Mode Of Production Research Articles

Inventory, Dynamic Evolution, and Scenario Projections of Agricultural Carbon Emissions in Shandong Province, China

The reduction in agricultural carbon emissions (ACEs) in Shandong Province is essential to China’s carbon peak and carbon neutrality objectives. In this regard, we constructed an ACE inventory for Shandong Province at a resolution of 1 km × 1 km, integrating the emission factor method with geographic information system (GIS) technology. Building upon this, we explored the dynamic evolution patterns of ACEs using kernel density estimation and conditional probability density estimation. Additionally, long short-term memory networks were trained to predict ACEs under various scenarios. The results showed that: (1) ACEs in Shandong Province exhibited two stages of change, i.e., “rise and decline”. Notably, 64.39% of emissions originated from the planting industry. The distribution of emissions was closely correlated with regional agricultural production modes. Specifically, CO2 emissions were predominantly distributed in crop cultivation areas, while CH4 and N2O emissions were primarily distributed in livestock breeding areas. The uncertainty of the emission inventory ranged from −12.04% to 10.74%, mainly caused by emission factors. (2) The ACE intensity of various cities in Shandong Province is decreasing, indicating a decoupling between ACEs and agricultural economic growth. Furthermore, the emission disparities among different cities are diminishing, although significant spatial non-equilibrium still persists. (3) From 2022 to 2030, the ACEs in Shandong Province will show a continuous downward trend. By 2030, the projected values under the baseline scenario, low-carbon scenario I, and low-carbon scenario II will be 6301.74 × 104 tons, 5980.67 × 104 tons, and 5850.56 × 104 tons. The low-carbon scenario reveals greater potential for ACE reduction while achieving efficient rural economic development and urbanization simultaneously. This study not only advances the methodology of the ACE inventory but also provides quantitative references and scientific bases for promoting low-carbon, efficient, and sustainable regional agriculture.

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.

Idealistic Modification of the Materialistic Veda

This article explores the origin and the later developments of the Veda. The objective is to examine the materialist philosophical content of the early Vedas and contrast it with the idealistic readings of the text from later eras. This article discusses the research issues surrounding the historical roots of the Vedic literature and the idealistic changes made to it in later times. The study has used historical materialism as a methodological tool and examined several Vedic verses, relating them to the socioeconomic conditions of the period in which they were composed. The article has analyzed the roots of some terminologies frequently used in the text and their connections to the philosophy of the Veda. The terms bhagavan, rita, Dānam, Havana, Brahman, yajña and name of some gods are frequently used in the Veda and their meanings are analyzed in a particular historical contexts. The article reveals that the Vedic verses are the simple creations of Vedic Aryans and they are associated with the everyday desires of the Vedic people, such as those for food, cattle, rain, safety, prosperity, health, and progeny. The early creation of the Vedic verses are this-worldly and materialistic in content and they are in no way otherworldly and spiritualistic. The meaning of most of the terminologies found in the text has materialistic origin and in course of time, their meanings are converted into opposite. The philosophy of the Veda has its root in the collective mode of production and with the introduction of private property and classes, the text is made an ideological tool of the minority of governing classes. The Veda's original materialistic meaning is altered, and the book is transformed into a spiritual guidebook—an ideological weapon used by the ruling classes to deceive and repress the vast majority of laboring people.

Mapping lock-ins and enabling environments for agri-food sustainability transitions in Europe

European agri-food systems must overcome structural lock-ins to achieve more sustainable modes of production and consumption. Yet European regions are highly diverse, and we lack understanding of how different regional characteristics may enable or inhibit sustainability transitions. This hinders the development of context-tailored governance strategies. In this paper, we identify and apply sets of spatial indicators to map the regional potentials for agri-food transitions. We first analyse the strength of lock-in to the incumbent agro-industrial paradigm. We then map the enabling environments for two alternative agri-food networks—multifunctional value chains and civic food networks—that each embed distinct social–ecological qualities of agriculture and food. Results demonstrate a large spatial diversity in transition potential, with stronger lock-ins throughout North and Western Europe and stronger enabling environments for agri-food transitions in Italy, France, Switzerland, and Southwest Germany. We find that lock-ins are strongest in livestock-dominated regions and are associated with higher GHG emissions and excess nitrogen levels. Our study demonstrates the need for coordinated public policies that (1) leverage region-specific transition potentials and (2) enable complementary innovations in market-based and community-led networks.

