High-end Equipment Manufacturing Research Articles

Research on Application and Quality Evaluation of Intelligent Teaching Model of AI Three Teachers in the Course of Motion Control Technology

Motion control technology is an important branch of automation, and is one of the important development directions of the emerging industry of high-end equipment manufacturing. As a comprehensive discipline with the intersection of mechanical, electrical and electronic, its teaching mode faces many challenges. There are practical problems that need to be solved, such as teaching content not matching seamlessly with post knowledge and skill literacy requirements, teaching project design is not high in modularity, and teaching evaluation system is not perfect. These problems seriously restrict the cultivation of students' technical practical ability. This project proposes the "intelligent teaching model of AI three teachers for the course of Motion Control Technology", which emphasizes student-centered, builds a core curriculum system that connects with the technology of field engineers in enterprises, and proposes the three-way collaborative teaching and education strategy of "vocational school Teacher + Al Teacher + enterprise tutor" to enable students to participate in learning in an all-round way. And timely access to feedback information, the formation of continuous improvement of the teaching cycle. Optimize the teaching team and teaching mode, improve the teaching quality, meet the diversified learning needs of students, and promote the continuous innovation and development of education and teaching.

Optimizing profiled ring preforms for enhanced deformation uniformity using the isothermal field method

One of the main challenges faced by the profiled ring rolling (PRR) process is to ensure the deformation uniformity of the ring, which is closely related to the design of the ring preform. The paper explores the application of the isothermal field method (IFM) in designing preforms for PRR processes, targeting the aerospace sector's high-end equipment manufacturing. Through the integration of advanced computational models and practical design principles, a comprehensive approach involving IFM, finite element simulation, support vector machine modeling, and genetic algorithm optimization is employed to determine the geometric dimensions of rational preforms. The material flow behavior during the PRR process is analyzed, and the effects of different isotherms combinations on strain, stress and temperature distribution are discussed. The findings demonstrate that adjusting the preform's shape during the section forming stage can optimize the uniformity of strain, stress, and temperature within the forging. The stable ring rolling process revealed no macroscopic or microscopic defects, validating the design method of ring preform based on IFM is reasonable. The research contributes to advancing near-net shape forming technologies, offering a novel approach to preform design that marries theoretical analysis with practical manufacturability considerations.

Digital Transformation, Absorptive Capacity and Enterprise ESG Performance: A Case Study of Strategic Emerging Industries

Digital transformation provides new drivers for economic performance growth in enterprises, but can it further improve ESG performance and support sustainable development? Based on the perspective of resources and capabilities, this article uses the relevant data of 1588 listed companies in strategic emerging industries from 2011 to 2021 to study the impact of digital transformation on enterprise ESG performance examines the intermediary role of absorptive capacity and the moderating role of regional digitalization level, and further analyzes the heterogeneity of property rights and industrial nature. The research results indicate that: firstly, digital transformation of enterprises can positively promote the improvement of ESG performance; secondly, absorptive capacity serves as a conduit through which digital transformation influences a company’s ESG performance; thirdly, the regional digitalization level positively moderates the promotion effect of digital transformation on enterprise ESG performance; fourthly, the impact of digital transformation on ESG performance of enterprises is significantly differentiated in the nature of enterprise property rights and industrial nature, and the ESG performance of state-owned enterprises and high-end equipment manufacturing enterprises is more sensitive to digital transformation. The research conclusion is based on a digital perspective, providing relevant insights for improving the ESG performance of strategic emerging industry enterprises and expanding their ESG development paths.

