Equipment Manufacturing Industry Research Articles

Influence Factors of Digital Economy on the Willingness of Equipment Manufacturing Industry to Green Production in the Era of Artificial Intelligence

In the age of artificial intelligence, the digital economy significantly promotes green production in manufacturing. It optimizes processes, recycles resources, drives green tech innovation, and boosts product sustainability. While there's ampleresearch on green manufacturing, studies linking it to the digital economy are limited. Given the machinery manufacturing sector's strategic importance and China's focus on green, sustainable manufacturing, this study bridges that gap. Using the Theory of Planned Behavior (TPB) and Normative Activation Model (NAM), it examines how the digital economy influences green production intentions in equipment manufacturing. The study surveys Middle Eastern equipment manufacturers, analyzes the data with SmartPLS, and finds that the digital economy positively impacts green production via personal norms, responsibility, consequence awareness, attitudes, and cost savings. These factors mediate the relationship between the digital economy and green production intentions. The paper concludes with practical recommendations based on these findings. In the era of artificial intelligence, how the digital economy provides a new path to the green production of the manufacturing industry, and provides a theoretical reference for the sustainable development of the equipment manufacturing industry.

Remanufacturing Operations in Different Financial Ownership Structures with Consideration of the Upwards Supplier

Under the increasing environmental pressure, remanufacturing has increasingly become a new mode of recycling economy and upgrading and transforming the equipment manufacturing industry. Some enterprises include remanufacturing businesses in the original production system by holding or controlling shares in other remanufacturing enterprises. This paper builds a two-echelon supply chain model composed of a supplier, a manufacturer, and a remanufacturer, considering the different ownership structures (i.e., shareholding and share-controlling) between them, in which the supplier sells non-remanufacturable parts to the manufacturer and the remanufacturer. At the same time, the optimal decisions of each firm are considered. The results show that for the manufacturer, a higher shareholding ratio means that it can obtain more profits. For the supplier, the impact of the shareholding ratio depends on the manufacturing cost. When the manufacturing cost is relatively low, the stock sharing relationship between the manufacturer and the remanufacturer will decrease the supplier’s profit. When the manufacturing cost is relatively high, it will depend on the shareholding ratio. In the case of shareholding between the manufacturer and the remanufacturer, a higher shareholding ratio will decrease the supplier’s profit. From the perspective of the supply chain, when the production cost is high enough, the supply chain’s profit decreases first and then increases with the shareholding ratio. Furthermore, the increase in the manufacturer’s shareholding in remanufacturing does not always improve the remanufacturing proportion of products.

Intelligence level evaluation and influencing factors analysis of equipment manufacturing industry in the Yangtze River Delta.

The Yangtze River Delta (YRD) bears the vital task of driving the growth of China's equipment manufacturing industry (EMI) intelligence as an advanced region. Fostering the transformation and upgrading of the EMI in the YRD and constructing a modern production mode is vital to developing and reforming China's manufacturing industry. This paper uses industrial robot data to assess the level of intelligence (LoI) in the EMI from 2016 to 2019. The OLS (ordinary least squares) model is used for the measurements, and the MQ (the modified contribution index) is used to estimate the degree of contribution from a host of variables. It is identified that the LoI is on the rise. However, excluding railways, aerospace, shipbuilding, and other transportation equipment manufacturing, the LoI is significantly higher than in other subsectors. It is also identified that technological innovation ability, human capital density, and enterprise cost pressure govern the industry's LoI. Moreover, while there is a difference in the main influencing factors in LoI within different industries, R&D investment, technological innovation ability, and enterprise cost pressure have the most significant impact across most equipment manufacturing sub-industries.

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.

Optimization of benzene exposure risk assessment: An integrated approach utilizing internal and external concentrations with a focus on biomarkers S-PMA & t, t-MA