EXPERT ASSESSMENT OF THE THERMODYNAMIC STATE OF THE "METAL-SLAG" SYSTEM IN REDUCING AND OXIDIZING CONDITIONS

A new approach has been developed to assess the thermodynamic state of the "metal-slag" system in the production of cast iron and steel using the parameters of interatomic interaction. As a result of computational and analytical studies of the indicators of the melting products of the blast-furnace and steelmaking processes, the regularities of the coordinated change in the chemical compositions of the metal and slag melts were revealed. A close relationship has been established between the integral indicators of metal (ZY) and slag (, e), characterizing the chemical and structural state of their melts. As a criterion for evaluating the thermodynamic matching of melts of the "metal-slag" system when it tends to equilibrium, an analytical expression is proposed in the form , which can be implemented in automated process control systems to solve the problems of controlling the thermal and slag modes of iron production and steel of the required composition.

Problems in Efficient Development of Clean Energy in Xizang

As Xizang's economic development enters the new normal stage, the extensive and highly polluting production mode in the past no longer meets the needs of high-quality economic development in Xizang. Therefore, there is an urgent need to reduce the consumption of traditional fossil energy, develop low-carbon and clean green energy, optimize the energy structure, and promote green and sustainable economic development.

Research on optimization layout of hydrogen refueling facility network based on renewable energy hydrogen production mode

Within the transportation sector, hydrogen fuel cell vehicles represent the most efficient method of harnessing hydrogen energy. The establishment of a hydrogen refueling facility network that encompasses the entire cycle of hydrogen energy production, transportation, and application contributes to the effective promotion of hydrogen fuel cell vehicles. However, existing research predominantly focuses on hydrogen production and the conversion of refueling stations, neglecting the economic and stability considerations of the full-cycle use of hydrogen energy. This study proposes a hydrogen refueling facility network planning model that utilizes hydrogen energy throughout its full cycle. Firstly, a hydrogen demand estimation model based on the application scenarios of hydrogen fuel cell vehicles which clarifies the hydrogen requirements in each scenario is proposed. Regional hydrogen energy needs and the costs of building hydrogen refueling stations are then considered, using a set cover model to optimize the overall layout of existing and new refueling stations. Finally, taking into account the supply costs of renewable energy hydrogen production points and regional supply stability, a hydrogen refueling facility network planning model is constructed. The Beijing-Tianjin-Hebei region is selected as a case study, establishing a large-scale hydrogen supply network based on renewable energy hydrogen production, transportation, and usage.

Using Transfer Learning and Radial Basis Function Deep Neural Network Feature Extraction to Upgrade Existing Product Fault Detection Systems for Industry 4.0: A Case Study of a Spring Factory

In the era of Industry 3.0, product fault detection systems became important auxiliary systems for factories. These systems efficiently monitor product quality, and as such, substantial amounts of capital were invested in their development. However, with the arrival of Industry 4.0, high-volume low-mix production modes are gradually being replaced by low-volume high-mix production modes, reducing the applicability of existing systems. The extent of investment has prompted factories to seek upgrades to tailor existing systems to suit new production modes. In this paper, we propose an approach to upgrading based on the concept of transfer learning. The key elements are (1) using a framework with a basic model and an add-on model rather than fine-tuning parameters and (2) designing a radial basis function deep neural network (RBF-DNN) to extract important features to construct the basic and add-on models. The effectiveness of the proposed approach is verified using real-world data from a spring factory.

Tradition and Innovation in the Italian Wine Industry: The Best Practices of Casa Paladin

This study aims to make a significant contribution to the development of a model for integrating research in the wine sector, innovative knowledge, and family businesses with the traditional mode of production in the context of the introduction of modern production technologies with a view to sustainability and the improvement of services in tourism. From a methodological point of view, the research is based on the case study and, in particular, on Casa Paladin, a family business in the Italian wine sector, which bases its strategy on innovation in production processes and customer relations to obtain high product quality with the aim of meeting consumer needs. The findings underscore the crucial role of family businesses in preserving cultural and traditional elements in the wine sector, with technology and innovation serving as vital drivers for their development. Casa Paladin’s commitment to innovation in production processes and products is evident, emphasizing sustainability as a core element that impacts customer relationships and product quality. Enotourism, including tastings, festivals, and fairs, emerges as a significant aspect contributing to the promotion of the company’s history, culture, and traditions. This study posits Casa Paladin as a notable example in the Italian wine industry, offering transferable insights for other businesses. Its successful integration of culture, innovation, and sustainability contributes to a broader understanding of the contemporary role of family businesses in the Italian wine sector.

