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Production and Operation Management for Intelligent Manufacturing: Challenges, Scientific Issues, Key Research, and Latest Development JIANG Zhibin, ZHOU Liping Industrial Engineering Journal    2024, 27 (1): 1-9.   DOI: 10.3969/j.issn.1007-7375.230254 Abstract （1239）   HTML （391）    PDF（pc） （672KB）（1270）       Knowledge map    This paper briefly introduces management characteristics of interconnection, integration, services, customization, and time-varying in intelligent manufacturing. The challenges these characteristics pose to production and operation management is analyzed, including multi-dimensional integration for digital supply networks, flexible and networked production, self-organizing optimization of manufacturing resources, decentralized autonomous decision-making and collaborative control, learning operation management, powerful self optimization and adaptability, forward-looking decision-making, etc. Subsequently, three aspects are identified as scientific issues that need to be addressed: value creation mechanism, resource organization and reconfiguration mechanism, and production planning and scheduling systems. Additionally, four key research directions are proposed: integration mechanism, resource reconfiguration methods, production planning and scheduling methods, and logistics operation and management methods. Finally, this paper highlights latest development in intelligent manufacturing production and operation management, providing new ideas for production management theories and practice. Reference | Related Articles | Metrics

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Innovative Applications of Industrial Engineering Theory and Methods in Healthcare Management: A Review in the Data and Intelligence Era LUO Li, LIAO Huchang, XIANG Jie, FANG Yuanchen Industrial Engineering Journal    2024, 27 (1): 10-24.   DOI: 10.3969/j.issn.1007-7375.240007 Abstract （1300）   HTML （245）    PDF（pc） （953KB）（828）       Knowledge map    The application of industrial engineering theory and methods in healthcare management runs through various aspects, including hospital facility planning, medical process optimization, medical resource management and disease diagnosis, providing strong support for the improvement of medical services and efficiency. With the development of new generation information technologies such as the internet of things, cloud computing, big data and artificial intelligence, the application of industrial engineering theory and methods in healthcare management has undergone significant transformations. Based on four hot topics of literature from 2014 to 2023 in the field of healthcare management, i.e., full life cycle health management, medical resource scheduling and optimization, hospital operation management, and medical logistics and supply chain management, this paper reviews the application of key industrial engineering technologies in these topics. Then, we summarize and analyze the innovation of new generation information technology application in the field of healthcare management, clarifying the technical and management empowerment of industrial engineering theory and methods in this field and the era of data and intelligence. Finally, we envision the application prospects of industrial engineering theory and methods in the informationization, refinement and intelligence of healthcare management in this new era. Reference | Related Articles | Metrics

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A Review on Planning and Control of Connected Autonomous Truck Platooning FU Hui, JIN Chengqian, NIU Zhangzhe, ZENG Weiliang Industrial Engineering Journal    2024, 27 (1): 25-35.   DOI: 10.3969/j.issn.1007-7375.230246 Abstract （1102）   HTML （324）    PDF（pc） （887KB）（743）       Knowledge map    Truck platooning organized by the logistics alliance may become a new form of future logistics transportation for reducing operating cost. The key technologies and related research progress on planning and control of connected autonomous truck (CAT) platooning is analyzed in this paper through literature search. From the perspective of commercial application, the challenges of currently implementing CAT platooning are discussed, also the cost allocation issues among relevant stakeholders are analyzed. From the perspective of technologies, the corresponding fundamentals and methods are interpreted considering planning and control of CAT platooning. By reviewing the existing studies of CAT platooning, the future research interests are concluded. The motivation of this paper is to provide a possible reference for researchers to understand the trends on CAT platooning. Reference | Related Articles | Metrics

