times, significantly improving the efficiency of solving such models. Finally, the model and algorithm are applied to practical cases, while detailed analysis of these cases is conducted, verifying the reliability of the model and the efficiency of the algorithm. The model and algorithm proposed in this paper contribute to providing scientific, rational, efficient, and precise decision support for the optimal operations of the integrated industrial chain in large-scale energy enterprises.

In order to improve the working posture of grinding workers and reduce the risk of developing work-related musculoskeletal disorders (WMSDs), an experimental study is carried out on the working postures of grinding workers in the pressure vessel workshop of a pharmaceutical equipment company. Based on the DMAIC model, a DMASIEC work improvement model is proposed which adds simulation (S) verification analysis in the improvement (I) stage and execution (E) analysis in the control (C) stage. Using the Xsens MVN motion capture system and the Jack TAT tool in a synchronized joint analysis method, an immersive real-world experiment for job posture improvement based on physiological signals and subjective evaluation indicators is designed and carried out. Through the simulation verification of grinding postures using Jack, a guideline for improving grinding worktable and operations is proposed to reduce work fatigue. Results show that the pressure on the L4/L5 spines of grinding workers is about 1/4 of the original pressure when performing the same task, and all working posture ratings are AC1; significant changes (P ＜ 0.05) in sEMG and HRV fatigue indicators are observed in three workers after transitioning from a squatting to a sitting posture; the improved worktable and operational guidance contribute to mitigating the adverse effects of working posture on the musculoskeletal system.

Natural disasters not only bring huge economic losses to society, but also cause serious psychological trauma to victims. To address the negative emotions experienced by disaster victims, this paper proposes a disaster shelter scheme to ensure emotional health, aiming to explore design strategies and methods to alleviate negative emotions of victims in post-disaster temporary shelters. Through the investigation of individuals who have experienced natural disasters, combined with the particularity of post-disaster products, virtual reality (VR) equipment is suggested to study a wider user group. According to the Kano model, the priority of functional requirements is established for disaster shelter designing. Also, the internal space is designed and experimentally analyzed by evidence-based physiological sensors and VR devices. Combined with experimental data and user feedback, the subjective and objective effects of different scenarios on the effect of negative emotion intervention are discussed. Results show that the disaster shelter scheme proposed in this paper improves the negative emotions among occupants, with the combination environment of cool tone-high brightness-hard materials, cool tone-high brightness-soft materials and warm tone-low brightness-soft materials providing the best effect on improving negative emotions. This paper provides reference for the emotional health-related design of temporary placement products after disasters.

To address the time-consuming, labor-intensive, and experience-dependent issues of traditional process action analysis methods in the field of industrial engineering, this paper proposes an intelligent detection method for process actions based on skeleton sequences using action recognition technology to replace traditional manual decomposition methods. A human body posture estimation model is built using a 2D camera and the MediaPipe framework to obtain skeleton sequences, and relevant evaluation metrics are introduced for action quantitative analysis. Also, a convolutional gated recurrent unit (CNN-GRU) based action classification model is trained using skeleton data. Experiments are conducted on a self-built process action dataset, demonstrating that the proposed CNN-GRU model achieves higher accuracy with fewer parameters compared with LSTM and GRU models. Furthermore, by comparing the inference results with standard operating procedures, abnormal actions are identified, providing an effective solution for process action recognition and analysis, which helps to standardize production operations and improve production efficiency.

In the context of upstream supply constraints, in order to ensure the effective operation of the supply chain, an decision model (Strategy 1) involving ordering and pricing decisions between the manufacturer and the brand owner is established in a supply chain where the brand owner adopts a "control" procurement mode, by introducing a supplier with capacity constraints and economies of scale. Furthermore, based on the classic competition-cooperation model, a game model (Strategy 2) is developed for the manufacturer to open direct sales channels. Through theoretical analysis of the game model, the equilibrium results in the supply network are obtained. Combining analytical and numerical analysis, the effects of competition-cooperation games and manufacturer strategic preferences are examined in two scenarios: the extreme high price procurement (for components) and the free-riding procurement. Different from the existing models, this study incorporates the component procurement processes of both manufacturers and brand owners, resulting in a circular supply structure and revealing the effect of exchanging orders through channels. The conclusions of this study can provide suggestions for the transformation and upgrading of manufacturers under supply constraints.

How to develop a closed-loop supply chain for the recycling of waste products and achieve value co-creation is the key issue for the smart phone supply chain to promote circular economy and improve competitiveness. This paper introduces shareholding cooperation and customer engagement, analyzing value co-creation in closed-loop supply chains from multiple perspectives. It explores the effects of shareholding ratio, commission rate and customer engagement on the decisions of supply chain members, the recycling amount of waste products, the profit of supply chain members, the customer surplus, and the total value of the closed-loop supply chain. Results show that shareholding cooperation is beneficial to improving the service level of recycling platforms. When customer engagement with a recycling platform is high or the cost coefficient of recycling service level is low, shareholding cooperation can promote the recycling of waste products. Shareholding by manufacturers in recycling platforms is a profit strategy for recycling platforms; however, in the long term, manufacturers and recycling platforms should set a low shareholding ratio and a moderate commission rate when customer response is high. In this way, the profit of both parties increases, leading to stable shareholding cooperation and promoting the realization of value co-creation in closed-loop supply chains.