In the majority of occupational settings within China, the concentrations of benzene are observed to fall markedly below the demarcated detection thresholds. Employing traditional risk assessment models, the presence of exceptionally low airborne benzene exposure concentrations may infuse heightened degrees of uncertainty. Consequently, the necessity arises to investigate risk assessment methodologies more apt for the prevalent exposure environment among employees. In the present study, a pharmacokinetic model premised on urinary benzene metabolites (S-PMA and t, t-MA) was employed to ascertain a more precise daily airborne benzene exposure concentration per individual. This value was integrated into the linear multistage model as the ‘internal exposure concentration’. In conjunction with the U.S National Environmental Protection Agency's (EPA) inhalation risk assessment model predicated on the external exposure concentration, the Singapore Ministry of Manpower's (MOM) model, and the linear multistage (LMS) model, the carcinogenic and non-carcinogenic effects of benzene were evaluated for 1781 benzene-exposed employees across 76 enterprises in Jiangsu Province. Findings suggest that in the linear multilevel model assessment, the cancer risk levels based on t, t-MA and S-PMA were higher in the printing and recording media reproduction industry, automobile manufacturing industry, general equipment manufacturing industry and the furniture manufacturing industry (median 2.842 × 10−4, 2.819 × 10−4, 2.809 × 10−4, and 2.678 × 10−4), which align more consistently with the actual benzene exposure circumstances of each industry's study participants, with overall risk levels calculated by the linear multistage model exceeding those of the EPA inhalation risk assessment model and the MOM model. This implies that the linear multistage model of internal exposure, based on the reciprocal of benzene biomarkers S-PMA and t, t-MA for airborne benzene exposure, presents enhanced sensitivity and suitability for the current occupational health risk assessment of workers. Without doubt, biomarker-based benzene exposure risk assessment emerges as the optimal choice.

Surveillance of noise exposure level in industrial enterprises-Jiangsu Province, China, 2022.

Occupational noise exposure is the most prominent problem in industrial enterprises in Jiangsu Province. Since 2019, China has established the National Surveillance System for Occupational Hazards in the workplace to grasp the current occupational hazards in critical industries, including occupational noise. According to the Work Plan for Surveillance of Occupational Hazards in the Workplace (2022) issued by the National Health Commission of the People's Republic of China, the noise exposure level of 3,142 enterprises in our province was monitored, the median and interquartile range (IQR) were calculated, and the distribution of noise exposure level was described by industry classification, enterprise-scale and ownership type of the enterprise. The railway, shipping, aerospace, and other transportation equipment manufacturing industries exhibited the highest proportion (42.9%) of individual noise exposure levels exceeding 85 dB(A), followed by the motor vehicles manufacturing industry (36.4%). The proportion of individual noise exposure levels exceeding 85 dB(A) was higher in medium and small enterprises, with rates of 28.1 and 28.6%, respectively. The highest proportion of personal noise exposure levels exceeding 85 dB(A) was observed in Hong Kong, Macao and Taiwan investment enterprises (37.5%), followed by incorporated companies (34.6%) and limited liability companies (28.1%), the lowest was state-owned enterprises(15.5%). The areas with excessive noise are primarily concentrated in grinding, welding, machining, cutting, and other related operations, accounting for 61.2% of the total. Among these operations, grinding accounts for 29.8%. The highest environmental noise and individual noise intensity were found in sandblasting and grinding positions, with individual noise intensities of 115.5 dB(A) and 108.4 dB(A), respectively. The noise exposure risk is so high that cannot be ignored in the manufacturing industry, especially in Hong Kong, Macao, and Taiwan investment enterprises, incorporated companies and medium and small enterprises.

A cascaded flowshop joint scheduling problem with makespan minimization: A mathematical model and shifting iterated greedy algorithm

This paper studies a cascaded flowshop joint scheduling problem that has critical applications in the electronic information equipment manufacturing industry but has received limited attention in the scheduling field. The cascaded flowshop joint scheduling problem encompasses both a distributed permutation flowshop scheduling problem and a hybrid flowshop scheduling problem. This paper investigates the efficient scheduling of a set of jobs in two heterogeneous flowshops to minimize the makespan. We present a mixed integer linear programming mathematical model and a shifting iterated greedy algorithm, which constantly changes its search space to explore different solution spaces. Based on the specific characteristics of the problem, a hybrid scheduling approach that combines forward and backward scheduling, a step-by-step destruction and reconstruction operator, and three adaptive reconstructive methods that combine coarse-tuning and fine-tuning are proposed to explore the near-optimal solution. Through comprehensive computational comparison and statistical analysis, the results demonstrate that the proposed shifting iterated greedy algorithm performs significantly better in relative deviation index values at the same CPU running time.