Reality Characteristics,Power Mechanism and Path Choice of New Quality Productivity

New-quality productivity is a new type of productivity that emerges in the context of a new era, a new economy and a new industry, aiming at high-quality development, led by scientific and technological innovation, with high-quality workers at its core, and based on a new mode of production and organisation. The new productive forces has the characteristics of attaching importance to innovation, a long-term approach and a grasp of opportunities. These characteristics of the new productive forces enable them to meet the needs of enterprises to promote efficient production, the economic needs to promote high-quality industrial development, the social needs to meet the people􀆳s high quality of life, and the political needs to ensure that the country wins the initiative. The power mechanism of China􀆳s new quality productivity consists of the driving force of scientific and technological innovation, the driving force of government support and the pulling force of market demand. On the basis of the internal and external driving forces, emphasis is placed on building a scientific and technological innovation system with scientific and technological innovation as the core, cultivate emerging future industries by means of government-enterprise synergy, and adhering to high-quality development guided by social demand, in order to promote the rapid development of new quality productivity, in order to contribute to the development of productive forces and the sustainable development of human society.

Affording digital transformation: The role of industrial Internet platform in traditional manufacturing enterprises digital transformation

This study aims to investigate the role of industrial Internet platform in facilitating the digital transformation of traditional manufacturing enterprises. While prior research has predominantly focused on industrial Internet platform enterprises, there is a noticeable dearth of research concerning traditional manufacturing enterprises lacking the resources to establish such platforms. To address this research gap, we conduct an exploratory case study and propose an affordances upgrade model that elucidates the digital business transformation process of these manufacturing enterprises leveraging industrial Internet platforms. The research findings can be summarized from two key perspectives. Firstly, the industrial Internet platform offers valuable technical support and potential opportunities for manufacturing enterprises to achieve digital business transformation through three distinct affordances: consonance, resonance, and adaptation. These affordances enable enterprises to align their operations with the capabilities and possibilities provided by the platform, thus facilitating their digital transformation. Secondly, to effectively harness these affordances, enterprises must strategically leverage the platform's technical services and systems in their production and operational practices. Through the accumulation of practical experiences, enterprises gradually transition their production modes from experience institutionalization and standardization to a state of refinement. The dynamic leapfrogging process of digital transformation in traditional manufacturing enterprises, facilitated by the industrial Internet platform, is reflected in the realization of these three affordances and their underlying resource capabilities. This research significantly contributes to the field by expanding the scope of inquiry to encompass traditional manufacturing enterprises and presenting a stage model for their digital transformation utilizing industrial Internet platform.

A typology of polished stone axes in the middle Yellow River and their impact on early complex societies

Polished stone axes have long been recognized as essential tools directly related to logging and wood processing in sedentary societies, playing a crucial role in prehistoric people's survival and social production. Their evolution as production tools has been a driving force behind the advancement of society's productive capacities. In this article, information and data on stone axes from the middle Yellow River region are systematically compiled, presenting a typology based on 640 stone axes from 153 sites spanning the Middle Neolithic to the Bronze Age. The main conclusions derived from the study are as follows: (1) Typological study reveals that stone axes can be classified into 7 types. By examining the evolution of these types and their combinations, four distinct phases in the development of stone axes are identified, reaching its peak during the middle stage of Phase 4. (2) The flourishing of the stone axe types originated in the Yangshao period in the Guanzhong Plain. With the development of social complexity and the establishment of the Xia and Shang dynasties after the Longshan period, the center of gravity of stone axe types and assemblages gradually shifted to the Central Plains and the Fen River Valley. Finally, the genealogy spread to the eastern foothills of the Taihang Mountains and declined. (3) The types of stone axes from the late Yangshao period and the middle and late Longshan periods exhibit a clear shift, highlighting their functional differentiation during these periods. (4) The production of stone axes can be categorized into two modes based on the raw stone material used: (a) and (b). The metallization of tools, even during the so-called Bronze Age, experienced significant delays, and it was only during the Yinxu era that bronze axes began to replace some of the stone axes produced in mode (b). (5) The functional differentiation of axes and the transformation of the mode of production have played a role in inducing the emergence of social complexity and expediting its development to some extent.

