Xiaoyong Zhu

In the process of CNC machining, reducing energy consumption, production costs, and improving machining quality are critical strategies for enhancing product competitiveness. Based on an analysis of machine tool processing mechanisms, calculation models for energy consumption, manufacturing cost, and quality (represented by surface roughness) in CNC lathes were established. These models were optimized using the Egret Swarm Optimization Algorithm (ESOA), which integrates three core strategies: waiting, random search, and bounding mechanisms. With the objectives of minimizing energy consumption, manufacturing cost, and maximizing quality, cutting parameters (e.g., cutting speed, feed rate, and depth of cut) were selected as optimization variables. A multi-objective ESOA (MOESOA) framework was applied to resolve trade-offs among conflicting objectives, and the effectiveness of the proposed method was validated through a case study. The simulation results show that the optimization of cutting parameters is beneficial to energy conservation during the machining process, although it may increase costs. Additionally, under the three-objective optimization, the improvement of surface roughness is relatively limited. The further two-objective (energy consumption and cost) optimization model demonstrates better convergence while ensuring that the surface roughness meets the basic requirements. This method provides an effective tool for optimizing cutting parameters.

Under the background of “carbon peaking and carbon neutrality”, carbon reduction is not only a realistic need for the high-quality development of the national economy, but also a key way for the sustainable development of enterprises. Cold chain logistics has the characteristics of high energy consumption and high carbon emissions. Fresh distribution requirements of fresh agricultural products may increase carbon emissions in the cold chain distribution process. Based on customer satisfaction and aiming at minimizing carbon emission and comprehensive distribution cost, this paper establishes an optimization model of cold chain logistics distribution routes for fresh agricultural products. First of all, a two-objective optimization model is proposed considering customer satisfaction maximization and comprehensive cost minimization, including fixed cost, fuel cost, carbon emission cost, cargo damage cost, and time window penalty cost. And when constructing customer satisfaction function, we mainly pay attention to the time factor. Secondly, a hybrid ant colony algorithm, which combines improved ant colony algorithm and local search algorithm 3-opt, is designed to solve the model. Thirdly, a hybrid ant colony algorithm is applied to the simulation example of fresh agricultural products distribution through experimental simulation, and the results are compared with the traditional ant colony algorithm and improved ant colony algorithm. Finally, the results reveal that the average total cost of the hybrid ant colony algorithm is lower than that of the traditional ant colony algorithm and the improved ant colony algorithm, achieving nearly 8.159% and 4.622% cost savings, respectively, and in terms of customer satisfaction, the new algorithm is slightly better than the other algorithms. The results show that the method can effectively optimize the cold chain logistics distribution routes of fresh agricultural products, and reduce carbon emission and distribution cost.

Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation efficiency and inability to achieve the optimal layout. For this reason, a smart manufacturing assembly line layout optimization model considering AGV path planning with the objective of minimizing the amount of material flow and the shortest AGV path is designed for the machining shop of a discrete manufacturing enterprise of a smart manufacturing company. Firstly, the information of the current node, the next node and the target node is added to the heuristic information, and the dynamic adjustment factor is added to make the heuristic information guiding in the early stage and the pheromone guiding in the later stage of iteration; secondly, the Laplace distribution is introduced to regulate the volatilization of the pheromone in the pheromone updating of the ant colony algorithm, which speeds up the speed of convergence; the path obtained by the ant colony algorithm is subjected to the deletion of the bi-directional redundant nodes, which enhances the path smoothing degree; and finally, the improved ant colony algorithm is fused with the improved dynamic window algorithm, so as to enable the robots to arrive at the end point safely. Simulation shows that in the same map environment, the ant colony algorithm compared with the basic ant colony algorithm reduces the path length by 40% to 67% compared to the basic ant colony algorithm and reduces the path inflection points by 34% to 60%, which is more suitable for complex environments. It also verifies the feasibility and superiority of the conflict-free path optimization strategy in solving the production scheduling problem of the flexible machining operation shop.