Influence of martensitic transformation on the surface integrity aspects during sustainable turning of NiTi-SMA

With the development of the equipment manufacturing industry towards the direction of intelligence, the demand for the use of intelligent materials is increasing, and high-performance shape memory alloy (SMA) products are getting more and more attention. However, the current machining quality of shape memory alloy products can not meet the existing quality requirements. The mechanism of surface integrity of NiTi SMA needs to be revealed. The influence of martensitic transformation on microstructure, roughness, hardness, and superelasticity was explored. The occurrence of martensitic transformation is determined by controlling the cutting speed (lower and higher cutting speeds) and machining conditions (dry and liquid nitrogen conditions). The superelasticity was expressed quantitatively by the remnant depth ratio. The occurrence of martensitic transformation promoted the formation of the affected layer and work hardening. Meanwhile, it is an important reason for the increase in roughness and the decrease in superelasticity.

Does the agglomeration of high-tech industries improve the efficiency of China’s green economy? Analysis based on the marketization

High-tech industries represent a country’s advanced productivity, technological level and innovation ability, they carry the characteristics of green industries in terms of resource-saving and being environmentally friendly, and their agglomeration has a significant impact on the development of green economy. The marketization process, as an important macroenvironment for the development of high-tech industries, not only relates to the motivation of high-tech industry agglomeration but also affects the effectiveness of the agglomeration effect. It is unclear whether marketization plays a role in the relationship between high-tech industry agglomeration and green economy efficiency and whether this role has heterogeneity. Taking China’s provincial panel data from 2001 to 2020, this paper constructs a panel threshold econometric model, and examines the threshold effect of the high-tech industry agglomeration impact on green economic efficiency under different marketization levels. 1) The high-tech industry agglomeration impact on green economic efficiency has a double-threshold effect due to the marketization level, with threshold values of 5.862 and 8.938, and the promotion effect is the highest in the second threshold range. 2) Under the influence of the government-market relationship, non-state economic development, market intermediary organization development and legal environment, the impact of high-tech industry agglomeration on green economic efficiency has a single-threshold effect, with threshold values of 7.4950, 12.1960 and 11.6100. After crossing the threshold values, the impact changes from negative to positive. Factor market development has a double-threshold effect, with threshold values 10.3100 and 14.9210, and the impact changes from negative to positive and gradually increases. 3) Among the major industries, the pharmaceutical manufacturing industry (3.978 and 9.639) and the computer and office equipment manufacturing industry (3.359 and 9.639) have a double-threshold effect due to the marketization level, and the medical equipment and instrumentation manufacturing industry (9.494) has a single-threshold effect. This study is of great significance for China as it would help optimize the market environment and stimulate the green economy effect of high-tech industry agglomeration; moreover, it would provide a reference for other developing countries and transition economies.

Multivariate statistical analysis applied to physical properties of soybean seeds cultivars on the post-harvest

To consider the different characteristics of soybean seeds for designing and regulating the post-harvest equipment, we evaluated the similarities in the physical properties of soybean cultivars in this study. Two-hundred soybean seeds from 40 genetically modified cultivars were collected in packages to measure the physical properties of the seeds. First, principal component analysis was performed to verify the interrelationships between the variables and soybean cultivars. Next, a boxplot was constructed for each variable, considering the groups obtained after analyzing the main components. Finally, a scatterplot containing the Pearson's correlations between the variables was constructed. We identified two clusters of cultivars: C1 and C2. The unit-specific mass was the physical property that contributed the most to the formation of C1, whereas the other physical properties contributed to the formation of C2. Soybean cultivars comprising C1 were similar to each other only in unit specific mass, and the cultivars allocated to group C2 were similar according to all the other properties evaluated. These results can serve as a guideline for genotype selection for soybean genetic improvement to minimize variations in the physical characteristics of the seeds and obtain greater efficiency in the processing stages. Thus, the equipment manufacturing industry and seed processing units can implement projects and equipment adjustments to manage the post-harvest and seeding processes of soybean seeds efficiently.

Digital technology and manufacturing industrial change: Evidence from the Chinese manufacturing industry

