Emergencies such as machine breakdowns and rush orders greatly affect the production activities of manufacturing enterprises. How to deal with the rescheduling problem after emergencies have high practical value. Meanwhile, under the background of intelligent manufacturing, automatic guided vehicles are gradually emerging in enterprises. To deal with the disturbances in flexible job shop scheduling problem with automatic guided vehicle transportation, a mixed-integer linear programming model is established. According to the traits of this model, an improved NSGA-II is designed, aiming at minimizing makespan, energy consumption and machine workload deviation. To improve solution qualities, the local search operator based on a critical path is designed. In addition, an improved crowding distance calculation method is used to reduce the computation complexity of the algorithm. Finally, the validity of the improvement strategies is tested, and the robustness and superiority of the proposed algorithm are verified by comparing it with NSGA, NSGA-II and SPEA2.

The dense population and the large amount of domestic waste generated make it difficult to determine the best route and departure time for waste removal trucks in a city. Aiming at the problems of municipal solid waste (MSW) removal and transportation not in time, high collection and transportation costs and high carbon emissions, this paper studies the vehicle routing problem of municipal solid waste removal under the influence of time-dependent travel time, traffic congestion and carbon emissions. In this paper, a dual objective model with the lowest total economic cost and the highest garbage removal efficiency is established, and a DCD-DE-NSGAII algorithm based on Dynamic Crowding Distance and Differential Evolution is designed to improve the search ability, improve the convergence speed and increase the diversity of the optimal solution set. The results show that: according to the actual situation of garbage collection and transportation, the method can scientifically plan the garbage collection and transportation route, give a reasonable garbage collection scheme and departure time, and effectively avoid traffic congestion time; Through algorithm comparison, the algorithm and model proposed in this paper can reduce collection and transportation costs, improve transportation efficiency and reduce environmental pollution.

The optimization of preventive maintenance and spare part ordering strategies for modern production equipment is of utmost importance, given its substantial influence on the reliability of equipment systems. Furthermore, the optimization problem discussed here has a direct impact on the reduction of maintenance expenses, thus making it a significant area of research. The optimization of preventive maintenance and spare parts ordering techniques for contemporary industrial equipment, which is a massive and complex system, faces substantial obstacles notwithstanding prior research efforts in the subject. Prior studies have typically assumed a fixed lead time for spare parts ordering, often leading to discrepancies with actual practice. When faced with a critical component failure, such as rolling bearings, it is not advisable for the decision maker to strictly adhere to the ordering strategy. Therefore, this paper presents a novel approach to the maintenance management method, which optimizes preventive maintenance and spare parts ordering strategies using a dynamic early warning period model based on different equipment states. The model incorporates two maintenance approaches, namely normal ordering and emergency ordering, and the equipment will adopt the corresponding maintenance method according to its state. Furthermore, the model takes imperfect detection of equipment states into account since equipment monitoring is not always accurate. Numerical experiments were conducted using rolling bearings, which are a crucial component in typical mechanical equipment, as a case study. The findings indicate that the improved model exhibits a unit time cost of 1.3021, whereas the original model has a unit time cost of 1.3611. Consequently, the new model effectively reduces the maintenance cost. This new method can better resolve the coordination challenge between preventive maintenance and spare parts ordering for equipment, the enhancing of equipment system reliability, and reduce maintenance expenses. In summary, the text presents a significant contribution in the form of a proposed preventive maintenance model that offers increased flexibility, aiming to effectively reduce maintenance costs.

Maintenance activities mostly depend on the specific conditions of individual equipment, being defined as personalized businesses. In order to improve the efficiency of maintenance activities for complex equipment in lots, the thinking of mass customization is used. After the modular technology used for generic maintenance model, the product/service was divided into mandatory and optional modules, which can form multiple optional maintenance service solutions. Considering the characteristics of maintenance activities and customers’ personalized maintenance requirements, configuration optimization is used to find the most satisfied maintenance solution under different objectives. This paper aims to provide the configuration optimization ideas and solutions for complex equipment maintenance services. A multi-objective optimization model was established, and an algorithm based on Non-Dominated Sorting Genetic Algorithms (NSGA-II) was proposed to solve this configuration optimization model. Finally, the maintenance service of the Electric Multiple Units (EMU) bogie was taken as an example to verify the feasibility of the model and the algorithm.

Since the commercialization of 5G, the government has actively encouraged 5G industry chain enterprises to accelerate the progress of 5G. Bundling is a popular means to expand 5G subscribers and improve 5G market coverage. Considering the characteristics of bundling, this study establishes a secondary supply chain composed of a terminal manufacturer and a telecom operator under the condition of network externality strength. In this supply chain, the product quality of the terminal manufacturer is complementary to the service quality of the telecom operator. Using Steinberg’s theory, we derive the optimal value of each decision variable in a centralized mode and a decentralized mode and take profit maximization as the goal. This paper also designs a contract of bidirectional cost sharing and revenue compensation for supply chain coordination. Finally, the influence of network externality strength and a mass-additive factor on the supply chain is discussed using numerical analysis. The results show that higher network externality strength has a significant impact on product pricing, the quality of each entity and the profit of the supply chain. At the same time, the degree of complementarity between the terminal product quality and the telecommunications service quality affects whether consumers choose to buy contract products. A higher degree of complementarity promotes the market inflow into high-end consumers.

The product’s manufacturing process has an evident influence on product quality. In order to control the quality and identify the critical procedure of the product manufacturing process reasonably and effectively, a method combining genetic back-propagation (BP) neural network algorithm and grey relational analysis is proposed. Firstly, the genetic BP neural network algorithm is used to obtain the key quality characteristics (KQCs) in the product manufacturing process. At the same time, considering the three factors that have an essential impact on the quality of the procedures, the grey correlation analysis method is used to establish the correlation scoring matrix between the procedure and the KQCs to calculate the criticality of each procedure. Finally, taking the manufacturing process of the evaporator as a case, the application process of this method is introduced, and four critical procedures are identified. It provides a reference for the procedure quality control and improvement of enterprise in the future.