Research on the Accounting and Prediction of Carbon Emission from Wave Energy Convertor Based on the Whole Lifecycle

Wave energy, as a significant renewable and clean energy source with vast global reserves, exhibits no greenhouse gas or other pollution during real-sea operational conditions. However, throughout the entire lifecycle, wave energy convertors can produce additional CO2 emissions due to the use of raw materials and emissions during transportation. Based on laboratory test data from a wave energy convertor model, this study ensures consistency between the model and the actual sea-deployed wave energy convertors in terms of performance, materials, and geometric shapes using similarity criteria. Carbon emission factors from China, the European Union, Brazil, and Japan are selected to predict the carbon emissions of wave energy convertors in real-sea conditions. The research indicates: (1) The predicted carbon emission coefficient for unit electricity generation (EFco2) of wave energy is 0.008–0.057 kg CO2/kWh; when the traditional steel production mode is adopted, the EFco2 in this paper is 0.014–0.059 kg CO2/kWh, similar to existing research conclusions for the emission factor of CO2 for wave energy convertor (0.012–0.050 kg CO2/kWh). The predicted data on carbon emissions in the lifecycle of wave energy convertors aligns closely with actual operational data. (2) The main source of carbon emissions in the life cycle of a wave energy converter, excluding the recycling of manufacturing metal materials, is the manufacturing stage, which accounts for 90% of the total carbon emissions. When the recycling of manufacturing metal materials is considered, the carbon emissions in the manufacturing stage are reduced, and the carbon emissions in the transport stage are increased, from about 7% to about 20%. (3) Under the most ideal conditions, the carbon payback period for a wave energy convertor ranges from 0.28 to 2.06 years, and the carbon reduction during the design lifespan (20 years) varies from 238.33 t CO2 (minimum) to 261.80 t CO2 (maximum).

Does Ecological Planting–Breeding Mix Pattern Improve Farmers’ Subjective Well-Being? Evidence from the Middle and Lower Reaches of the Yangtze River

The Chinese government is making vigorous efforts to control agricultural pollution. The promotion of an ecological planting–breeding mix pattern is one of them. Farmers’ mode of production will affect their subjective well-being. Thus, this paper aims to analyze the impact of adopting the ecological planting–breeding mix pattern on farmers’ subjective well-being based on 895 pieces of survey data from the provinces of Hubei, Hunan, and Jiangxi in the middle and lower reaches of the Yangtze River. Using the endogenous switching regression model, we find that the adoption of the ecological planting–breeding mix pattern has a significant positive effect on farmers’ subjective well-being. Based on this counterfactual hypothesis, if the farmers who actually adopted the ecological planting–breeding mix pattern did not adopt it, their subjective well-being would decrease from 4.006 to 3.669. Further examination indicates that self-worth identification, income increase, and neighborhood communication could be potential mechanisms. Additionally, the effect of the ecological planting–breeding mix pattern on subjective well-being is stronger in the group with low financial support. The technical support provided by the government does not have a significant regulatory effect on the adoption of the ecological planting–breeding mix pattern on subjective well-being. Our results suggest that farmers’ ecological production behavior can affect their subjective well-being. This may be relevant to many developing countries today that are attempting to adopt ecological agriculture patterns.

Conformation selection by ATP-competitive inhibitors and allosteric communication in ERK2.

Activation of the extracellular signal-regulated kinase-2 (ERK2) by phosphorylation has been shown to involve changes in protein dynamics, as determined by hydrogen-deuterium exchange mass spectrometry (HDX-MS) and NMR relaxation dispersion measurements. These can be described by a global exchange between two conformational states of the active kinase, named 'L' and 'R,' where R is associated with a catalytically productive ATP-binding mode. An ATP-competitive ERK1/2 inhibitor, Vertex-11e, has properties of conformation selection for the R-state, revealing movements of the activation loop that are allosterically coupled to the kinase active site. However, the features of inhibitors important for R-state selection are unknown. Here, we survey a panel of ATP-competitive ERK inhibitors using HDX-MS and NMR and identify 14 new molecules with properties of R-state selection. They reveal effects propagated to distal regions in the P+1 and helix αF segments surrounding the activation loop, as well as helix αL16. Crystal structures of inhibitor complexes with ERK2 reveal systematic shifts in the Gly loop and helix αC, mediated by a Tyr-Tyr ring stacking interaction and the conserved Lys-Glu salt bridge. The findings suggest a model for the R-state involving small movements in the N-lobe that promote compactness within the kinase active site and alter mobility surrounding the activation loop. Such properties of conformation selection might be exploited to modulate the protein docking interface used by ERK substrates and effectors.