Method of inclined hole axis adjustment based on geometric optimization and minimum zone theory

The machining accuracy of the axis position and spatial angle of the array microholes with high aspect ratio on the components of the high-end equipment manufacturing industry will directly affect the performance of the whole machine. A method of tilt hole axis adjustment is proposed in this paper. It utilizes geometric optimization and minimum zone theory to measure the position and spatial angle of the hole axis. This addresses the slight angle error that occurs after correcting the inclined hole axis using visual techniques. Based on the minimum zone criterion for the cylindricity error, the evaluation model is re-established through the inner and the outer loop and spatial projection, which includes roundness evaluation of the projection curve by an improved particle swarm optimization (PSO) algorithm and elliptic fitting by a geometric parameter method, so as to obtain the optimal solution of the axis vector and spatial angle. Experimental comparison with the theory above shows that the precision of the method proposed is better than that of the least squares method and the traditional PSO algorithm based on the minimum zone criterion. The research results indicate that the precision of the tilt angle is better than 0.03°, and simultaneously the rotation angle is better than 0.1° in the range of 1°. This experiment verifies the effectiveness and accuracy of the proposed method.

Investigating the Dynamic Mechanical Properties and Strengthening Mechanisms of Ti-6Al-4V Alloy by Using the Ultrasonic Surface Rolling Process.

The demand for titanium alloy has been increasing in various industries, including aerospace, marine, and biomedical fields, as they fulfilled the need for lightweight, high-strength, and corrosion-resistant material for modern manufacturing. However, titanium alloy has relatively low hardness, poor wear performance, and fatigue properties, which limits its popularization and application. These disadvantages could be efficiently overcome by surface strengthening technology, such as the ultrasonic surface rolling process (USRP). In this study, the true thermo-mechanical deformation behavior of Ti-6Al-4V was obtained by dynamic mechanical experiment using a Hopkinson pressure bar. Moreover, USRP was applied on the Ti-6Al-4V workpiece with different parameters of static forces to investigate the evolution in surface morphology, surface roughness, microstructure, hardness, residual stress, and fatigue performance. The strain rate and temperature during the USRP of Ti-6Al-4V under the corresponding conditions were about 3000 s-1 and 200 °C, respectively, which were derived from the numerical simulation. The correlation between the true thermo-mechanical behavior of Ti-6Al-4V alloy and the USRP parameters of the Ti-6Al-4V workpiece was established, which could provide a theoretical contribution to the optimization of the USRP parameters. After USRP, the cross-sectional hardness distribution of the workpiece was shown to initially rise, followed by a subsequent decrease, ultimately to matrix hardness. The cross-sectional residual compressive stress distribution of the workpiece showed a tendency to initially reduce, then increase, and finally decrease to zero. The fatigue performance of the workpiece was greatly enhanced after USRP due to the effect of grain refinement, work hardening, and beneficial residual compressive stress, thereby inhibiting the propagation of the fatigue crack. However, it could be noted that the excessive static force parameter of USRP could induce the decline in surface finish and compressive residual stress of the workpiece, which eliminated the beneficial effect of the USRP treatment. This indicated that the choice of the optimal USRP parameters was highly crucial. This work would be conducive to achieving high-efficiency and low-damage USRP machining, which could be used to effectively guide the development of high-end equipment manufacturing.

Dynamic scheduling of decentralized high-end equipment R&D projects via deep reinforcement learning

Distributed research and development (R&D) plays a pivotal role in high-end equipment manufacturing enterprises, significantly impacting interdisciplinary innovation and expediting development cycles. In the uncertain environment characterized by random arrivals of new projects, achieving dynamic scheduling and real-time coordination for decentralized R&D projects is a challenging problem. This paper studies a dynamic decentralized resource-constrained multi-project scheduling problem with product transfers (DDRCMPSP-PT). The objective is to minimize the development cycle after initial local schedules are generated to minimize the individual project makespan. To tackle the problem, we develop a decentralized multi-agent system using the dynamic coordination mechanism for resource assignment (DMAS/DCMRA). An up-to-date deep reinforcement learning (DRL) algorithm, dueling double deep Q-learning (D3QN) with prioritized replay, is employed to select the optimal strategy to resolve the global resource conflicts adaptively. Two priority rules are presented based on the structural properties and make up the action space of the DRL agent together with ten high-quality priority rules. Extensive simulation experiment results based on real enterprise cases reveal that the DMAS/DCMRA outperforms all traditional priority rule-based methods. The proposed priority rules also show competitive performance compared to other priority rules, which validates the applicability and superiority of the proposed method. This research offers a new agile project management technique for organizations running multiple R&D projects and managing crucial bottleneck resources.

