Production Planning Research Articles

The impact of energy structure on agricultural green productivity in China

The agricultural development in China is currently grappling with the challenges of limited water and soil resources, environmental pollution, and ecosystem degradation. Therefore, improving agricultural green productivity has become the focus of academic attention. Based on the panel data of 30 provinces in China from 2008 to 2020, this paper constructs an index system of agricultural productivity. Subsequently, the super-efficiency SBM model is used to calculate the green productivity of agriculture, and its characteristics are analyzed from the perspectives of temporal and spatial.Through the empirical test of threshold model, it is found that China’s agricultural green productivity shows a gradual growth trend from 2008 to 2020, and the growth rate has accelerated significantly in recent years. There is a non-linear relationship between energy structure and agricultural green productivity. Especially in areas with reasonable energy structure, the increase in the proportion of coal consumption will inhibit the improvement of green agricultural productivity. This paper suggests reducing dependence on fossil energy, improving market mechanism and optimizing resource utilization in order to promote green agricultural production in China. This study expands the research perspective of agricultural green productivity, provides new research methods and ideas for related fields, and provides a reasonable energy allocation plan for agricultural production, which helps to improve production efficiency and reduce costs.

Numerical Simulation of Seepage in Shale Oil Reservoirs Under Hydraulic Fracturing: From Core-Scale Experiment to Reservoir-Scale Modeling

In this study, finite element software was used to simulate seepage at the core scale, the stress sensitivity of the shale core of the stripe layer and fractures was evaluated, and the production optimization design of reservoir C in block B of the oilfield under different fracturing parameters and wellbore parameters was simulated. The coupled finite element model of reservoir seepage stress was established; the pore elasticity model was used to determine the reservoir deformation; the seepage followed Forchheimer’s law and Darcy’s law; and finally, the liquid production was calculated to optimize the production plan. The results showed that the permeability under the same stress conditions increased nonlinearly with the increase in the striatal angle at the core scale, the permeability under the same effective stress conditions decreased gradually with the increase in the shale/fringe thickness ratio, and the elastic modulus and Poisson’s ratio of the proppant decreased. The permeability stress sensitivity was stronger. In the reservoir-scale model, the production pressure difference was the most significant factor affecting shale oil production, followed by the number of fractures and the length of the horizontal zone wellbore, and the elastic modulus of the proppant and Poisson’s ratio had the least impact on production.

The Convergence of SAP PP, RPA, and IBP: A Comprehensive Analysis of Integration Benefits and Challenges in Modern Manufacturing

The integration of SAP Production Planning (PP) with Robotic Process Automation (RPA) and SAP Integrated Business Planning (IBP) represents a significant advancement in manufacturing and supply chain management. This article examines the synergies, challenges, and potential benefits of combining these technologies in modern production environments. Through a comprehensive analysis of existing literature, case studies, and industry best practices, we propose a framework for seamless integration that enhances operational efficiency, improves forecast accuracy, and enables real-time decision-making. Our findings reveal that successfully implementing this integrated approach can substantially improve production planning, resource allocation, and overall supply chain performance. However, organizations face notable challenges regarding technical complexity, data synchronization, and change management. This article contributes to the growing body of knowledge on digital transformation in manufacturing by providing actionable insights and recommendations for practitioners. Furthermore, it identifies key areas for future research, including the role of artificial intelligence and machine learning in further optimizing these integrated systems.

