A Customer Demand Mining Algorithm Based on Online Comments and Machine Learning

In the current market environment, the phenomenon of product homogenization is severe. If enterprises cannot deeply understand customer needs and provide differentiated products or services, it is difficult to stand out in the competition. In order to effectively improve overall customer satisfaction and enhance the market competitiveness of enterprises, a customer demand mining algorithm based on online comments and machine learning is proposed. Collect customer demand information data through online comments and process the collected data with redundant information to improve the efficiency and accuracy of demand mining. On this basis, customer demand attribute features have been further extracted, and a customer demand clustering mining model has been constructed using a self-organizing mapping neural network. By training the model, the final clustering mining results can be obtained, thus achieving precise mining of customer needs. This study clearly addresses a key issue in the current field of consumer demand mining: how to efficiently and accurately identify and utilize consumer demand information in online comments. By constructing a clustering mining model based on the Self-Organizing Maps (SOM) neural network, this study fills the literature gap in this field and provides more accurate and practical consumer demand analysis methods for enterprises. The experimental results show that, compared with the three comparison methods, the proposed method has a 98% feasibility of customer demand mining and 92% customer satisfaction. It shows that the proposed method has high feasibility and customer satisfaction for customer demand mining and has a better overall customer demand mining effect. This provides strong support for improving overall customer satisfaction and corporate competitiveness

Evolutionary Game Analysis of Quality and Safety Regulatory Mechanism In Agricultural E-Commerce Supply Chain Considering Government Subsidy

The quality and safety of agricultural products on e-commerce platforms are increasingly of increasing concern in China. This study constructs a three-party evolutionary game model to analyze the production decisions of producers and the regulatory decisions of e-commerce platforms. It explores the influence of online social supervision and the government's subsidy policy. The results show that social supervision helps to externally discourage producers from colluding with e-commerce platforms to produce 'fake' green agricultural products. On this basis, a reasonable subsidy policy in addition to the existing punitive measures can internally motivate producers and e-commerce platforms to consciously produce and sell green agricultural products in a compliant manner, thus realizing a win-win situation and a collaborative environment for all three parties. Moreover, the government should construct a long-term subsidy mechanism for e-commerce platforms and a short-term subsidy mechanism for producers, so as to guarantee the sustainable development of the green agricultural e-commerce supply chain

The Impact of Corporate Social Responsibility on Sustainable Development Performance - Mediating Effect of Dual Green Innovation

Green sustainable development remains the cornerstone of China's economic progress and the guiding principle for the transformation and advancement of its manufacturing sector. Aligned with China's objectives of achieving a "carbon peak" by 2030 and "carbon neutrality" by 2060, expediting the green development of manufacturing enterprises and enhancing their sustainable development performance are crucial to ensuring their long-term health and prosperity amid transformation and upgrading. This holds the key to realizing sustained and healthy growth for manufacturing enterprises as they navigate the process of transformation and advancement. According to stakeholder theory and natural resource-based theory, this study evaluates Chinese listed manufacturing enterprises, utilizing panel data spanning 2012-2021 to empirically analyze the relationship between CSR, dual green innovation, sustainable development performance, and redundant resources in these enterprises. A model is constructed to explain the effect of CSR on sustainable development performance. The findings demonstrate that CSR significantly cultivates both continuous and disruptive green innovation, elevating corporate sustainability performance. Continuous and disruptive green innovation represents positive mediating factors in this relationship. The moderating effect analysis indicates that non-sedimentary redundant resources exert a positive moderating effect between CSR and dual green innovation, while the moderating effect of sedimentary redundant resources between the two remains insignificant. Further exploration indicates that in the short term, continuous green innovation exhibits a more significant and positive effect on sustainability performance. Conversely, in the long run, disruptive green innovation demonstrates a greater positive effect on sustainability performance. Considering these conclusions and the specific characteristics and requirements of manufacturing enterprises, this paper proposes relevant recommendations to assist enterprises in effectively enhancing their sustainable development performance

A Genetic Algorithm for Collaborative Truck-Drone Routing and Scheduling Problem in Surveillance Operations

Drones can access areas that are difficult to reach for ground surveillance resources. However, drones have limited surveillance operations over large areas because of their short flight durations. To tackle the limitations of drones, one viable approach to use trucks as mobile platforms for the takeoff and landing of drones, ensuring close proximity to surveillance areas. However, coordinating the trucks and the drones is challenging due to the combinatorial complexity of scheduling their surveillance routes collaboratively. Motivated by this challenge, this study develops a genetic algorithm to solve the truck-drone routing and scheduling problem for surveillance. This algorithm determines the routes and schedules of multiple trucks and drones to monitor a given set of surveillance areas, aiming to minimize the time spent completing all surveillance operations. A set of numerical experiments is performed to validate the performance of the algorithm and discuss the managerial implications of collaborative surveillance

Joint Optimization of Vehicle and Driver Scheduling for Pure Electric Bus Line

Pure electric buses are rapidly being promoted in various cities. However, the unbalanced distribution of tasks and underutilization of vehicles and drivers is a growing problem. This paper proposes an optimization model to solve these problems while considering factors such as vehicle charging demand and driver working hours. The objective of the model is to minimize the fleet size and number of drivers, aiming to strategically schedule the rest time for drivers and charging time for vehicles within the same period of time. The model involves an improved shifting trip departure time algorithm for charging and resting time windows and an algorithm for generating vehicle and driver chains. Finally, the proposed model and algorithms are applied to bus route no. 31 in Harbin. The results indicate that reasonable scheduling plans can reduce the fleet size and number of drivers while increasing the average utilization rates of buses and drivers