Scheduling identical serial-batching machines in the engine manufacturing supply chain by an integrated variable neighborhood search algorithm

The optimization of the high-end equipment manufacturing process is becoming progressively more difficult, which brings a big challenge for high-end equipment manufacturers. Numerous variables, such as equipment wear and tear, the service level of the supply chain, etc., may have a significant impact on the efficiency of high-end equipment manufacturing. In this paper, motivated by the need to improve the productivity of engines, we study a scheduling problem considering the deterioration effect, component parts supply, and parallel serial-batch processing machines. The job processing time is sum-of-actual-processing-time-dependent and the set-up time of a batch is time-dependent. The objective is to minimize the makespan. Firstly, we analyze the structural properties of the problem under two special cases. Then, a dynamic programming (DP) algorithm is proposed to group the jobs into batches, and two heuristic algorithms are designed to improve the job sequence based on the structural properties. Algorithm 3 is proposed to provide a lower bound for the investigated problem. Next, we develop a variable neighborhood search algorithm (VNS-H) which integrates the DP algorithm, heuristic algorithms, and four local search strategies. Extensive computational experiments are conducted to validate the performance of the proposed algorithm. The results show that the VNS-H algorithm solves the proposed problem effectively and outperforms other involved metaheuristics. The proposed methods in this work can provide manufacturers with useful decision-making support that improves production efficiency and reduces operational costs.

Analysis on the effects of global supply chain reconfiguration on China's high-end equipment manufacturing industry

PurposeThe challenges faced by China's high-end equipment manufacturing (HEEM) industry are becoming clearer in the process of global supply chain (GSC) reconfiguration. The purpose of this study is to investigate how China's HEEM industry has been affected by the GSC reconfiguration, as well as its short- and long-term strategies.Design/methodology/approachThe authors adopted a multi-method approach. Interviews were conducted in Phase 1, while a three-round Delphi survey was conducted in Phase 2 to reach consensus at the industry level.FindingsThe GSC reconfiguration affected China's HEEM supply chain (SC). Its direct effects include longer lead times, higher purchasing prices and inconsistent supply and inventory levels of key imported components and materials. Its indirect effects include inconsistent product quality and cash flows. In the short term, China's HEEM enterprises have sought to employ localized substitutes, while long-term strategies include continuous technological innovation, industry upgrades and developing SC resilience.Originality/valueThis study not only encourages Chinese HEEM enterprises to undertake a comprehensive examination of their respective industries but also provides practical insights for SC scholars, policymakers and international stakeholders interested in how China's HEEM industry adapts to the GSC reconfiguration and gains global market share.

Empirical insights into industrial policy's influence on phytoprotection innovation.

Intelligent Phytoprotection is an important direction for the modern development of plant protection related disciplines, and its essence is the innovative application of new generation information technology industry, high-end equipment manufacturing industry, and digital industry related technologies in the traditional plant protection field. This article first identifies 224 International Patent Classification (IPC) Main groups in the field of intelligent phytoprotection technology based on the International Patent Classification System. And then combines with China's industrial policy practice, we explore the impact of industrial policy on the application number of invention patents in the field of intelligent phytoprotection technology using the Difference-in-difference (DID) method and the Synthetic DID method. The study results showed that the implementation of industrial policy can significantly promote the patent application activities in the intelligent phytoprotection treatment group, with an average increase of 517 invention patent applications compared to the control group that is not affected by the policy. The research conclusion of this article suggests that for countries and regions, industrial policies are an important tool for promoting the innovation and development of intelligent phytoprotection related technologies.