Advanced photodetector for hybrid PET-MRI systems

Background Currently, a wide variety of silicon photomultipliers (SiPMs) are available, each designed for specific applications in fields such as science, medicine, and industry. Advances in production technology have led to the development of more sensitive and efficient photodiodes, which are critical for applications requiring precision, such as medical imaging. Methods A research group has been working on designing a highly sensitive photodiode to enhance the capabilities of next-generation PET-MRI scanners. This involves integrating micropixel avalanche photodiodes (MAPDs) to improve image resolution. The chosen design features deep-immersion MAPDs with a pixel size of 12 microns and a density of 1000 pixels per square millimeter, allowing for high-detail photon detection. The 4x4 mm2 active area is optimized to balance sensitivity and size for high-resolution medical imaging. To produce these photodiodes, the group has outlined a production plan involving 20-inch silicon wafers grown using multiple techniques to enhance material properties. The Malaysian Institute of Microelectronic Systems (MIMOS), renowned for its expertise in optical microelectronics, was selected as the production center. With MIMOS' state-of-the-art facilities, the project aims to meet stringent medical diagnostics standards. Results The experimental results demonstrated that the MAPD-3NM photodiode achieved an amplification factor 1.8 times greater than the MAPD-3NK under optimal conditions. Its overvoltage range increased by 100%, reaching 4 V, enhancing photon detection and amplification. The MAPD-3NM also showed a significant reduction in dark current, about 3.5 times lower than the MAPD-3NK, improving performance in low-light environments. Additionally, the MAPD-3NM had a capacitance of 200 pF compared to 176 pF for the MAPD-3NK, contributing to its superior performance. These improvements make the MAPD-3NM more efficient and sensitive for scientific and medical applications. Conclusions This project represents a major advancement in photodetector technology for medical diagnostics, aiming to develop more accurate and efficient PET-MRI scanners that enhance patient outcomes with improved imaging capabilities.

A reinforcement learning-based approach for solving multi-agent job shop scheduling problem

Wafer processing is the most expensive, time-consuming, and complex stage in semiconductor manufacturing. It varies significantly based on orders of customers (agents). Optimising the wafer processing flow in a multi-agent scenario can meet customised requirements, speed up delivery, and reduce costs. This work models wafer processing as a multi-agent job shop scheduling problem (MAJSP) with release dates. The objective is to minimise the total weighted makespan of agents. To address both dynamic and static scheduling scenarios in the MAJSP context, two deep reinforcement learning-based (DRL) methods are proposed. In a dynamic scheduling scenario, the statuses of orders and production resources can change at any moment. A DRL method called Graph Transformer Network (GTN) is proposed to rapidly generate high-quality solutions. In a static scheduling scenario, the production plan can be formulated based on predetermined demand and resource conditions. A novel hybrid method (GTN-DABC) that combines GTN with the discrete artificial bee colony algorithm (DABC) is proposed to provide high-quality production plans for manufacturers within an acceptable computation time. Experimental results demonstrate that the proposed GTN outperforms existing heuristics, and the well-designed GTN-DABC is more competitive than other meta-heuristics.

Accuracy and robustness of a plant-level cabbage yield prediction system generated by assimilating UAV-based remote sensing data into a crop simulation model

In-season crop growth and yield prediction at high spatial resolution are essential for informing decision-making for precise crop management, logistics and market planning in horticultural crop production. This research aimed to establish a plant-level cabbage yield prediction system by assimilating the leaf area index (LAI) estimated from UAV imagery and a segmentation model into a crop simulation model, the WOrld FOod STudies (WOFOST). The data assimilation approach was applied for one cultivar in five fields and for another cultivar in three fields to assess the yield prediction accuracy and robustness. The results showed that the root mean square error (RMSE) in the prediction of cabbage yield ranged from 1,314 to 2,532 kg ha–1 (15.8–30.9% of the relative RMSE). Parameter optimisation via data assimilation revealed that the reduction factor in the gross assimilation rate was consistently attributed to a primary yield-limiting factor. This research further explored the effect of reducing the number of LAI observations on the data assimilation performance. The RMSE of yield was only 107 kg ha–1 higher in the four LAI observations obtained from the early to mid-growing season than for the nine LAI observations over the entire growing season for cultivar ‘TCA 422’. These results highlighted the great possibility of assimilating UAV-derived LAI data into crop simulation models for plant-level cabbage yield prediction even with LAI observations only in the early and mid-growing seasons.