Conformation selection by ATP-competitive inhibitors and allosteric communication in ERK2

Activation of the extracellular signal-regulated kinase-2 (ERK2) by phosphorylation has been shown to involve changes in protein dynamics, as determined by hydrogen-deuterium exchange mass spectrometry (HDX-MS) and NMR relaxation dispersion measurements. These can be described by a global exchange between two conformational states of the active kinase, named ‘L’ and ‘R,’ where R is associated with a catalytically productive ATP-binding mode. An ATP-competitive ERK1/2 inhibitor, Vertex-11e, has properties of conformation selection for the R-state, revealing movements of the activation loop that are allosterically coupled to the kinase active site. However, the features of inhibitors important for R-state selection are unknown. Here, we survey a panel of ATP-competitive ERK inhibitors using HDX-MS and NMR and identify 14 new molecules with properties of R-state selection. They reveal effects propagated to distal regions in the P+1 and helix αF segments surrounding the activation loop, as well as helix αL16. Crystal structures of inhibitor complexes with ERK2 reveal systematic shifts in the Gly loop and helix αC, mediated by a Tyr-Tyr ring stacking interaction and the conserved Lys-Glu salt bridge. The findings suggest a model for the R-state involving small movements in the N-lobe that promote compactness within the kinase active site and alter mobility surrounding the activation loop. Such properties of conformation selection might be exploited to modulate the protein docking interface used by ERK substrates and effectors.

Hybrid hydropeaking mitigation at storage hydropower plants combining compensation basins with battery energy storage systems (BESS)

Storage hydropower has the ability of flexibly generating electricity to match fluctuating power demand. However, rapid changes of water discharge in river reaches downstream of storage hydropower plants, referred to as hydropeaking, result from this intermittent production mode and may have potentially damaging effects on a river’s ecosystem. The construction of compensation basins usually embedded along the waterway between the powerhouse and the receiving water represents the state-of-the-art for hydropeaking mitigation. In recent studies, the application of battery energy storage systems (BESS) as an alternative mitigation option has been proposed. Instead of buffering the turbine discharge in a basin, electrical energy is stored in and retrieved from a BESS to meet short-term demand requirements, while the hydraulic machinery is ramping up and down according to environmental discharge constraints. The technical and economic feasibility of BESS compared to compensation basins is evaluated based on a newly introduced first-order assessment approach. If environmental discharge constraints are to be strictly followed, the exclusive application of BESS is deemed not feasible, due to the limited operation range of Francis and – to a lesser extent – Pelton turbines, especially for low partial loads. Therefore, hybrid systems combining a BESS with a smaller compensation basin are investigated as existing research focused either on a basin- or a BESS-only approach. Based on production time series of three case study hydropower plants, the sizes of basins and BESS are estimated. Basin sizes can be reduced with a hybrid system by 51% to 96% compared to a basin-only approach, with BESS energy capacities ranging approximately from 10 to 100 MWh. The cost efficiency of hybrid systems is strongly influenced by two factors, namely the future battery costs as well as the overall unit costs for the compensating basin. While the former are subject to considerable uncertainty especially as BESS reach their end-of-life much earlier compared to hydraulic structures and therefore require regular replacements after 10 to 15 years, the latter are highly site-specific as they include land availability and acquisition costs. The case studies show that a hybrid system of basin and BESS may represent a cost-competitive alternative to a basin-only approach when the latter’s investment costs are high or space is limited, even if additional revenue streams including ancillary services and energy arbitrage are not considered.