The influence of digital innovation ecosystem of high-end equipment manufacturing on the intelligent maturity of enterprise – an empirical study on the configuration of the “three-layer core-periphery” structure

PurposeWith the deep development of the new technological revolution and industrial transformation, the development, application, expansion and integration of digital technology provide opportunities for transforming the manufacturing industry from traditional manufacturing to intelligent manufacturing. However, little research currently focuses on analyzing the influencing factors of intelligent development in this field. There is a lack of research from the perspective of the digital innovation ecosystem to explore the intrinsic mechanism that drives intelligent development. Therefore, this article starts with high-end equipment manufacturing enterprises as the research subject to explore how their digital innovation ecosystem promotes the effectiveness of enterprise intelligent development, providing theoretical support and policy guidance for enterprises to achieve intelligent development at the current stage.Design/methodology/approachThis article constructs a logical framework for the digital innovation ecosystem using a “three-layer core-periphery” structure, collects data using crawling for subsequent indicator measurement and assessment and uses the fuzzy set Qualitative Comparative Analysis method (fsQCA) to explore how the various components of the digital innovation ecosystem in high-end equipment manufacturing enterprises work together to promote the development of enterprise intelligently.FindingsThis article finds that the various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises, through mutual coordination, can help improve the level of enterprise intelligence. Empirical analysis shows four specific configuration implementation paths for the digital innovation ecosystem of high-end equipment manufacturing enterprises to promote intelligent development. The core conditions and their combinations that affect the intelligent development of enterprises differ in each configuration path.Originality/valueFirstly, this article discusses the practical problems of intelligent transformation and development in the manufacturing industry and focuses on the intelligent development effectiveness of various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises in the context of digitalization. Secondly, this article uses crawling, text sentiment analysis and other methods to creatively collect relevant data to overcome the research dilemma of being limited to theoretical analysis due to the difficulty in obtaining data in this field. At the same time, based on the characteristics of high-end equipment manufacturing enterprises, the “three-layer core-periphery” digital innovation ecosystem framework constructed in this article helps to gain a deep understanding of the development characteristics of the industry's enterprises, provides specific indicator analysis for their intelligent development, opening the “black box” of intelligent development in the industry's enterprises and bridging the gap between theory and practice. Finally, this study uses the fsQCA research method of configuration analysis to explore the complexity of the antecedents and investigate the combined effects of multiple factors on intelligent development, providing new perspectives and rich research results for relevant literature on the intelligent development of high-end equipment manufacturing enterprises.

Dynamic evolution and impact mechanism of human capital mismatch in strategic emerging industries: Evidence from the Yangtze River Delta region of China

China's “demographic dividend” has gradually disappeared since 2013, but the problem of human capital mismatch prevails in various industries and sectors, and improving human capital mismatch and optimizing the allocation of human capital has become an important measure for high-quality economic development. This study uses panel data at the provincial level in the Yangtze River Delta region from 2012 to 2021 and employs spatial econometric models to quantitatively analyze the index of human capital mismatch, the impact mechanism, and spatial interaction effects in strategic emerging industries. The results show that the spatial agglomeration and dependence of human capital mismatch decreased in the Yangtze River Delta region, and the index of human capital mismatch in strategic emerging industries is generally insufficient. The shortage of talent supply within industries is particularly severe in high-end equipment manufacturing and biotechnology industries. Second, fiscal subsidies have exacerbated human capital mismatches in both local and neighboring areas, with significant spatial spillover effects. Urbanization has exacerbated local human capital mismatches but has improved the degree of human capital mismatch in neighboring areas. The index of industrial structural adjustment is generally conducive to alleviating human capital mismatches. Distorted markets and financial levels have accelerated human capital mismatches by distorting the efficiency of human capital allocation. Third, regional heterogeneity significantly influences the spatial interaction effects of human capital mismatches. This study contributes to improving the situation of human capital mismatch in strategic emerging industries in the Yangtze River Delta and provides theoretical and policy recommendations for local governments.