Analisis Peramalan Penjualan untuk Perencanaan Produksi pada Lavanda Brownies Karawang

This research was conducted at Lavanda Brownies Karawang which compared the moving average and exponential smoothing forecasting methods. This research aims to find out, calculate, analyze and explain how to use the moving average and exponential smoothing methods in sales forecasting for production planning at Lavanda Brownies Karawang as well as to find out, calculate, analyze and explain what forecasting method is most appropriate to use for Lavanda Brownies Karawang based on calculations of Mean Absolute Devitiation (MAD), Mean Square Error (MSE), and Mean Absolute Percent Error (MAPE). This research uses a quantitative descriptive method, to get a general picture of the company's condition, a literature review related to the research topic is carried out as well as field research through interviews and direct observation. Based on the research results, the exponential smoothing forecasting method based on alpha 0.9 is the best forecasting method for production planning. This method was chosen because it has the smallest MAD, MSE and MAPE accuracy values ​​when compared to other methods, namely with MAD of 489,909, MSE of 506.950 and a MAPE percentage of 14,14%, which percentage has the smallest value and is included in the category Good. A smaller MAPE accuracy value indicates more precise forecasting. Because in forecasting the smaller the accuracy value, the better the forecasting results so that the Lavanda Brownies business can plan optimal steamed brownie production activities in the next period.

Dynamic Integrated Scheduling of Production Equipment and Automated Guided Vehicles in a Flexible Job Shop Based on Deep Reinforcement Learning

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated guided vehicles (AGVs) in a flexible job shop environment is investigated in this study. Firstly, a static model for the integrated scheduling of production equipment and AGVs (ISPEA) is developed based on mixed-integer programming, which aims to optimize the maximum completion time and total production energy consumption (EC). In recent years, reinforcement learning, including deep reinforcement learning (DRL), has demonstrated significant advantages in handling workshop scheduling issues with sequential decision-making characteristics, which can fully utilize the vast quantity of historical data accumulated in the workshop and adjust production plans in a timely manner based on changes in production conditions and demand. Accordingly, a DRL-based approach is introduced to address the common production disturbances in emergency order insertions. Combined with the characteristics of the ISPEA problem and an event-driven strategy for handling dynamic events, four types of agents, namely workpiece selection, machine selection, AGV selection, and target selection agents, are set up, which refine workshop production status characteristics as observation inputs and generate rules for selecting workpieces, machines, AGVs, and targets. These agents are trained offline using the QMIX multi-agent reinforcement learning framework, and the trained agents are utilized to solve the dynamic ISPEA problem. Finally, the effectiveness of the proposed model and method is validated through a comparison of the solution performance with other typical optimization algorithms for various cases.

A novel data-driven rolling horizon production planning approach for the plastic industry under the uncertainty of demand and recycling rate

Efficient production planning in the plastic industry requires integrating sustainability practices, particularly the use of recycled plastics. Recycling helps reduce environmental impacts and conserve resources by decreasing the reliance on virgin materials. To develop a responsive plan, dynamic approaches are beneficial to confront fluctuations in uncertain parameters such as recycling rate and market demand. The present study proposes a novel Data-driven Rolling Horizon Planning (DRHP) approach for a sustainable and dynamic production plan in the plastic industry. A dynamic Rolling Horizon (RH) planning framework is formulated as a multi-product, multi-period Mixed Integer Linear Programming (MILP) model for the problem. This model aims to minimize total production cost while taking into account the system’s constraints and sustainability considerations. To deal with uncertainty, the RH-based MILP model is coupled with a rolling Long Short-Term Memory (LSTM) model. The rolling LSTM model leverages historical data of market demand and recycling rate to predict their future values and consequently improve the responsiveness of the production plan. The outperformance of the proposed DRHP approach is demonstrated through an extensive comparison with a robust static counterpart in terms of total production cost and sustainability performance. Results indicate significant reductions, up to 80%, in production costs using the proposed DRHP approach. Furthermore, the effect of rolling duration is investigated concerning production cost, backlog, late-order, and inventory level. Findings highlight the potential of the proposed DRHP approach to mitigate inventory- and late-order-related challenges.