Smart Manufacturing Systems Under Transportation and Energy Management Constraints

In the Industry 4.0 era, achieving high energy efficiency and flexibility in manufacturing systems remains a critical challenge, particularly in the integration of Automated Guided Vehicles with on-board batteries. This study addresses this gap by introducing a novel evolutionary approach that simultaneously optimizes task scheduling, transport operations, and vehicle battery management—pioneering a comprehensive solution for manufacturing efficiency. Unlike traditional methods, our approach not only reduces lead times and operational costs but also extends battery lifespan through improved energy management strategies. Experimental results demonstrate significant advancements, including a 29.25% improvement in battery levels and a 3.64% reduction in production time. These results surpass established benchmarks in 88.23% of test cases. These outcomes not only enhance sustainability and operational resilience but also provide actionable insights for implementing more efficient and competitive Industry 4.0 manufacturing systems. By addressing critical challenges in energy and operational management, this study lays the groundwork for future innovations in sustainable and adaptive manufacturing practices

Evolutionary Game Analysis of Construction Risk Management of Subcontracting Project under EPC Mode

This paper develops a two-party evolutionary game model between the general contractor and the construction subcontractor under the Engineering-Procurement-Construction (EPC) mode to determine the positivity of strict management by the general contractor as well as high standards of production by the subcontractor under different social and production conditions. The results show that whether the game system converges to the ideal Evolutionary Stable Strategy (ESS) depends on both parties’ benefits in the project, reputation value in the industry, additional losses incurred by defaults, and supervision costs. In the process of EPC project construction risk management, each game party should clarify contractual responsibilities, set specific penalties, build an information disclosure platform, evaluate performance regularly, and establish a reward system based on quality to promote the sustainable and healthy development of the EPC project mode

Strategic Choices of Online Retailers in Live-Streaming E-Commerce

Some online retailers, based on traditional channels, have launched live-streaming channels. To study the optimal live-streaming choices of online retailers, this paper models the supply chain composed of a manufacturer and an online retailer by establishing three modes: no live-streaming mode, influencer live-streaming mode, and retailer self-live-streaming mode. The online retailer's optimal choice of live-streaming mode is derived by analyzing the equilibrium solutions. The results suggest that when the sales ability of the employee-streamer and the live-streaming consumer purchase rate are high, it's always advantageous to opt for retailer self-live-streaming mode. Affected by the influencer's commission rate and fixed participation fee, the influencer live-streaming mode is harmful to the online retailer. However, if the influencer chooses a profit-sharing mechanism, the online retailer and influencer can achieve a Pareto profit improvement, and online retailers will open the influencer live-streaming mode

Pickup Hub Location Problem with Prospective Customers in Rural Area: A Case Study

This paper addresses the pickup hub location problem in rural areas, considering dynamic changes in prospective customers and optimizing equity. The problem involves selecting optimal pickup hub locations from candidate hubs while minimizing transportation costs, measured as the distance between a set of determined customers and pickup hubs. A nonlinear fractional integer programming model is developed to formulate the problem. A scenario-based Dinkelbach’s algorithm combined with a mathematical reformulation approach is proposed to solve the problem efficiently. The effectiveness of the proposed method is demonstrated through a case study on the location selection of smart cigarette delivery lockers. The results highlight the method’s ability to balance equity, offering a practical solution for logistics planning in rural areas. The key contributions of this study are: (1) a novel pickup hub location model that accounts for dynamic customer changes and (2) validation of the approach through a real-world case study, showcasing its applicability and effectiveness

A New Loss Function Based on BURR XII for Using Risk Estimation

Achieving a competitive advantage in today's fast-paced and globally competitive business environment is one of the main goals for organizations across all industries. To attain this, businesses must adopt effective strategies and tools to enhance their operational efficiency, product quality, and overall customer satisfaction. One of the most significant tools that can help businesses achieve this goal is the implementation of quality control methods. Quality control not only ensures that products and services meet predefined standards but also helps reduce costs, increase customer loyalty, and maintain a sustainable competitive edge. In recent years, the importance of loss functions in quality assurance has grown considerably. Loss functions are used to quantify the deviation of a product's performance or quality from its desired target, translating this deviation into a monetary loss. This concept enables businesses to assess the broader impact of poor quality, not only on the organization but also on society as a whole. The monetary value of the loss represents the cost associated with a product's failure to meet expectations, including customer dissatisfaction, warranty claims, and potential reputational damage. Advancements in statistical methodologies, particularly those involving inverted probability density functions (PDFs), have opened new avenues for the application of loss functions. Inverted PDFs allow for a more detailed understanding of quality-related losses. This paper introduces the Inverted Burr XII Loss Function (IBXIILF) as a novel member of the inverted probability loss function family. The IBXIILF provides a robust framework for evaluating and minimizing quality-related losses in various industrial settings. The performance and applicability of the IBXIILF are demonstrated through a comparative study and an industrial example, highlighting its practical relevance and effectiveness in monitoring losses