Enhancing Operating Efficiency in China’s High-End Equipment Manufacturing Industry: Insights from Listed Enterprises

The high-end equipment manufacturing industry is a strategic sector for China’s manufacturing transformation and upgrading. However, this industry is facing a series of challenges, such as insufficient innovation capabilities and poor business operations. This paper uses the super-efficiency SBM model to calculate the operating efficiency of listed companies in this industry from a micro perspective and conducts in-depth multi-angle analysis of their operating efficiency. Furthermore, Tobit regression is utilized to identify the factors that affect operating efficiency. The aim is to provide a pathway for companies in this industry to achieve efficiency maximization and sustainable development. The research shows that the average operating efficiency of high-end equipment manufacturing companies was around 0.7 from 2016 to 2021, and nearly 70% of companies were in a non-DEA efficient state. The operating efficiency of the intelligent manufacturing equipment industry is far higher than other industries, and the western region has great development potential. In addition to government subsidies, factors such as company age, equity concentration, regional GDP, and regional openness all have a positive impact on the operational efficiency of high-end equipment manufacturing companies. This paper combines the characteristics of the equipment manufacturing industry and analyzes their operating efficiency from multiple dimensions, providing decision support and pathways for the high-quality and efficient development of this industry.

Evaluation research on the independent controllability of high-end equipment manufacturing industry based on dual incentive control line: an empirical analysis from Jiangsu Province

PurposeIn order to analyze the level of independent controllability and its evolution of high-end equipment manufacturing industry from Jiangsu Province, this article introduces the dual-excitation control line method to construct a comprehensive evaluation model for independent controllability.Design/methodology/approachThrough the collection of information of high-end equipment manufacturing industry's independent and controllable capabilities on different indicators, the three aspects of advancement, autonomy and controllability, an empirical evaluation of 10 enterprises in the high-end equipment cluster in Jiangsu Province was conducted in terms of advancement, autonomy and controllability.FindingsIt effectively reveals the area and evolution characteristics of the “reward” and “punishment” of different indicators of each representative enterprise and reflects the development status and different characteristics of each representative enterprise on the three indicators. The research results provide decision-making guidance for enterprises in the management and control of advanced manufacturing systems with independent and controllable capabilities.Originality/valueExisting research focuses on the evaluation of enterprises' independent controllability only on a single angle or index. This paper maps the dynamic evaluation problem of multiple time-point data to the evaluation problem of single time-point multi-index data and investigates the fluctuation of the performance of the same enterprise under different indexes, so as to comprehensively evaluate the independent controllable level of high-end equipment manufacturing industry and analyze the reasons. Further, this paper first establishes an evaluation index system of independent controllable level of high-end equipment manufacturing industry and quantitatively measures the advanced, independent, controllable and other aspects of typical enterprises in this industry by constructing a double incentive control line evaluation model.

Effect of double laser beam surface treatment on the surface morphological characteristics of Ti6Al4V at different heat source incidence angles

With the development of the high-end equipment manufacturing industry, the requirements for the surface performance of critical components are likewise increasing. To control the heat and mass transfer in the laser surface treatment process more efficiently and flexibly, the multiangle double laser surface treatment technology shows unique advantages. This study is focused on the experimental study of the laser surface treatment process for Ti6Al4V material. By changing the laser power and the incidence angle of the laser beam, the effects of single-laser beam surface treatment (SLST) and dual-laser beam surface treatment (DLST) on the surface morphology of the alloy were studied. The effects of laser treatment on the surface morphology, element distribution, roughness, surface chemical state, and surface structure of the alloy were emphatically analyzed. The mechanism of SLST and DLST at different incident angles of heat source and the effect of DLST on the formation of an oxide layer on the surface of the alloy were compared and analyzed. The results show that when the average power of the high-power nanosecond pulse width laser in DLST is 100 W and the incident angle is 135°, the surface oxygen content reaches the lowest, as low as 8.31%. The secondary effect of the laser makes the alloy surface remelt again, and then the best roughness after laser processing is obtained, with a roughness value of 2.801 μm. Finally, we obtain the optimal process for laser surface treatment and conclude that DLST can significantly optimize the surface state after a high-energy laser beam action.