In the Industry 4.0 environment, an ideal smart factory should be intelligent, green, and humanized, and the logistics transportation from raw materials to final products in the factory should be completed by smart logistics. In order to address the problems of low efficiency, poor workstation service satisfaction, high distribution costs, and non-greening during the logistics distribution processes in discrete smart manufacturing workshops are required. A mathematical model of optimized multi-objective green logistics distribution paths in a smart manufacturing workshop has been constructed in this study, with low costs, high efficiency, and workstation service satisfaction taken into consideration. Then, this mathematical model was solved with an improved ant colony optimization algorithm. A “time window span” was introduced in the basic ant colony optimization algorithm to prioritize the services to workstations with a relatively high urgency in material demand, with the aim of improving workstation service satisfaction. Lastly, in order to verify the effectiveness of the model and algorithm proposed in this study, a simulation experiment has been conducted on the workstation logistics distribution system in a smart manufacturing workshop to provide convincing evidence for optimizing workstation logistics distribution paths in workshops of discrete manufacturing enterprises.

In order to solve the problems of high carbon emissions, low distribution efficiency and high costs related to the process of material distribution in manufacturing workshops, a multi-objective workshop material distribution path optimization problem model is established, and the model is solved using an improved genetic algorithm. The problem is processed using Gray code and crossover and variation operations with a genetic algorithm. To improve the search accuracy and convergence speed of the algorithm, an adaptive mutation method is proposed to enhance the diversity of the population and to achieve global optimal path objective finding. The improved algorithm is applied to workshop path multi-station logistics path planning, which effectively solves the transport path optimization and station solving problems in workshop logistics distribution, and the convergence speed and convergence accuracy of the algorithm are significantly improved. Finally, a simulation analysis is carried out on the optimization of the production material distribution of a smart gas meter workshop owned by K Company, which is an electronic assembly manufacturing company. We used MATLAB software for the case company logistics distribution route model for data analysis and solving. Due to the consideration of carbon emissions, we did not consider two kinds of experiments, which were two different cases of the optimal path. The experimental results verify that the distribution optimization scheduling model can meet the demands for immediate material distribution in the production workshop, which is conducive to improving material distribution efficiency, reducing logistics costs and achieving the goal of lowering carbon emissions. This optimization model has a certain utility in that in the current context of aiming for carbon neutral and carbon peaking, early low carbon distribution layout can reduce the environmental cost of the enterprise, making material distribution a more environmental economic path.

Reducing carbon emission and raising efficient production are the important goals of modern enterprise production process. The same product can be produced by a variety of equipment, and the carbon emissions and processing time of different equipment vary greatly. Choosing suitable production equipment is an important method for manufacturing enterprises to achieve the efficient emission reduction of production process. However, the traditional production equipment selection mode only gives qualitative results, and it is difficult to provide effective advice for enterprises to choose suitable equipment under the needs of carbon neutrality. To solve this problem, this paper systematically analyzes carbon emission and the time of the turning production process, and a unified calculation model for carbon emission and efficient production of multi-type processing equipment is established. The important point of the article is to research the diversity among between carbon emissions and efficiency levels of the same product produced by different devices. The carbon emissions and efficiency levels of different kinds of equipment can be calculated by the BAS algorithm. By turning a shaft part as an example, the results show that this method can calculate the optimal value of carbon emissions and efficiency of the same product produced by different equipment and can provide suggestions for enterprises to select appropriate equipment for low-carbon and efficient production. This paper provides a reference for further research on the quantitative calculation model for the selection of high-efficiency and low-carbon production equipment.

In the context of Industry 4.0, the lean–green manufacturing system has brought many advantages and challenges to industrial participants. Security is one of the main challenges encountered in the new industrial environment, because smart factory applications can easily expose the vulnerability of manufacturing and threaten the operational security of the whole system. It is difficult to address the problem of the brittleness factor in manufacturing systems. Therefore, building on vulnerability theory, this study proposes a vulnerability index system for lean–green manufacturing systems in a manufacturing company in the context of Industry 4.0. The index has four dimensions: human factors, equipment factors, environmental factors, and other factors. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach was used to calculate the degree of influence, the degree of being influenced, and the centrality and causes of the factors. The causal relationships and key influences between the factors were identified. Then, the dependence and hierarchy of each of the key influencing factors were analyzed using the Matrix-Based Cross-Impact Multiplication Applied to Classification (MICMAC) and Interpretative Structural Model (ISM) methods, and a hierarchical structural model of the factors was constructed. Finally, an intelligent manufacturing system that produces a micro-acoustic material and device was used as an example to verify the accuracy of the proposed method. The results show that the method not only identifies the key brittleness factors in a lean–green manufacturing system but can also provide a guarantee for the safe operation of a manufacturing system. This study provides theoretical guidance for the effective management of intelligent manufacturing systems; moreover, it lays a foundation and provides a new methodology for assessing the vulnerability of manufacturing systems.