This paper investigates the optimal channel operation strategy of manufacturers in the product sales market where two iterations of products coexist. Combined with the retail attributes of B2C e-retailers and the physical characteristics of manufacturer offline direct sales channels, considering consumer showroom behavior towards new products, game models are established from the perspective of manufacturers under three modes: distributing new products and directly selling old products, distributing new products and directly selling both iterations of products, and distributing the two iterations of products. The optimal pricing decisions for two iterations of products under the three modes are analyzed, while the optimal sales mode and product launch strategy of manufacturers are discussed on this basis. Results show that when consumers have low access cost to offline direct sales channels, manufacturers should choose to sell both iterations of products through offline direct sales channels; when the cost of accessing offline direct sales channels is moderate, the optimal decision for manufacturers is to distribute new products online and sell old products offline to avoid the competition effect between two iterations of products; otherwise, manufacturers should choose to distribute new products online and not sell products through direct sales channels, simultaneously, they should play the showroom function of offline channels to obtain incremental benefits. Moreover, consumer showroom behavior can bring incremental demand for new product sales in B2C e-commerce, but it further intensifies the competition for new product demand between online and offline channels.

Currently, changes in international economic and technological connections increase the risk of high-tech supply disruptions in the global supply chain system. For Chinese smart manufacturing enterprises that are undergoing digital and intelligent transformation, balancing the procurement of high-tech supplies from international and domestic suppliers to deal with uncertainties and enhance supply chain resilience has become an important issue. This paper proposes a game model consisting of an overseas supplier and a domestic supplier with technological quality differences and a domestic smart manufacturer, to study whether potential government intervention measures can improve the willingness of Chinese smart manufacturing enterprises to procure from domestic suppliers in the presence of overseas supply disruption risks. Results show that the loss compensation provided by the government lowers the threshold for technological requirements of manufacturers on domestic suppliers. The higher the proportion of government loss compensation, the higher the manufacturer acceptance of domestic supplies. Further study reveals that when the potential quality risk probability of domestic suppliers' products due to lower technological levels is closer to the risk probability of overseas supply disruptions, the loss compensation from the government to smart manufacturing enterprises can more effectively increase the willingness to procure domestically, leading to greater selection of domestic suppliers by smart manufacturing enterprises.

In order to solve the problem of weak downstream orders for new products caused by uncertain demand in the consumer electronics industry, this paper designs a put option contract between a risk-neutral manufacturer and a risk-averse retailer, considering service level constraints. By comparison, this paper discusses whether the put option contract can motivate the risk-averse retailer to increase orders, and analyzes the effects of the retailer's risk aversion coefficient, service level constraints and put option contract parameters on retailer decisions and the profit of related members. Results show that an increase in the retailer's risk aversion level may inhibit the increase in the optimal initial order quantity, but has a positive impact on the manufacturer's expected profit. High service levels are beneficial to increasing the retailer's optimal initial order quantity and the manufacturer's expected profit, but not to increasing the retailer's maximum conditional risk value. The existence of put option contract is beneficial to all supply chain members.

In order to realize the complementary operation of "wind, photovoltaic and energy storage" sources in microgrid systems, and simultaneously to inspire the three types of energy prosumers to actively participate in power trading, this paper establishes a Stackelberg game model between the microgrid scheduling center and the three types of prosumers. In the model, as the leader, the microgrid scheduling center determines the purchase and sale prices for each time period, As followers, the prosumers send their respective electricity demand to the scheduling center according to these prices. Then, the scheduling center adjusts the purchase and sale prices based on the received electricity demand, ultimately achieving a game equilibrium. This paper extends the power trading models in the existing literature primarily focused on unilateral decision-making in microgrids, and proves the existence and uniqueness of the equilibrium solution of the Stackelberg game model in the situation that each game participate makes independent rational decision. This paper also develops an equilibrium solution algorithm based on differential evolution, and verifies the effectiveness of the model and algorithm through numerical experiments. The results of this paper provide a scientific basis for setting the purchase and sale prices of electricity by microgrid scheduling centers, which promotes the efficient utilization of distributed renewable energy.

With the rapid development of the sharing economy, the financing leasing industry is undergoing a crucial transformation. Decision-makers, confronted with uncertainty regarding the duration of equipment leasing demands, can achieve better decision-making by utilizing shared two-stage online algorithms and competitive analysis. This paper introduces a two-stage online leasing problem in the context of the sharing economy for financing leases. By incorporating variables such as shared revenue and leasing period ownership, the proposed model improves online competitive strategies to better reflect real-world scenarios, thereby providing decision-makers with the optimal online competitive ratios for strategy selection. Results reveal that distinct strategies exhibit variations in the optimal switching points, and selecting the appropriate time duration for strategy transitions can reduce decision costs and enhance the competitiveness of financing leases. This study highlights the importance of strategy transition timing in financing leasing, offering both practical and theoretical contributions.

Carbon footprint measurement and estimation are important indicators for low-carbon supply chain assessment, but currently there is a lack of unified carbon footprint measurement standards and boundaries, and traditional carbon footprint measurement methods require significant computational costs. Therefore, a two-stage full lifecycle carbon footprint estimation method that calculates first and predicts later has been proposed. In the first stage, the power grid material supply chain is divided into five stages, and corresponding calculation models are constructed to achieve quantitative description and evaluation of carbon footprint; In the second stage, a supply chain lifecycle carbon emission prediction model based on long short-term memory neural network (LSTM) was constructed using cable products as carbon sources. Finally, numerical experiments were conducted based on carbon footprint management data of the power grid supply chain from 2020 to 2023, with a prediction accuracy of 99.3%. By comparing the models constructed with BP neural network and GABP neural network, the accuracy and superiority of the models have been demonstrated, achieving effective accounting and prediction of carbon footprint.