Assessing the competitiveness of listed Chinese high-growth companies in the STAR market

This paper aims to assess the competitiveness of 68 high-growth enterprises listed on China's STAR market and explore the combination of antecedent conditions that led to their listing using fuzzy set qualitative comparative analysis. The interpretive structure model was utilized to identify the factors influencing their competitiveness, and the listing index weight for the STAR market was determined using the analytic hierarchy process. The competitiveness of the listed enterprises was found to be promising, with new energy, next-generation IT, and high-end equipment manufacturing being the most prominent fields. However, energy conservation and environmental protection exhibited relatively weak listed competitiveness. The listing of these enterprises was the result of multiple factors rather than a single factor. The listing paths of high-growth enterprises in China were classified into three types: well-operated with high technical competence and innovation-led, high-profit with low growth and innovation, and large-scale, high-profit, and innovation-led.

Real-Time Quality Monitoring of Ultrathin Sheets Edge Welding Based on Microvision Sensing and SOCIFS-SVM

Ultrathin sheets edge welding is a critical technology for aircraft, aerospace, or much high-end equipment manufacturing industrial applications. However, its online quality monitoring is still challenging due to the complicated and multicoupled transport phenomena in the mesoscale molten pool. This article presents an intelligent methodology for real-time monitoring of edge welds forming quality using microvision sensing and support vector machine based on swarm optimized and computationally inexpensive floating selection (SOCIFS-SVM). The clear images of the mesoscale molten pool are continuously acquired by using the passive microvision sensor. Based on the analysis for the dynamic behavior of the mesoscale molten pool and the morphology of the formed edge welds, a robust image processing procedure is developed to extract the single-frame spatial features and interframe correlation features from the microvision image sequence of the molten pool. A Kalman filter is applied to suppress the noise caused by the pulse current, etc. Then, the SOCIFS-SVM-based approach is established to predict three typical types of welding states. Experimental results with multiple groups of different welding parameters show that the proposed method can effectively and robustly identify the lack of fusion, humping, and sound weld with the highest accuracy of 96.09%. In addition, the established model can still achieve mean accuracy of 94.98% for additional untrained data, indicating that the model has strong generalization ability. The proposed real-time quality monitoring method makes online defect diagnosis of ultrathin sheets edge welding possible and provides a basis for online quality closed-loop control.

Research on Innovative Training Mode of High-Skilled Talents in Equipment Manufacturing Industry Based on School-Enterprise Cooperation

School-enterprise cooperation is not only one of the prominent features of modern vocational education, but also helps to strengthen the cooperation between vocational colleges and enterprises, and the combination of teaching and production. It is an important way to realize the development of modern vocational education and build a high-skilled personnel training system. From the perspective of the significance of school-enterprise cooperation in the process of cultivating high-skilled talents, the key factors of the cultivation system of high-skilled talents based on school-enterprise cooperation, the mechanism of "government leadership, industry guidance, and enterprise participation" should be played to promote the deep integration of vocational education with industrial and regional economic development, improve the quality of the cultivation of skilled talents in high-end equipment manufacturing industry, and improve the innovation of the cultivation mode of high-end equipment talents.

Impact of government subsidy on diagnostic tests decisions of core products in high-end equipment manufacturing

In the high-end equipment manufacturing industry, diagnostic test is a common procedure for core products. The core component supplier could obtain the true quality of its products by implementing diagnostic test during the R&D (research and development) process and adjusting the efforts to achieve better quality. However, diagnostic test is not well implemented due to its high cost. As China’s favorable policies increase in relevant regard, government subsidy becomes an emerging incentive for core product manufacturing. The core product supplier who receive financial incentives usually contributes extra effort to achieve better quality. However, the extra effort comes with a corresponding cost. In addition to this, the supplier has received government subsidy to fund the implementation of diagnostic test. In turn, conducting diagnostic test can provide a safety plug as to whether the supplier should receive government subsidy. To investigate this interaction between the government subsidy adoption decision and the diagnostic test decision, we use game theory to model a supply chain composed of an upstream supplier and a downstream manufacturer, where the supplier is delegated by the manufacturer to research and development a batch of core products and to maintain them during the warranty period. Our research shows that even though government subsidies can provide the supplier with direct financial support, it may still not be adopted. In essence, government subsidy has a bright side and a downside effect on supplier, wherein the former side is the direct financial support and the downside is the extra effort cost. The supplier does not always accept government subsidy because the bright side not always sufficient to cover the downside. Specifically, when diagnostic test is costly or with high-quality products, the supplier will accept subsidy but not perform diagnostic tests regardless of government subsidy. Counterintuitively, if consumer preferences for the product are high, the supplier will choose not to accept government subsidy but to perform diagnostic test. This research contributes to providing some theoretical insights for managers in the high-end equipment manufacturing industry.