Aiming at resolving the problem of low assembly accuracy and the difficulty of guaranteeing assembly quality of remanufactured parts, an optimization classification method for the assembly sequence of remanufactured parts based on different accuracy levels is proposed. By studying the characteristics of recycled parts, based on the requirement that the quality of remanufactured products not be lower than that of the assembly quality of new products, the classification selection matching constraints of remanufactured parts are determined, and the classification selection matching optimization models of remanufactured parts with different precision levels is established. An algorithm combining particle swarm optimization and a genetic algorithm is proposed to solve the model and obtain the optimal assembly sequence. Taking the remanufacturing assembling of a 1.4 TGDI engine crank and a connecting rod mechanism as an example, the comparison of quality data shows that this method can effectively improve the qualified rate of assembly, reduce the cost of after-sale claims, provide new theories and methods for remanufacturing enterprises that need hierarchical assembly, and provide effective guidance for the development of the remanufacturing industry.

The existing research and practices have shown that the coordinated implementation of lean-green manufacturing can have a positive impact on the economic and environmental benefits, which is an effective means to ensure the environmental protection of the production process of manufacturing without damaging their profitability. Within the field of lean-green research, there is still a lack of research to analyze the driving factors for the collaborative implementation of integrated lean and green integration. Although, some scholars and researchers have studied lean and green integration paradigms, their research has mostly focused on lean-green integration practices and their impact on environmental performance and their respective operations. In the context of Industry 4.0, this article investigates the driving forces behind the collaborative integration implementation of a lean-green manufacturing system from the viewpoint of stakeholders. Specifically addressing the issues of correlation and ambiguity in the identification of driving factors, this manuscript proposes an Interpretation Structure Model (ISM) of fuzzy comprehensive Analytic Hierarchy Process (AHP), based on Decision-Making Trial and Evaluation Laboratory (DEMATEL), to determine the importance of the driving factors. Combined with the complex network theory, the evaluation index system is divided into four levels from eight factor categories, including endogenous lean-green driving factors and exogenous driving factors. The fuzzy AHP-DEMATEL-ISM is used to analyze the relationship between indicators and the structure of the indicator system. The complex network which is composed of the indicator system is divided into different levels. The importance of indicators is analyzed from the perspective of the global network, and key factors affecting the driving of lean-green system is analyzed. The integration of the lean-green manufacturing system and organizational synergy are promoted to jointly lead the enterprise toward sustainable development by paying particular attention to the primary impact indicators and aggressively cultivating the key impact indicators.

The key to achieving low-carbon manufacturing is to effectively reduce the carbon emissions of production systems and improve carbon benefits. The use of lean and green tools aids in measuring the added value of products, and increases the efficiency and sustainability of production systems. To address this problem and verify that the synergetic relationship between lean and green innovation increases the efficiency and sustainability in production systems, a new low-carbon manufacturing evaluation indicator—carbon benefit—in lean manufacturing systems was discussed. A low-carbon decision-making model of multiple processes aiming at carbon benefit maximization, as well as the dynamic characteristics of carbon benefit and sustainable process improvements in a lean production system, was established. A case study of a certain satellite dish parts manufacturing line was introduced to analyze and verify the feasibility of the proposed model. After improvement, the processing time of unit parts was reduced from 63 s to 54 s. The workstations were optimized again according to the lean–green manufacturing concept, and the number was reduced by 37.5%. The process was recombined and reduced from 8 to 5 to achieve continuous-flow processing. This reduced the distance by 77 m, and at the same time, the number of operating personnel was reduced, and the after-improvement carbon efficiency increased from 12.98 s/kg CO₂e to 36.33 s/kg CO₂e in comparison with that before the improvement. The carbon benefit after improvement was 193.92% higher than that before the improvement.