Real Production Research Articles

Semi-supervised noise-resilient anomaly detection with feature autoencoder

Most methods only use normal samples to learn anomaly detection (AD) models in an unsupervised manner. However, these samples may be noisy in real-world applications, causing the models to be unable to accurately identify anomaly objects. In addition, there are a small number of anomaly samples in real industrial production that should be fully utilized to help model discrimination. Existing methods of introducing anomaly samples still have bottlenecks in model identification capabilities. In this paper, by introducing both normal and a few abnormal samples, we propose a novel semi-supervised learning method for anomaly detection, named RobustPatch, which can improve the model discriminability through a self-cross scoring mechanism and the learning of feature AutoEncoder. Our approach contains two core designs: Firstly, we propose a self-cross scoring module, calculating the weights of normal and anomaly features extracted from corresponding images using a self-scoring and cross-scoring manner, respectively. Secondly, our approach proposes a fully connected feature AutoEncoder to rate the extracted features, which is trained with the supervision of the scored weights. Extensive experiments on the MVTecAD and BTAD datasets validate the superior anomaly boundaries discriminability of our approach and superior performance in noise-polluted scenarios.

A model for energy predictions and diagnostics of large-scale photovoltaic systems based on electric data and thermal imaging of the PV fields

The aim of this investigation is the development of robust models for the performance prediction and automatic monitoring of large photovoltaic systems, based on historical and real-time electric and thermal data. This issue is increasingly important due to the worldwide diffusion of large photovoltaic systems and their need to identify and predict failures and malfunctions, in order to promptly assess the convenience of maintenance actions. The present model describes the response to irradiance and temperature conditions of both modules and inverters and also it is able to predict shading conditions able to affect the energy yield. The model has been validated against real electric measurements in 6 large PV plants located in southern Italy and it demonstrated to be able to predict the real time power production within a 4.1 % error. Even more importantly, the model and its comparison with subhourly measurements over several years has demonstrated its effectiveness in detecting downtime conditions caused by inverter or string problems. Simulations and measurements revealed that missed energy production due to electrical grid coupling downtime can exceed 50 % on certain days and that the shading conditions (up to 5 % of the daily energy production) can be easily detected and separated from component problems, thus avoiding false alarms. Finally, the analysis of aerial infrared images allowed to further test the model in failure detection capability, assess the relationship between thermal anomalies and underperformance conditions and in predicting the yearly deterioration rate at the PV plants.

Association Between Government Expenditures, Inflation and Interest Rate in Nigeria

The study analysed the relationship between government expenditure, economic growth, inflation and interest rate in Nigeria. The high inflation and interest rate with high government expenditure has been a problem for the Nigerian economy. Secondary data from 1986 to 2022 were collated from the Central Bank of Nigeria Statistical Bulletin and analyzed using multiple regression analysis. The results showed a positive relationship between inflation rate, government expenditure and Gross Domestic Product (GDP). This means that as inflation and government expenditure increased, real gross domestic product (GDP) would increase while there existed an inverse relationship between interest rate and gross domestic product (GDP), which implies that as interest rate increased, GDP would decrease. The test for the model fitness using adjusted R2 indicated the predictor values accounted for 95 per cent variations in the dependent variable (GDP). The study therefore recommended that monetary and fiscal authorities should regulate government expenditure by targeting a contractionary fiscal policy which decreases the amount of government expenditure in a bid to tackle inflationary trends. The government should aim at inflation targeting through a single-digit interest rate which could encourage the private sector to accumulate funds to be used to augment government efforts in regulating stability in the economy. Besides, even though the structural inflation facing the country did not have a very significant effect on the domestic economy concerning government expenditure and its impact on GDP, this should be resolved by dismantling all rigidities and bottlenecks such as the war against insecurities and herdsmen that have been distorting farming and agricultural output in Nigeria of late.