Implementation and Evaluation of a Smart Machine Monitoring System under Industry 4.0 Concept

Production planning and control is essential in industrial manufacturing, ensuring efficient resource allocation and process management. Industry 4.0 introduces advanced technologies like cyber physical systems (CPS), artificial intelligence (AI), and internet of things (IoT) to effectively manage and monitor manufacturing operations. However, integrating these technologies into existing machinery, particularly for small and medium-sized enterprises (SMEs), poses challenges due to complexity and cost. The present study addresses this gap by designing and implementing a Smart Machine Monitoring System (SMMS) compatible with existing machinery such as computer numerical control and special purpose machines. The SMMS integrates IoT-based systems with AI algorithms to enhance machine tool utilization through effective planning, scheduling, and real-time monitoring. Through a nine-month case study in the shackle bolt manufacturing section, it was tested and compared to an Enterprise Resource Planning (ERP)-based system to assess its performance. Results showed significant improvements in production output, machine utilization rates, labor efficiency, and overall manufacturing costs. In conclusion, this study contributes to the body of knowledge on practical Industry 4.0 implementations for SMEs, offering insights into cost-effective solutions for enhancing operational efficiency and resource utilization in manufacturing environments.

The Last Planner System as an Emergent Production Planning and Control Method: The Role of Multiple Representations

The Last Planner System as an Emergent Production Planning and Control Method: The Role of Multiple Representations

DYNAMIC DEMAND FORECASTING IN SUPPLY CHAINSUSING HYBRID ARIMA-LSTM ARCHITECTURES

Demand forecasting in supply chains is essential for efficient inventory management, production planning, and cost optimization. Traditional time series models, such as Autoregressive Integrated Moving Average (ARIMA), are effective in modeling linear relationships but often fall short in handling the nonlinear complexities characteristic of volatile demand patterns. Conversely, deep learning models like Long Short-Term Memory (LSTM) networks have proven adept at capturing intricate temporal dependencies, though they are computationally intensive and require substantial data. This study introduces a hybrid ARIMA-LSTM framework that leverages the linearity strengths of ARIMA in conjunction with LSTMs capacity for learning non-stationary, nonlinear patterns. The hybrid model decomposes the demand series, modeling linear trends with ARIMA and residual nonlinear components with LSTM. Empirical evaluations of real-world supply chain data reveal that this integrated architecture outperforms standalone ARIMA and LSTM models, achieving superior predictive accuracy and robustness in handling demand variability. Our findings underscore the hybrid models potential as an advanced predictive solution for dynamic, data-driven supply chain management.

Predicting Work-in-Process in Semiconductor Packaging Using Neural Networks: Technical Evaluation and Future Applications

This review paper focuses on the application of neural networks in semiconductor packaging, particularly examining how the Back Propagation Neural Network (BPNN) model predicts the work-in-process (WIP) arrival rates at various stages of semiconductor packaging processes. Our study demonstrates that BPNN models effectively forecast WIP quantities at each processing step, aiding production planners in optimizing machine allocation and thus reducing product manufacturing cycles. This paper further explores the potential applications of neural networks in enhancing production efficiency, forecasting capabilities, and process optimization within the semiconductor industry. We discuss the integration of real-time data from manufacturing systems with neural network models to enable more accurate and dynamic production planning. Looking ahead, this paper outlines prospective advancements in neural network applications for semiconductor packaging, emphasizing their role in addressing the challenges of rapidly changing market demands and technological innovations. This review not only underscores the practical implementations of neural networks but also highlights future directions for leveraging these technologies to maintain competitiveness in the fast-evolving semiconductor industry.

Analysis of Micro Business Marketing Strategy in Facing Competition

This study is motivated by the challenges faced in the development of micro-enterprises at Bukit Sion Market, largely due to the high level of competition. Many businesses struggle to determine appropriate financing models and product offerings, as the market is saturated with similar products offered by competitors. The objective of this research is to analyze the marketing strategies employed by micro-enterprises and identify the obstacles they face in dealing with competition at Bukit Sion Market, Lahomi District. The research employs a descriptive qualitative method. The findings reveal that all food stalls adopt marketing strategies that include market selection, product planning, pricing, distribution systems, and marketing communication. Effective marketing strategies, such as offering discounts, special promotions, or hosting promotional events, have been shown to significantly boost sales. However, the food stalls encounter several challenges, including intensified competition due to the emergence of similar businesses offering comparable products, a lack of product innovation and promotion, fluctuations in raw material prices, as well as issues with product quality consistency, customer service, and overall customer experience.