This study utilized data from 2011 to 2020 across 29 manufacturing industries in China to comprehensively analyze the macro-level impact of digital technology applications on industrial transformation. The main findings are as follows: Firstly, a significant positive correlation between digital technology application and industrial change was observed, with an average effect of 1.2128 and statistical significance (α = 1 %). In specific sectors such as textile manufacturing, resource processing, and machinery equipment manufacturing, the positive influence of digital technology on industrial transformation is evident, with respective effects of 1.2088, 1.0934, and 1.1737-all statistically significant (α = 1 %). Secondly, examining heterogeneity among different manufacturing industries revealed that process manufacturing and labor-intensive sectors exhibited more pronounced responses to digital technology, with impact coefficients of 1.2189 and 1.2784, both significant (α = 1 %). Robustness tests confirmed the reliability of these results, demonstrating that the positive relationship between digital technology application and industrial change remained robust even after addressing endogeneity concerns. Lastly, a mechanism analysis uncovered the pivotal roles of innovation mechanisms, talent effects, and government institutional arrangements in the digital technology-driven industrial transformation. Notably, the machinery and equipment manufacturing industry displayed a significant positive correlation with the innovation mechanism, with a coefficient of 3.8344. The resource processing industry showed a positive impact associated with the talent effect, with a coefficient of 0.0002. However, in the textile manufacturing and machinery and equipment manufacturing sectors, government institutional arrangements were negatively correlated with industry performance, with coefficients of −0.0056 and −0.0096, respectively. Overall, these findings offer valuable insights into how digital technology shapes the transformation of the manufacturing industry, holding great theoretical and practical significance for guiding the industry's digital transformation efforts.

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.

Heavy industrial aggregation area's green transformation optimisation pathways exploration with synergistic reduction pursue of CO2 and gaseous pollutants

A profound green transformation of China's heavy industrial aggregation regions is required to conquer severe air pollution, and fulfill carbon peak pledge. Here, to clarify the coordinated mechanism and an optimal roadmap for the reduction of CO2 and gaseous pollutants, we integrated input-output modelling, system dynamics, and multi-objective programming to construct a CO2 and gaseous pollutants synergistic reduction model initially; investigated incentive approaches from 2020 to 2035; Hebei, with the largest steel production in China, was adopted as a demonstrative region. Results revealed that intensive dual control of efficiency and structure in energy and industry can accelerate achieving carbon peak in 2029. In optimal case, CO2 emission intensity can reach a 75.2% reduction compared to 2020, and gaseous pollutants continue decline simultaneously (SO2 and NOx can drop by 63% and 48%); and the synergistic reduction level is expected to improve. Thus, vigorously develop decoupling between economy-air pollution-carbon reduction in Hebei. As opposed to efficiency improvement, structural adjustments were demonstrated to be more effective than short-term efficiency improvements. In addition, the estimated development potential of traditional heavy industries such as steel and petrochemicals is limited, whereas the equipment manufacturing industry, closely linked to traditional industries, is expected to continue its development. Furthermore, although in the electrification process, coal consumption is still needed because of its indispensable role in the production process of heavy industries. The results can facilitate policy-making for heavy industrial aggregation areas' green transformation in shaping policies and actions with clear objectives, effective measures, and sound coordination.

Influencing factors and improvement paths of manufacturing innovation performance: Configuration analysis based on TOE framework.

Innovation is the first driving force to lead development, how to improve manufacturing innovation performance has become a hot topic. Based on 47 listed companies in the computer, communication and other electronic equipment manufacturing industry in the A-share market, this paper adopted the Fuzzy set qualitative comparative analysis (fsQCA) to explore the influencing factors of technology, organization and environment on the innovation performance of manufacturing industry and the improvement path. The findings are as follows: (1) A single condition is not a necessary condition for high innovation performance in manufacturing industry, but government support plays a key role in improving innovation performance in manufacturing industry. (2) There are two improvement paths for high innovation performance in manufacturing industry, which are specifically explained as "technology-environment dual improvement path" and "technology-organization-environment collaborative improvement path". (3) The improvement of innovation performance in the manufacturing industry is the result of multiple factors, showing the characteristics of "all paths lead to the same destination". Different manufacturing enterprises have different paths to improve innovation performance based on their actual conditions. Based on these findings, this study may provide some implications for the effective improvement of manufacturing innovation performance.

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.

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.

MOTIVATIONAL MECHANISM OF ADVANCED EQUIPMENT MANUFACTURING INDUSTRY TECHNOLOGICAL INNOVATION BASED ON EVOLUTIONARY GAME

ABSTRACT: he advanced equipment manufacturing industry is an important symbol of a country's core competitiveness and an essential part of strategic emerging industries, whereas technological innovation is the key driving force for the progress of the advanced equipment manufacturing industry. A three-party (government-enterprise-Research & Development(R&D) institution) evolutionary game model was established to explore the internal mechanism of technological innovation impetus in the advanced equipment manufacturing industry. Results show that: (1) The driving force of technological innovation in the advanced equipment manufacturing industry comes from the cost-benefit situations of different subjects. (2) The strategy selection of subjects has a considerable learning effect. The decision of one subject is influenced by the decisions of the two others, and their decision behaviors tend to be identical. (3) Technology spillover effect and penalty effect exert a minor influence on the innovation decisions of different subjects. The findings enrich the theoretical results of technological innovation in the advanced equipment manufacturing industry by simulating different subjects' selection mechanisms of innovation strategies. It also provides practical guidance for the advanced equipment manufacturing industry to choose technological innovation strategies in different scenarios. Keywords: advanced equipment manufacturing industry; technological innovation impetus; evolutionary game