Path detectability verification for time-dependent systems with application to flexible manufacturing systems

This paper addresses the path detectability verification problem for time-dependent systems modeled by time labeled Petri nets (TLPNs). To capture the information precisely, it may not be sufficient to estimate the current state by resorting to the partial system observation, and it is usually crucial to decide the path of a system to reach the current state. Path detectability characterizes a time-dependent system whose current state and the corresponding path can be uniquely determined after a real-time observation (RTO). Revised state class graphs (RSCGs) are proposed to capture the time information for the evolution of the RTO in a TLPN system. We demonstrate the time information overlap problem in the RSCG, i.e., several paths are associated with the same observable events and the same time instants, which leads to such paths that cannot be distinguished. The nodes required to be computed in the proposed RSCGs are always less or equal to those of the modified state class graphs reported in the literature, since the enumeration of all the states is avoided. Based on the RSCG, an RSCG observer is formulated to address the time information overlap problem and capture the number of such paths in the TLPN system. The efficiency analysis of this verification method is provided. In this paper, the results are applied to a real production system, exposing the practical value of the reported method.

Prediction of future customer needs using machine learning across multiple product categories.

In recent years, computational approaches for extracting customer needs from user generated content have been proposed. However, there is a lack of studies that focus on extracting unmet needs for future popular products. Therefore, this study presents a supervised keyphrase classification model which predicts needs that will become popular in real products in the marketplace. To do this, we utilize Trending Customer Needs (TCN)-a monthly dataset of trending keyphrase customer needs occurring in new products during 2011-2021 across multiple categories of Consumer Packaged Goods e.g. toothpaste, eyeliner, beer, etc. We are the first study to use this specific dataset and employ it by training a time series algorithm to learn the relationship between features we generate for each candidate keyphrase on Reddit to the ones in the dataset 1-3 years in the future. We show that our approach outperforms a baseline in the literature and through Multi-Task Learning can accurately predict needs for a category it wasn't trained on e.g. train on toothpaste, cereal, and beer products yet still predict for shampoo products. The findings from this research could provide many advantages to businesses such as gaining early access into markets.

Reproductive Morphology and Success in Annual versus Perennial Legumes: Evidence from Astragalus and the Fabeae (Papilionoideae).

The third largest angiosperm family, Leguminosae, displays a broad range of reproductive strategies and has an exceptional practical value. Whereas annual legume species are mostly planted as crops, there is a significant interest in breeding and cultivating perennials. It is therefore of importance to compare reproductive traits, their interactions and the resulting productivity between related annual and perennial species. Two highly variable taxa were chosen for this purpose, the Fabeae tribe, including numerous temperate crops, and the largest angiosperm 'megagenus' Astragalus. A dataset of quantitative reproductive traits was composed of both originally obtained and previously published data. As a result of statistical analysis, we found that perennials in both groups tend to produce more flowers per axillary racemose inflorescence as well as more ovules per carpel. Perennial Astragalus also have larger flowers. Only a part of the developing flowers and ovules gives rise to mature pods and seeds. This difference is especially pronounced in small populations of rare and threatened perennials. Numerous reasons underlie the gap between potential and real productivity, which may be potentially bridged in optimal growing conditions.

A measurement system for assessing the completeness and deformation of aluminium ladders: case study

Aluminium ladders are popular tools used in many fields of industry and construction. Their completeness and durability are key to ensure work safety and avoid dangerous scenarios. To provide safety to aluminium ladders, it is necessary to perform completion checks and measurements of ladder deformation are necessary during operation. This article presents a measurement system to assess the completeness of aluminium ladders and describes the use of the GOM Pontos system to measure the deformation of the ladder during operation. The main purpose of the research was to verify whether the system proposed by the authors works successfully and the results obtained allow it to be deployed in the production line. Research results and conclusions can provide a basis for further research and may help improve the production and utilisation processes of aluminium ladders. The research was carried out in a company that is engaged in the production of aluminium ladders. This allowed a thorough examination of the efficiency and effectiveness of the proposed methods under real production conditions, which is crucial for the practical implementation of this technology in the industry.

Insight into the dynamic tensile behavior of deep anisotropic shale reservoir after water-based working fluid cooling

