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.

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.

Making a reasonable and effective production plan is always an essential and challenging task in industrial production. A joint optimization model of production and maintenance is proposed in this paper, which considers the structural relationship between production units and the influence of the unit state on demand. A three-unit series–parallel system is selected to calculate the steady-state probability density function of the system, and the model is established by dividing different maintenance situations in one cycle. By analyzing the composition of expected cost and expected time in each situation, the expected cost rate is calculated by using renewal reward theory. The objective function of the model is to minimize the expected cost rate. The genetic algorithm is improved according to the model characteristics. The application of the model is illustrated by a case, and the sensitivity analysis is set to show the influence of different parameters on the decision-making results of the system, providing ideas for decision-makers. Finally, the contrast experiments show the advantages of the proposed model and method.

To make a production plan fit with the actual situation better, we focus on the production system with equipment, and design a joint optimization strategy combining the economic production quantity (EPQ) model with condition-based maintenance. In this strategy, different maintenance operations are carried out when the state of the equipment exceeds different thresholds. The numerical relationship between product demand rate and equipment state is established, and the average cost rate is calculated by using the renewal reward theory. An optimization model is proposed, which takes the lowest average cost rate as the objective function with the economic production quantity and condition-based maintenance threshold are taken as the decision variables. An improved genetic algorithm with an elite strategy is used to solve the model. The results shows that the cost of the proposed model is lower and the sensitivity analysis can describe the relationship between the various elements of the production system clearly, understand the system state quickly, and demonstrate the proposed model.