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A Review of Trustworthy Machine Learning CHEN Caihua, SHE Chengxi, WANG Qingyang Industrial Engineering Journal    2024, 27 (2): 14-26.   DOI: 10.3969/j.issn.1007-7375.230241 Abstract （468）   HTML （45）    PDF（pc） （937KB）（626）       Knowledge map    Machine learning technology is continuously evolving and is extensively applied across various domains, demonstrating capabilities beyond human abilities. However, improper use of machine learning methods or biased decision-making can harm human interests, especially in sensitive areas with high-security demand such as finance and healthcare, etc., leading to an increasing attention on the trustworthiness of machine learning. Currently, machine learning technology commonly exhibits several drawbacks, such as biases against underrepresented groups, lack of user privacy protection, lack of model interpretability, and vulnerability to threats and attacks. These shortcomings undermine human trust in machine learning methods. Although researchers have conducted targeted studies on these issues, there is a lack of a comprehensive framework and methodology to systematically provide trustworthy analysis of machine learning. Therefore, this paper reviews the current mainstream definitions, indicators, methods, and evaluations of fairness, interpretability, robustness, and privacy in machine learning. Then, the relationships among these elements are discussed, while a trustworthy machine learning framework is established by integrating an entire lifecycle of machine learning. Finally, we present some of the current issues and challenges awaiting resolution in the field of trustworthy machine learning. Reference | Related Articles | Metrics

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A Review on Surface Defect Detection Based on Deep Intelligent Vision GAO Yiping, WANG Hao, LI Xinyu, GAO Liang Industrial Engineering Journal    2024, 27 (2): 27-36,66.   DOI: 10.3969/j.issn.1007-7375.230233 Abstract （559）   HTML （54）    PDF（pc） （1036KB）（493）       Knowledge map    The exploration on surface defect detection based on deep intelligent vision plays an increasingly important role in the manufacturing industry. The importance of surface defect detection based on deep intelligent vision in modern industrial quality inspection is explained and the existing research progress is summarized in this paper. Deep intelligent vision provides high-precision and high-efficiency surface defect detection algorithms for different industrial scenarios based on the technologies of machine vision and deep learning. Surface defect detection can be divided into three categories: surface defect classification, localization, and segmentation from the perspective of detection fineness. The classification, localization, and segmentation methods are systematically reviewed, respectively, to sort out the problematic points and lines of the existing surface defect detection methods. Surface defect classification focuses on the problem of data and defective graphical features, which shows decentralized development due to its basic and easily expandable nature for application in different industrial scenarios. Surface defect localization takes the model framework, rectangular box detection mechanism, and annotation cost as the main problems, showing a research trend of pursuing lightweight and feature fusion mechanisms. Surface defect segmentation pays more attention to detailed features of an image. A multi-task framework for classification, localization, and segmentation, is studied to explore the complementarity between classification and segmentation detection. Finally, the current issues of existing surface defect detection studies are concluded and an outlook on the development trend is given. Reference | Related Articles | Metrics

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Operational Management in the Context of Industrial Internet of Things: A Review WANG Kangzhou, WANG Dongdong, DOU Lei, XUE Lin Industrial Engineering Journal    2024, 27 (2): 1-13.   DOI: 10.3969/j.issn.1007-7375.230247 Abstract （622）   HTML （81）    PDF（pc） （707KB）（466）       Knowledge map    The Industrial Internet of Things (IIoT), as a new industrial ecosystem integrating advanced information technologies with manufacturing, holds significant importance for enhancing operational efficiency and promoting high-quality development in the manufacturing sector. A comprehensive analysis of literature related to operational management in the IIoT context reveals several key findings. 1) Research on IIoT platforms often employs qualitative methods to analyze their application scenarios and ecosystems across various industries. However, it is essential to focus on quantitative methods to explore operational and coordination mechanisms in depth. 2) Existing studies on value creation suggest enhancing product quality, reducing costs, and optimizing business processes through digitalization, networking, and intelligence. Thereby, it highlights the importance of understanding value co-creation mechanisms and patterns among multiple stakeholders, including manufacturing enterprises and customers. 3) Current research primarily concentrates on optimizing individual activities in production operations using IIoT technologies, mathematical models and algorithms. Nevertheless, there exists a critical need to investigate methods for constructing integrated collaborative processes with the cycle of “research, manufacturing and maintenance” within the IIoT environment, along with exploring multi-level closed-loop decision-making systems and intelligent decision-making methods. 4) It is demonstrated that supply chain management in IIoT can improve operational performance through interconnection, visibility, real-time capabilities, and traceability, while the impact of technology adoption on strategy selection and coordination mechanisms is also explored. Future attention may shift towards downstream clients in supply chain management. Reference | Related Articles | Metrics