During deep shale gas production, flowing water-based working fluid inevitably cools shale reservoirs around boreholes and some fractures, and possible extraction methods induce dynamic stresses. To understand the dynamic tensile behavior of deep anisotropic shale reservoir after water-based working fluid cooling, a split Hopkinson pressure bar was used for performing the dynamic Brazilian tests on shale samples with bedding angles of 0°, 30°, 45°, 60° and 90° after reservoir temperature realization (25–200 °C) and water cooling. The results illustrate that dynamic tensile strength of shale samples decreases gradually as reservoir temperature increases under the loading rates 100–1000 GPa/s. From room temperature to 200 °C the most strength deterioration appears on samples with the bedding angle of 90°. A dynamic tensile strength deterioration model for deep shale reservoirs after water-based working fluid cooling is proposed considering the influence of loading rate and bedding angle. Geometrical trajectories of the main failure cracks are separated into three types, i.e., fully central tensile failure, tensile-shear failure and fully shear failure (sliding of bedding planes). For samples with bedding angles of 30°, 45° and 60°, increasing reservoir temperature encourages tensile failure to change into shear failure. The roles that bedding planes play in interacting with failure crack growth are summarized as IP mode (intersecting propagation), TP mode (turning propagation) and PP mode (promoting propagation). Anisotropic dynamic tensile strength responses are systematically discussed by using thermal stress simulation in ABAQUS, microstructure analyses, crack interaction conditions and the one-dimensional stress wave propagation theory. Based on experimental observations, field implications in borehole stability and fracturing of deep shale reservoirs are proposed under medium and high loading rates. This work is instrumental in providing valuable information and technology assistance for real deep shale gas production projects.

Production Optimization of Premium Food Can with Distortion Printing under Waving Requirement

This research aims to propose a novel approach for evaluating and minimizing scraps in an industrial production of premium food cans with distortion printing. Beyond conventional formability criteria, a waving requirement is introduced to ensure aesthetic quality of the printed graphics. The research focuses on real production conditions, specifically involving double-cold-reduced (DR) low-carbon steel sheets and chromium-coated tin-free steel with a thickness of 0.16 mm. The sheets are laminated on both sides with a plastic film prior to undergoing distortion printing on the exterior. Subsequently, a blank is subjected to a drawing-redrawing process to form a food can. To address challenges associated with characterizing these thin sheets, a material parameter identification method is proposed and demonstrated. The thickness profile and flange length are identified as key criteria for this identification process. Measurements of thickness distribution and flange length are obtained using digital image correlation (DIC) and microscopy techniques. Within the manufacturing system, uncertainties related to material properties and forming processes can result in scraps or defects. To analyze these processes, finite element analysis (FEA) is employed and validated through experiments. For the evaluation of scrap rates, uncertainty propagation is conducted using a metamodeling technique, specifically employing radial basis function (RBF) neural networks. The study concludes by offering process optimization recommendations aimed at reducing the scrap rate.

Impact of Financial Development on Economic Growth of Nepal

This study examines the impact of financial development on economic growth of Nepal. The dependent variables are real gross domestic product and per capita income. The independent variables are money supply, stock market capitalization, private sector credit, financial access and net foreign direct investment inflows. The study is based on time series data for the period by Ministry of Finance and Central Bureau of Statistics of Nepal. The correlation coefficients and regression models are estimated to test the significance and importance of financial development on economic growth in Nepal. The study showed that stock market capitalization has a positive impact on real gross domestic product and per capita income. It indicates that higher the stock market capitalization, higher would be the real gross domestic product and per capita income. Similarly, there is a positive impact of private sector credit to GDP on real gross domestic product and per capita income. It indicates that increase in private sector credit leads to increase in real gross domestic product and per capita income. In addition, broad money supply has a positive impact on real gross domestic product and per capita income indicating that higher the broad money supply, higher would be the real gross domestic product and per capita income. Similarly, financial access has a positive impact on real gross domestic product and per capita income. It indicates that increase in financial access leads to increase in real gross domestic product and per capita income. Likewise, net foreign direct investment inflow has a positive impact on real gross domestic product and per capita income. It indicates that increase in net foreign direct investment inflow leads to increase in real gross domestic product and per capita income of Nepal.

Financial Inclusion, Poverty Reduction, and Economic Growth in Nigeria: An Empirical Study Using SVAR Approach (1980-2020)

This study examines the empirical analysis of financial inclusion, poverty reduction and economic growth in Nigeria from 1981 to 2020 using the Structural Vector Auto-Regressive (SVAR) model for analysis, with Gross Domestic Product (GDP) as dependent variable and Branches of Commercial Banks (CBBA), Automated Teller Machine (ATMAD), Mobile Phone-Based Transactions and Broad Money Supply (MS) as explanatory variables. The findings of the study reveal that the unit root test shows that Real Gross Domestic Product (RGDP), Commercial Bank Branches (CBB), Money Supply (MS) and Poverty Rate (PR) are stationary at first difference. The results also reveal that in Nigeria, availability and access to money have a positive shock impact on RGDP, and CBB also has a favourable shock effect on RGDP in Nigeria. However, the PR has a negative impact on the RGDP. Therefore, the study recommends that the government should increase its efforts to promote financial inclusion and the Central Bank of Nigeria (CBN) should make commercial banks increase not only their branches but also increase the number of Automated Teller Machines (ATMs).