A production-inventory model to optimize the operation of distributed energy resource networks in a rolling horizon

The recent advancements in energy production, storage, and distribution are creating unprecedented opportunities in the field. Major consumers can benefit from the implementation of distributed energy resource networks capable of generating electricity or heating from sources, often renewable ones, in close proximity to the point of use, rather than relying on centralized generation sources from power plants. In this paper, we introduce a pioneering model designed to determine the optimal set of energy commands in a distributed energy resource network, minimizing operational costs in a time horizon. Indeed, we propose an innovative mixed-integer linear programming formulation rooted in the production-inventory models commonly employed in aggregate production planning. The system integrates diverse energy generation sources, storage facilities, and demand points, encompassing both electric and heating commodities. The optimum of the model is achieved for all analyzed instances of the test library (2 scenarios-20 instances) in an exceptionally short time, outperforming other approaches previously presented in the literature. We employed the Gurobi optimizer to solve the model, obtaining rapid responses that ensure real-time decision-making and facilitate effective control of the distributed energy resource network within a three-days' rolling horizon, as discussed in a simulated real-life application case study. Indeed, the proposed model solves in less than 1s, enabling near-instantaneous decision-making. This swift solution time surpasses any known references in the field, effectively shifting the bottleneck in DER network operation from the decision-making process to the forecasting of demand and weather conditions. While forecasting typically requires a minimum of 15min, our approach suggests that a reduction in this forecasting time could further enhance the control system's response time, given the model's ability to deliver optimal solutions almost immediately. The real-time availability of optimal solutions allows for the seamless incorporation of stochastic elements into the control loop via a rolling horizon process.

Multi-criteria decision-making model for sustainability functions integrated Industry 4.0 technologies within small and medium enterprises in emerging countries

PurposeIn the Industry 4.0 (I4.0) era, small and medium enterprises (SMEs) face significant pressure to make their manufacturing operations more sustainable. However, SMEs often lack the knowledge and information needed to leverage I4.0 technologies for achieving sustainability. This paper aims to develop a multi-criteria decision-making (MCDM) model to prioritize sustainability functions supported by I4.0 technologies in SMEs.Design/methodology/approachThe fuzzy-DEMATEL method was developed to classify sustainability functions based on their prominence and influence, categorizing them into cause-and-effect groups. Experts in I4.0 technologies, sustainability and SMEs from Brazil were consulted during the data collection phase. Sensitivity analysis was also performed to validate the results obtained through the DEMATEL method.FindingsThe results indicate that the most prominent and influential sustainability functions include organizing production processes, employee skill development and simplifying production planning and control. Therefore, investing in I4.0 technologies to support these functions can enable SMEs to amplify the impact of their sustainability initiatives and improve operational efficiency. The authors also observed that improving quality monitoring is the primary function within the effect group.Practical implicationsThis paper serves as a roadmap for SME managers seeking to enhance the effectiveness of their sustainability practices using I4.0 technologies, thereby maximizing impact while minimizing the complexity of implementation.Originality/valueThis study ranks key sustainability functions as actions and practices that SMEs can implement using I4.0 technologies to achieve sustainability. It also identifies the most prominent and influential functions managers should prioritize when investing in I4.0 technologies.

Towards the Implementation and Integration of a Digital Twin in a Discrete Manufacturing Context

In the context of enhanced decision making related to Industry 4.0 and 5.0, this work examines the first step toward the implementation of a Digital Twin (DT) in a discrete manufacturing firm. It will be required that the DT be adequately integrated with the information systems, especially the Manufacturing Execution System (MES), because the virtual counterpart of the DT itself, a Discrete Event Simulator (DES) model, will exploit the MES data for the validation and monitoring. The objective of the DT is to enhance the decision making related to production planning in particular, achieving better on-time delivery to customers. Therefore, the DT intends to depict material flows within the production department to enhance the monitoring and control, facilitating the prompt identification of deviations from the plan and supporting the decision-makers, enabling a more responsive and informed management of delay alerts. The first goal to achieve the DT implementation and integration is to establish a conceptual framework that improves material flow data synchronization. A conceptual integration and implementation framework for the DT will be proposed and discussed, underlying the technical decisions chosen to achieve the functional and integration requirements.