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A Study on Digital Derivation Mechanisms and Maturity of Digital Twins in Manufacturing Firms WANG Feng, GAI Yongjie, ZHANG Haitao Industrial Engineering Journal    2024, 27 (2): 158-172.   DOI: 10.3969/j.issn.1007-7375.230153 Abstract （1029）   HTML （35）    PDF（pc） （1420KB）（276）       Knowledge map    This study explores the basic management architecture (composition, connotation, components, functions, etc.) of digital twins regarding "production power, computational power, digital power" in manufacturing enterprises, and analyzes the integrated process framework and big data function model based on the endogenous 4.0 value chain of digital twins. The 4.0 value chain has a value transfer and iteration mechanisms, which empowers manufacturing enterprises to digitize their supply chains and expand the industrial chain derivation. Manufacturing enterprises generate big data through digital twins, where a digital industrial chain is formed (4.0 value chains, digitized supply chains, mobile value-added service MvaS chains, demand chains, and spatial platform chains). This study innovatively analyzes a digital growth path (digital twins →4.0 value chains → supply chains → digital industry chains → digital economy) through the digital twin derivation mechanism of manufacturing enterprises. Also, a five-level digital twin maturity model and an evaluation algorithm for manufacturing companies is comprehensively designed and condensed. Digital twins reshape the theory paradigm of digital management, and tamp the digital era foundation (smart manufacturing, digital factories, digital enterprises, digital industry chains, digital economy, etc.), having a far-reaching impact on the development of the digital economy, with important research and application value. Reference | Related Articles | Metrics

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Order Dispatching and Routing for Decentralized Joint Distribution Considering Empty-loading Losses ZHANG Meng, SUN Lulu, SU Bing, WANG Nengmin Industrial Engineering Journal    2024, 27 (2): 107-118,137.   DOI: 10.3969/j.issn.1007-7375.230228 Abstract （227）   HTML （31）    PDF（pc） （1624KB）（124）       Knowledge map    The high empty-loading rate in logistics activities may result from unreasonable routing and insufficient cooperation among enterprises. Joint distribution is an effective mode to reduce empty-loading losses. However, under the condition of decentralized joint distribution, logistics enterprises may choose the routes with minimum cost based on the assigned orders, resulting in an increase in empty-loading losses of the joint distribution alliance. This study investigates order dispatching and routing for decentralized joint distribution considering empty-loading losses. A definition of empty-loading losses is given firstly. Then, with the trade-off between the objectives of minimizing cost and empty-loading losses in the entire distribution process, an order dispatching strategy is proposed based on the characteristics of decentralized joint distribution mode, and the order dispatching and routing optimization models are developed. A precise algorithm based on epsilon constraint method, an improved MOPSO (Multiple Objective Particle Swarm Optimization) algorithm, and a polynomial-time fast algorithm are developed to solve the problem. The effectiveness of the proposed algorithms is verified based on numerical instances. Analysis results indicate that even if logistics companies pursue cost minimization, the proposed order dispatching strategy can achieve results similar to global optimization. Reference | Related Articles | Metrics

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A Joint Model of Revenue Management and Aircraft Assignment Considering Passenger Choice Behavior LE Meilong, CHEN Yi, HUANG Zhouchun Industrial Engineering Journal    2024, 27 (5): 1-10.   DOI: 10.3969/j.issn.1007-7375.230171 Abstract （18）      PDF（pc） （664KB）（12）       Knowledge map    The assumption of traditional revenue management on passenger demand is independent and fails to fully account for passenger choice behavior. This paper discusses the revenue management and aircraft assignment problems considering passenger choice behavior. Observations from historical sales data reveal that passengers have a special preference for the lowest fare class. Therefore, the Spiked Multinomial Logit (Spiked -MNL) choice model is used in this paper to predict the potential real demand of passengers. Also, a joint model of revenue management and aircraft assignment considering passenger choice behavior is established, with the objective of maximizing airline profit. Case studies are conducted using actual sales transaction data from an airline to verify the feasibility of the proposed model and to evaluate its performance by comparison in different scenarios. Results show that the joint model yields an average profit of 5% higher than the independent model. In the comparison of different fleet sizes, the 5-aircraft joint model produces the highest profit, with an average profit of 6.6% higher than the 3-aircraft model and 7.3% higher than the 9-aircraft model. In addition, compared with traditional methods of predicting passenger demand using passenger choice models, the Spiked-MNL model can reflect actual passenger purchasing behavior more accurately, with an average profit of 0.7% higher than the Generalized Attraction Model (GAM) and 1.2% higher than the Multinomial Logit (MNL) model. Reference | Related Articles | Metrics