Domestic Private Investment and Selected Macroeconomic Indicators in Nigeria:An Econometric Analysis

Domestic private investment (DPI) plays significant role in economic growth of any economy. It helps in mobilizing domestic resources for socio-economic advancement. Domestic Investment and its determining factors remains one recalling issues in the field of economics and international finance. This is based on the fact that aggregate investment plays a dominant role in stimulating economic growth and development of every nation. However, most of the developing and underdeveloped economies have witnessed low domestic investment and consequently slow economic growth. Therefore, this study investigates the relationship, trends, and impact of domestic private investment and selected macroeconomic indicators in Nigeria, a developing economy. The study analyzed data gotten from the CBN Statistical Bulletin for the duration of 1991-2020. The statistical package used for the study is Econometric Views (E-Views 9.0). Multiple regression techniques via OLS, Augmented Dickey Fuller (ADF) test and Granger causality procedure was applied to determine causalities while Johansen Co-integration test was administered to verify sustainability of the long-run relationships. The result revealed that there exist short and long-run relationships between domestic private investment and key macroeconomic variables- Inflation, Lending rates, gross domestic savings, real gross domestic product, money supply in Nigeria. Vector error correction model was also adopted to determine the speed of adjustment between the independent variables and the dependent variables. The OLS result revealed that there exist a positive and significant relationship among GDSA, BMOS, RGDP and PDIV, negative and significant relationship among PLER, INFR, and PDIV. Therefore, the study recommends among others that Nigerian government must be resolute in their resolve to drive private domestic investment in the country by implementing appropriate monetary and fiscal policies that address inflation, interest rate, domestic savings and money supply. This will no doubt fast track inclusive growth.

New Law Responding to Imitations of Designs that Intersect Real and Virtual Space: Amendment of the Unfair Competition Prevention Act in Japan

Abstract Digital items that have the appearance of real utilitarian products are often tied to NFTs (Non-Fungible Tokens) in the virtual space known as the metaverse, and sold as goods. The designs of real utilitarian products are increasingly being converted into the designs of such NFT goods without the permission of their developers. In order to deal with these imitations, which intersect the real and the virtual worlds, the Japanese legislator decided to utilize the existing provision for the protection of forms of goods in the unfair competition prevention law (UCPA 1993). The law was amended in June 2023 to newly define ‘the act of offering through telecommunication line’ goods that imitate the form of another person’s goods as an act of unfair competition. Copyright protection under Japanese case law has rarely been granted to designs of utilitarian products, and under Japanese design law the product design is identified not only by its shape but also by its product type. The protection of image designs is almost exclusively limited to specific images unrelated to the NFT goods. However, the revised provision of UCPA 1993 has also been generally interpreted as the provision prohibiting the imitation of designs between homogeneous products. The amendment effectively negates this interpretation. This paper reveals the impact on the existing interpretation of the relevant provision of UCPA 1993 and the issues arising from other legal provisions with respect to such imitations.

Shaping of the risk of a series-parallel manufacturing structure maintenance according to quasi-coherence and the [formula omitted]-th survival value