Strategical and tactical supply chain optimisation for smart production planning and control 4.0

Industry 4.0 (I4.0) technologies generate new opportunities for developing smart models, algorithms and tools to support supply chain (SC) production planning and control (PPC), or smart PPC (SPPC 4.0). Paired with opportunities, challenges arise in integrating sustainability and resilience in SC network design (SCND) according under I4.0. The main novelty of this paper is to propose two multi-objective models that integrate strategical and tactical PPC decisions into a sustainable-resilient SC under I4.0 towards SPPC 4.0. Sustainability is incorporated in terms of reducing costs and CO2 emissions, and a job creation factor. Resilience is incorporated in terms of contracting support suppliers. To solve the multi-objective models, the augmented epsilon constraint method (AUGMECON) was used. This algorithm minimises a target objective function while it treats the others as constraints. Our experiments indicate that AUGMECON is sensitive to the choice of epsilon values. Furthermore, a synthetic data generation tool called SR1-SR2_SynthDataGenerator was developed. This tool generates input datasets to validate and evaluate our models against benchmarks. A stochastic model (SR2) is also proposed that, with small datasets, takes 8.39 seconds to solve. The proposed models can be useful for industries that seek sustainable and resilient SCs to adapt to changing environments.

Optimizing production planning and sequencing in hot strip mills: an approach using multi-objective genetic algorithms

Optimizing production planning and sequencing in hot strip mills: an approach using multi-objective genetic algorithms

Evaluating Maize Residue Cover Using Machine Learning and Remote Sensing in the Meadow Soil Region of Northeast China

The management of crop residues in farmland is crucial for increasing soil organic matter and reducing soil erosion. Identifying the regional extent of crop residue cover (CRC) is vital for implementing conservation tillage and formulating agricultural subsidy policies. The Google Earth Engine (GEE) and remote sensing images from 2019 to 2023 were used to obtain spectral characteristics before the maize seedling stage in Northeast China, followed by constructing the CRC estimation models using machine learning algorithms. To avoid the impact of multicollinearity among data, three machine learning algorithms—ridge regression (RR), partial least squares regression (PLSR), and least absolute shrinkage and selection operator (LASSO)—were employed. By comparing the accuracy of these methods, the most accurate model was determined and applied to subsequent CRC estimation. Based on the estimated CRC and Conservation Technology Information Center definitions of tillage practices, the conservation tillage mapping was completed, and the spatiotemporal distribution characteristics were thoroughly analyzed. The following findings were demonstrated: (1) the PLSR-based model outperformed RR (Pearson’s correlation coefficient (r) = 0.8875, R2 = 0.7877, RMSE = 6.99%) and LASSO (r = 0.8903, R2 = 0.7926, RMSE = 6.88%) with higher accuracy (r = 0.9264, R2 = 0.8582, RMSE = 4.93%). (2) Over the five years, the average no-tillage (NT) proportion in the study area was 15.9%, reduced tillage (RT) was 17.8%, and conventional tillage (CT) was 66.3%. In 2020 and 2022, NT rates were significantly higher at 27.5% and 15.5%, while RT were 15.7% and 30.0%, respectively. (3) Compared to the Sanjiang and Liaohe Plains (RT = 1907 km2 and 1336 km2, and NT = 559 km2 and 585 km2, respectively), the Songnen Plain exhibited higher conservation tillage rates (where RT was 3791 km2 and NT was 1265 km2). This provides crucial scientific evidence for the management and planning of conservation tillage, thereby optimizing farmland production planning, enhancing production efficiency, and promoting the development of sustainable agricultural production systems.