A Decentralized Optimization Algorithm for Multi-Agent Job Shop Scheduling with Private Information

The optimization of job shop scheduling is pivotal for improving overall production efficiency within a workshop. In demand-driven personalized production modes, achieving a balance between workshop resources and the diverse demands of customers presents a challenge in scheduling. Additionally, considering the self-interested behaviors of agents, this study focuses on tackling the problem of multi-agent job shop scheduling with private information. Multiple consumer agents and one job shop agent are considered, all of which are self-interested and have private information. To address this problem, a two-stage decentralized algorithm rooted in the genetic algorithm is developed to achieve a consensus schedule. The algorithm allows agents to evolve independently and concurrently, aiming to satisfy individual requirements. To prevent becoming trapped in a local optimum, the search space is broadened through crossover between agents and agent-based block insertion. Non-dominated sorting and grey relational analysis are applied to generate the final solution with high social welfare. The proposed algorithm is compared using a centralized approach and two state-of-the-art decentralized approaches in computational experiments involving 734 problem instances. The results validate that the proposed algorithm generates non-dominated solutions with strong convergence and uniformity. Moreover, the final solution produced by the developed algorithm outperforms those of the decentralized approaches. These advantages are more pronounced in larger-scale problem instances with more agents.

ЦИФРОВЫЕ РЕШЕНИЯ В АГРОБИЗНЕСЕ: ФОРМИРОВАНИЕ МЕТОДОЛОГИИ МОНИТОРИНГА ЗЕРНОВОГО ПРОИЗВОДСТВА В УСЛОВИЯХ ТЕХНОЛОГИЧЕСКИХ ИННОВАЦИЙ

The active penetration of digital technologies into human economic activity objectively poses the task of forming an informational space and a new technological base across the entire economic space of society. The appearance of key sectors of the national economy is changing, with an increasing portion of business processes moving into the digital environment, thereby forming a barrier-free character of exchange and consumption relationships. Among the sectors of the domestic economy, the agricultural sector (AIC) has the highest rates of digital activity growth in recent years, with its indicator in 2023 amounting to 200% relative to the average level across the economy – 131% (compared with 2016), indicating the beginning of fundamental transformations within the mode of production. The leader of the AIC is the production of grain and its processing products, collectively accounting for more than a third of the total volume of the agri-food market, hence the processes of digital solutions penetration into grain production require close attention. The key business process of grain production is the monitoring of all its elements, ensuring the quality and timeliness of management decisions at each level of added value production. The spread of business models based on digital technologies requires a new methodology of platform solutions not only at the level of technological adaptation but also restructuring, modification of established ways of conducting agribusiness, and significant organizational changes. Systematization of digital solutions approaches shows that the use of artificial intelligence significantly accelerates the digital transformation of grain production; however, for a widespread transition to intelligent monitoring methods of grain production, a number of objective conditions must be met, among them: data handling, the ability to choose a computer vision model, creation of neural network architecture, organization of training for personnel capable of making decisions on digital platforms, and the formation of corresponding psychological-behavioral client content. The implementation of these conditions, based on ongoing institutional transformations, is capable of ensuring stable growth of grain production, reducing its energy intensity, and preparing personnel with digital economy competencies.

Mathematical model and adaptive multi-objective evolutionary algorithm for cellular manufacturing with mixed production mode

As the product mix in production changes dramatically, cell reconfiguration is requisite to smoothen the production process. Meanwhile, multiple production modes are simultaneously adopted in the site to promote productivity and assure flexibility, and thus the coordination scheduling among them becomes a challenging problem. To achieve cell reconfiguration and cell scheduling in a cellular manufacturing system in which no-idle flow-line and flexible job-shop production modes are hybridized, a mixed integer linear programming model is formulated and an enhanced adaptive multi-objective evolutionary algorithm is developed. In the proposed algorithm, a decision tree-based rule combination selector is developed to adaptively select the most appropriate rule combination fit for the given production scenario to generate a high-quality initial population. Three types of crossover operators and six objective-oriented local search operators are designed to increase the exploration and exploitation capability. An adaptive balance mechanism of exploration and exploitation is trained by Q-learning to maximize search efficiency. In addition, an adaptive adjustment mechanism of population size is designed to ensure diversity and speed up convergence. The comparative study demonstrates that the three proposed adaptive mechanisms are effective and the proposed algorithm with three adaptive mechanisms significantly outperforms other comparison algorithms in solving the studied problem.