The paper presents the authors’ model for determining the probability value of the survival function of the difference of two random variables: TP – the amount of time of correct operation and TB – the amount of time of failure. For this purpose, the concept of K-th survival value is introduced. The K-th survival value defines the probability that the total time of correct operation of the production system will be longer by at least k time units (for k≥0) than the total amount of time of failures. The model was exemplified by a real production system with a series–parallel structure. In the nesting manufacturing structure, the availability of machines determines the number and the type of flow streams of the processed material. To formalise the chains of relations at a given Δt, this paper utilises the quasi-coherence property. The paper identifies four possible cases of system operation: 1) a fully operational system in which every machine is capable of meeting the assumed production schedule; 2) a system which has lost the property of quasi-coherence (certain system objects are in an inoperable state) but it is possible to meet the assumed production schedule; 3) a system which has lost the property of quasi-coherence but the assumed task schedule is possible to meet after redefining the material flow streams; 4) a system which has lost the property of quasi-coherence and it is impossible to meet the assumed production schedule. Based on the definition of the survival function [1] S of the random variable T: ST:=PT>τ=1-FTτ,τ∈R, the concept of K-th survival value was introduced. For possible cases, K-th survival value was defined as: KT1,Ti=PT1-Ti≥k=1-FT1-Tik (for i=2,3,4), where T1 and Ti are random variables with given probability distributions. In turn, k is a deterministic value that determines the total number of time units of correct system operation in relation to the total failure time. The application of the developed algorithm enables the system to be analysed in terms of three aspects: (1) to determine the probability of on-time execution of tasks, taking into account historical values of machine failure and correct operation durations; (2) to determine the stability of the system based on empirical data; (3) to monitor the level of risk in real time. The paper validates the model by determining the stability of the analysed system for real machine failure data. The introduced criterion of stochastic stability of the production system has been verified for data in which distributions of correct operation times and failure times are random variables from the family of Gamma distributions. The presented model can be included in the group of multifactorial risk assessment methods.

Design of A Property Product Recommendation System Using Association Rule Method Based on User Interaction Patterns

This work creates an association rule-based real estate product recommendation system. Personalizing property suggestions based on user behaviour optimises property searches. Data-driven insights enhance dynamic property market user experience. Association rules alter property advice. Data-driven insights and adaptability improve property search by proposing homes depending on user engagement patterns. Strong algorithms establish location, budget, and property associations, and association rule technology and user interaction patterns increase property recommendations. Personalized property discovery uses accurate and adaptive suggestions from continuous learning. Results reveal that user interaction pattern-based association rule techniques improve property suggestion accuracy and personalization. The system's tailored advice improves property market decisions, confirming its usefulness and adaptability. Insufficient user data might distort suggestions, especially for specific interests. Not enough user diversity can lower system accuracy. User data and privacy must be secured to optimize the recommendation system. Association rules and user engagement patterns can transform property recommendations. This innovative technique can improve property searches, provide personalized ideas, and help consumers make informed decisions in a competitive market.

Network Topology Evaluation and Transitive Alignments for Molecular Networking.

Untargeted tandem mass spectrometry (MS/MS) is an essential technique in modern analytical chemistry, providing a comprehensive snapshot of chemical entities in complex samples and identifying unknowns through their fragmentation patterns. This high-throughput approach generates large data sets that can be challenging to interpret. Molecular Networks (MNs) have been developed as a computational tool to aid in the organization and visualization of complex chemical space in untargeted mass spectrometry data, thereby supporting comprehensive data analysis and interpretation. MNs group related compounds with potentially similar structures from MS/MS data by calculating all pairwise MS/MS similarities and filtering these connections to produce a MN. Such networks are instrumental in metabolomics for identifying novel metabolites, elucidating metabolic pathways, and even discovering biomarkers for disease. While MS/MS similarity metrics have been explored in the literature, the influence of network topology approaches on MN construction remains unexplored. This manuscript introduces metrics for evaluating MN construction, benchmarks state-of-the-art approaches, and proposes the Transitive Alignments approach to improve MN construction. The Transitive Alignment technique leverages the MN topology to realign MS/MS spectra of related compounds that differ by multiple structural modifications. Combining this Transitive Alignments approach with pseudoclique finding, a method for identifying highly connected groups of nodes in a network, resulted in more complete and higher-quality molecular families. Finally, we also introduce a targeted network construction technique called induced transitive alignments where we demonstrate effectiveness on a real world natural product discovery application. We release this transitive alignment technique as a high-throughput workflow that can be used by the wider research community.

Deep learning-based quality prediction for multi-stage sequential hot rolling processes in heavy rail manufacturing