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Dynamic Pricing Strategy for Microgrid Power Trading with Multiple Types of Prosumers Based on Stackelberg Game LIU Jiwen, HOU Qiang, YAN Pengyu Industrial Engineering Journal    2024, 27 (5): 138-149.   DOI: 10.3969/j.issn.1007-7375.230239 Abstract （16）      PDF（pc） （990KB）（10）       Knowledge map    In order to realize the complementary operation of "wind, photovoltaic and energy storage" sources in microgrid systems, and simultaneously to inspire the three types of energy prosumers to actively participate in power trading, this paper establishes a Stackelberg game model between the microgrid scheduling center and the three types of prosumers. In the model, as the leader, the microgrid scheduling center determines the purchase and sale prices for each time period, As followers, the prosumers send their respective electricity demand to the scheduling center according to these prices. Then, the scheduling center adjusts the purchase and sale prices based on the received electricity demand, ultimately achieving a game equilibrium. This paper extends the power trading models in the existing literature primarily focused on unilateral decision-making in microgrids, and proves the existence and uniqueness of the equilibrium solution of the Stackelberg game model in the situation that each game participate makes independent rational decision. This paper also develops an equilibrium solution algorithm based on differential evolution, and verifies the effectiveness of the model and algorithm through numerical experiments. The results of this paper provide a scientific basis for setting the purchase and sale prices of electricity by microgrid scheduling centers, which promotes the efficient utilization of distributed renewable energy. Reference | Related Articles | Metrics

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Research on Measuring and Predicting the Carbon Footprint of Supply Chain Throughout its Life Cycle Based on LSTM YU Chunhua, SHE Chengxi, LI Jinxia, CHEN Qi, WEN Fuguo, WU Yinan Industrial Engineering Journal    2024, 27 (5): 161-171.   DOI: 10.3969/j.issn.1007-7375.240218 Abstract （12）      PDF（pc） （660KB）（7）       Knowledge map    Carbon footprint measurement and estimation are important indicators for low-carbon supply chain assessment, but currently there is a lack of unified carbon footprint measurement standards and boundaries, and traditional carbon footprint measurement methods require significant computational costs. Therefore, a two-stage full lifecycle carbon footprint estimation method that calculates first and predicts later has been proposed. In the first stage, the power grid material supply chain is divided into five stages, and corresponding calculation models are constructed to achieve quantitative description and evaluation of carbon footprint; In the second stage, a supply chain lifecycle carbon emission prediction model based on long short-term memory neural network (LSTM) was constructed using cable products as carbon sources. Finally, numerical experiments were conducted based on carbon footprint management data of the power grid supply chain from 2020 to 2023, with a prediction accuracy of 99.3%. By comparing the models constructed with BP neural network and GABP neural network, the accuracy and superiority of the models have been demonstrated, achieving effective accounting and prediction of carbon footprint. Reference | Related Articles | Metrics

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Optimization of Service Resource Allocation for Airport Security Checkpoints Considering Non-stationary Random Processes ZHANG Huiyu, YAN Lizhou, CHEN Qingxin, MAO Ning Industrial Engineering Journal    2024, 27 (5): 11-22.   DOI: 10.3969/j.issn.1007-7375.230017 Abstract （14）      PDF（pc） （1483KB）（7）       Knowledge map    The non-stationarity and randomness of airport passenger arrivals result in uncertain and highly time-varying requirements for security checkpoints, increasing the difficulty of resource allocation. To address this issue, this paper proposes a method embedding a non-stationary queuing model into a genetic algorithm. Initially, a non-stationary queuing model for a single-stage service system with multiple parallel service desks is established, where the input and service processes are fitted to general distributions. Furthermore, an extended stationary backlog-carryover (SBC) approximation is proposed to quickly solve system performance metrics. Then, the extended SBC approximation is embedded into the genetic algorithm for optimization. Finally, experimental and optimization examples are designed. The accuracy of the extended SBC approximation and the effectiveness of the optimization method are verified by comparing the analytical results with the simulation results. The impact of non-stationarity on system performance and optimization results is also analyzed. Results of applying this method to a practical case show that the optimized allocation scheme reduces the number of security checkpoints by 14.80% compared to the actual allocation scheme, proving its effectiveness. Reference | Related Articles | Metrics