A cooperative hierarchical deep reinforcement learning based multi-agent method for distributed job shop scheduling problem with random job arrivals

Distributed manufacturing can reduce the production cost through the cooperation among factories, and it has been an important trend in the industrial field. For the enterprises with daily delivered production tasks, the random job arrivals are regular. Thus, the Distributed Job-shop Scheduling Problem (DJSP) with random job arrivals is studied, and it is a typical case from the equipment manufacturing industry. The DJSP involves two coupled decision-making processes, job assigning and job sequencing, and the distributed and uncertain production environment requires the scheduling method to be more responsive and adaptive. Thus, a Deep Reinforcement Learning (DRL) based multi-agent method is explored, and it is composed of the assigning agent and the sequencing agent. Two Markov Decision Processes (MDPs) are formulated for the two agents respectively. In the MDP for the assigning agent, fourteen factory-and-job related features are extracted as the state features, seven composite assigning rules are designed as the candidate actions, and the reward depends on the total processing time of different factories. In the MDP of the sequencing agent, five machine-and-job related features are set as the state features, six sequencing rules make up the action space, and the change of the factory makespan is the reward. Besides, to enhance the learning ability of the agents, a Deep Q-Network (DQN) framework with variable threshold probability in the training stage is designed, which can balance the exploitation and exploration in the model training. The proposed multi-agent method’s effectiveness is proved by the independent utility test and the comparison test that are based on 1350 production instances, and its practical value in the actual production is implied by the case study from an automotive engine manufacturing company.

Effect of manufacturing service transformation management on technological innovation

The immediate need for servitization and digital technologies like the Internet of Things (IoT), cloud computing and predictive analysis must be addressed in manufacturing industries. This research aims to analyze the impact of manufacturing services transformation management from traditional methods to technological innovation. This research approached the panel data of Chinese multinational equipment manufacturing industries for the analytical purpose from 2011 to 2017. The unit-root test has checked the stationarity of panel data and cointegration regression checked the long-term relationship among selected variables. The research results have confirmed a long-run and positive relationship between service transformation management of manufacturing enterprises and technological innovation. The positive upward trend among these two variables is because of increasing market demand-driven, open innovation network construction and increase in capital (intellectual, human & financial capital). The negative sign of values is declared the validity of an inverted “U-Shaped” analysis, which confirms the relationship's longevity. The adaptation of technological innovation has also indicated the growing performance trend in service management enterprises. As per the rule of inverted “U-Shaped” law, at a specific point, the manufacturing enterprises’ servitization management will start declining, and it will continue as a cyclic process. This research suggests that enterprises should prepare for the declining phase of servitization and sort out the other performance growth area, which will help to balance the performance of manufacturing enterprises wholesomely.

An improved multi-objective Wild Horse optimization for the dual-resource-constrained flexible job shop scheduling problem: A comparative analysis with NSGA-II and a real case study

The equipment manufacturing industry needs skilled workers to operate a specific set of machines following process specifications. Optimizing machine and worker assignments to achieve maximum efficiency is a critical problem for workshop managers. This paper investigates a multi-objective dual-resource-constrained flexible job shop scheduling problem. An improved wild horse optimization (IWHO) algorithm is developed to simultaneously optimize three objectives: makespan, maximum machine workload, and total machine workload. To evaluate the quality of individuals in multi-objective optimization, the Pareto fast non-dominated sorting method is used, and the crowding distance is calculated. To update the algorithm's solution, the crossover and mutation operations are used. Further, a local neighborhood search strategy is employed to enhance searchability and avoid trapping into the local optima. The benchmark of the flexible job shop scheduling problem is extended to create test instances, and the performance of the suggested IWHO algorithm is evaluated compared with the NSGA-II. The computational results show that the IWHO algorithm provides a non-dominated efficient set within a reasonable running time. Furthermore, a buffers and chain coupler assembly process is designed to analyze the practical value of the IWHO algorithm. The proposed solutions can be used to generate daily schedules for managing machines, workers, and production cycles.