Traditional quality control practices in heavy rail manufacturing primarily rely on experiential knowledge and numerical simulations. However, these methods come with significant drawbacks, including high costs, safety concerns, and limited accuracy due to the inadequate consideration of various influencing factors. Despite the accumulation of extensive data by some heavy rail manufacturers in recent years, achieving data-driven quality modeling remains a formidable challenge, mainly due to the complexity of the multi-stage sequential production process. This study presents the Temporal-Spatial Mapping approach to transform isochronous sampling data into spatially aligned data. Additionally, it introduces a multi-objective prediction model, Temporal-Fusion-LSTM (TFL), designed for evaluating the quality of heavy rails. The proposed model presents the Double-Layer LSTM with Time-Steps Fusion (DLL) architecture. The first layer of the DLL architecture targets the temporal transfer regularity within a single stage, while the second layer addresses the transition dynamics across multiple rolling stages in the heavy rail rolling process. Following this, the Local Process Correlation Matrix (LPCM) based on the Maximal Information Coefficient (MIC) is employed to empower the model to comprehend the intricate interactions among parameters in each stage of the rolling process. Finally, the Multi-Gate based Shared-Bottom architecture is employed to accentuate manufacturing processes highly correlated with the target quality indicators during multi-objective prediction. Real production data from a heavy rail factory is utilized to validate the effectiveness of the method. The experimental results unequivocally demonstrate that the proposed method accurately predicts heavy rail quality and facilitates cross-stage prediction, thus establishing the groundwork for data-driven quality diagnosis and pre-control in heavy rail production.

Links between technological innovation, financial efficiency and environmental quality using quantile regressions: The role of foreign direct investment, institutional quality and natural resources

Policymakers and academics are interested in identifying mechanisms that promote environmental sustainability due to their relationship with climate change. This research evaluates the channels that transmit the effect of technology and financial efficiency on environmental quality. The relationship is moderated by real production per capita, foreign direct investment, natural resource rents, and institutional quality in the context of the Environmental Kuznets Curve. The research covers the 1996–2021 period for a sample of 88 economies classified into three groups according to the World Bank Atlas Method. We use second-generation cointegration techniques with structural breaks and quantile regression models. The findings offer sufficient evidence to conclude that the impact of technology and financial efficiency on environmental quality is heterogeneous throughout the distribution. Our findings suggest that technology is more associated with maximizing production than mitigating or restoring environmental deterioration. A policy implication derived from our research is to encourage the generation of carbon-free technology and promote financial efficiency in upper-middle-income countries to achieve environmental sustainability.

Budget Deficit and Economic Growth in Nigeria: An Empirical Analysis

This study was expected to investigate the impact of Budget Deficit on economic growth of Nigeria between 1983 and 2023. Ex – post facto research design was accepted; annual time series data for analyses were collected from Central Bank of Nigeria Statistical Bulletin of 2022. Real Gross Domestic Product (RGDP) was used as the explained or dependent variable proxy for economic growth. budget deficit (BDF), inflation (INF) and money supply (MS) all represent explanatory or independent variables. The study employed Auto Regressive Distributed Lag (ARDL) Model which was used to analyzed and evaluate the coefficients of the model’s parameters. Other diagnostic tests employed by this study include; unit root test, descriptive statistics, correlation coefficient matrix, Cointegration test and test of Normality, and they confirmed the validity and reliability of the model used; the inferential results showed that budget deficits impacted significantly on the economic growth of Nigeria under the review period. The paper recommended strongly that the country should display a high degree of transparency in its fiscal policies or operations by directing its fiscal deficits present towards investments that will increase productivity such as building roads, providing electricity and encouraging Foreign Direct Investments (FDI). The paper equally recommended inflation targeting in order to achieve a non-inflationary trend economy purposely to achieve the macroeconomic goal of price stability. The policy makers should direct capital and financial resources of government toward targeted programs like employment opportunity in productive ventures.

Distributed assembly shop scheduling problem for complex products considering multiskilled worker assignment and transportation time

The distributed assembly hybrid flow shop scheduling problem (DAHFSP) is a type of distributed shop scheduling problem, and each distributed shop can be regarded as a hybrid flow shop. In distributed assembly processes for complex products such as satellites and missiles, transportation time and worker assignment have important effects on production scheduling. Based on real production situations, this study proposes a novel DAHFSP considering worker assignment and transportation time, aiming to minimise the makespan and the imbalance degree of worker workloads. First, to solve these problems, we construct a mathematical model and design a two-layer chromosome coding scheme including worker assignment and task sequence. Then, in the local search stage, we propose a mutation-based search method and an elite search method. On that basis, we propose a multi-objective evolutionary algorithm with reinforced elite retention strategy (MOEA-RERS). Finally, based on a set of 12 test instances generated by actual enterprise production data, we compare the MOEA-RERS algorithm with five multi-objective evolutionary algorithms. The results show that the MOEA-RERS algorithm is superior to other algorithms in terms of solution quality and distribution.