Robust Optimization on Unrelated Parallel Machine Scheduling With Setup Times

The parallel machine scheduling problem has been a popular topic for many years due to its theoretical and practical importance. This paper addresses the robust makespan optimization problem on unrelated parallel machine scheduling with sequence-dependent setup times, where the processing times are uncertain, and the only knowledge is the time intervals they take values from. We propose a robust optimization model with the min-max regret criterion to formulate this problem. To solve this problem, we prove that the worst-case scenario with the maximum regret for a given solution belongs to a finite set of extreme scenarios. Based on this theoretical analysis, a procedure to obtain the maximum regret is proposed and an enhanced regret evaluation method (ERE) is designed to accelerate this process, which is of great significance to improve the efficiency of the algorithm. A multi-start decomposition-based heuristic algorithm (MDH) based on the analysis of properties is proposed to solve this problem. Computational experiments are conducted to justify the performance and robustness of these methods. Note to Practitioners—Various uncertainties may occur in the production process, which brings great challenges to production and operations management. A robust production schedule is of great significance for factories to make full use of production capacity and deal with production abnormalities. This study is motivated by an R&D and assembly task scheduling problem encountered in a high-end equipment manufacturing factory in which the processing time of each job is uncertain, and its distribution is also unknown due to limited information. In this study, with the consideration of sequence-dependent setup time and uncertain job-processing time, we view the labor groups with different skill levels as unrelated parallel machines and build a robust (min-max regret) scheduling model to formulate this problem so as to reduce the production makespan. An enhanced regret evaluation method is developed to improve the evaluation efficiency for a given solution, and a multi-start decomposition-based heuristic algorithm is proposed to solve this problem. This study can be applied in practice to release schedulers from burdensome work and provide high-quality robust schedules for this complicated production environment.

How Multidimensional Digital Empowerment Affects Technology Innovation Performance: The Moderating Effect of Adaptability to Technology Embedding

Technology innovation in high-end equipment manufacturing (HEM) enterprises technology innovation plays an important role in supporting national economies and social development, accelerating the speed of digital transformation. Digital empowerment aggravates the dynamics, complexity, and uncertainty of technology innovation in HEM enterprises. In order to improve the innovation performance mechanism of China’s HEM enterprises, the adaptability of technology embedding and digital empowerment are introduced to further explain the direct and the moderating effect. Specifically, through a literature review and practical research, the connotation and dimensional division of digital empowerment are defined. According to empowerment tools, digital empowerment is divided into resource empowerment and structural empowerment, and according to the empowerment actor relationships, digital empowerment is divided into platform empowerment and ecological empowerment. Additionally, 436 HEM enterprises are used as research objects to build a conceptual model of the different digital empowerment dimensions, adaptability to technology embedding and technology innovation performance. The research results show that resource empowerment and structural empowerment have U–shaped effects on technology innovation performance, and platform empowerment and ecological empowerment positively affect technology innovation performance. Then, adaptability to technology embedding positively moderates the U-shaped relationship between structural empowerment and technology innovation performance. Adaptability to technology embedding positively moderates the relationship between resource empowerment, platform empowerment, ecological empowerment and technology innovation performance. The research findings deepen the connotation and dimension of digital empowerment, demonstrating the nonlinear relationship between digital empowerment and technology innovation performance of HEM enterprises. Additionally, the research expands on the new applications of adaptability to technology embedding in the digital transformation of manufacturing.