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An Ergonomics Model and Posture Improvement for Grinding Workers Based on an Improved DMAIC Approach GUO Chundong, LI Yunhao, YAN Shuo, SHEN Lin Industrial Engineering Journal    2024, 27 (5): 53-63.   DOI: 10.3969/j.issn.1007-7375.230195 Abstract （14）      PDF（pc） （2111KB）（6）       Knowledge map    In order to improve the working posture of grinding workers and reduce the risk of developing work-related musculoskeletal disorders (WMSDs), an experimental study is carried out on the working postures of grinding workers in the pressure vessel workshop of a pharmaceutical equipment company. Based on the DMAIC model, a DMASIEC work improvement model is proposed which adds simulation (S) verification analysis in the improvement (I) stage and execution (E) analysis in the control (C) stage. Using the Xsens MVN motion capture system and the Jack TAT tool in a synchronized joint analysis method, an immersive real-world experiment for job posture improvement based on physiological signals and subjective evaluation indicators is designed and carried out. Through the simulation verification of grinding postures using Jack, a guideline for improving grinding worktable and operations is proposed to reduce work fatigue. Results show that the pressure on the L4/L5 spines of grinding workers is about 1/4 of the original pressure when performing the same task, and all working posture ratings are AC1; significant changes ( P ＜ 0.05) in sEMG and HRV fatigue indicators are observed in three workers after transitioning from a squatting to a sitting posture; the improved worktable and operational guidance contribute to mitigating the adverse effects of working posture on the musculoskeletal system. Reference | Related Articles | Metrics

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Joint Optimization of Multi-period Inventory and Transportion for Coal Considering Business Conflicts LIU Zhijiang, DU Gang, ZHANG Hailong, XU Lufei, BAI Zhijun, GENG Hua Industrial Engineering Journal    2024, 27 (5): 43-52.   DOI: 10.3969/j.issn.1007-7375.230117 Abstract （16）      PDF（pc） （794KB）（5）       Knowledge map    This study delves into the comprehensive optimization challenge of integrating production, operation, sales, and storage of the industrial chain when large-scale energy enterprises formulate their operational plans. To address the limitations of existing models, which typically cover only a single time period, ignore business conflicts, and treat transportation and inventory separately, this paper develops a multi-period inventory-transportation joint optimization model. The objective is to maximize the total profit and minimize the impact of business conflicts within the enterprise planning horizon. This model considers constraints such as inventory balance across different time periods and production-operation-sales activities, while business conflicts are also taken into account. Given the complexity of the model structure, numerous constraints, and substantial computation cost, this paper introduces a fast genetic algorithm with progressive enhancement of soft constraints. Compared to traditional genetic algorithms, this algorithm can accelerate computation by 5-10 times, significantly improving the efficiency of solving such models. Finally, the model and algorithm are applied to practical cases, while detailed analysis of these cases is conducted, verifying the reliability of the model and the efficiency of the algorithm. The model and algorithm proposed in this paper contribute to providing scientific, rational, efficient, and precise decision support for the optimal operations of the integrated industrial chain in large-scale energy enterprises. Reference | Related Articles | Metrics

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A Channel Operation Strategy for Manufacturers with Two Iterations of Products Considering Consumer Showroom Behaviour LI Hao, LIN Jiaxin Industrial Engineering Journal    2024, 27 (5): 103-113.   DOI: 10.3969/j.issn.1007-7375.230019 Abstract （14）      PDF（pc） （761KB）（5）       Knowledge map    This paper investigates the optimal channel operation strategy of manufacturers in the product sales market where two iterations of products coexist. Combined with the retail attributes of B2C e-retailers and the physical characteristics of manufacturer offline direct sales channels, considering consumer showroom behavior towards new products, game models are established from the perspective of manufacturers under three modes: distributing new products and directly selling old products, distributing new products and directly selling both iterations of products, and distributing the two iterations of products. The optimal pricing decisions for two iterations of products under the three modes are analyzed, while the optimal sales mode and product launch strategy of manufacturers are discussed on this basis. Results show that when consumers have low access cost to offline direct sales channels, manufacturers should choose to sell both iterations of products through offline direct sales channels; when the cost of accessing offline direct sales channels is moderate, the optimal decision for manufacturers is to distribute new products online and sell old products offline to avoid the competition effect between two iterations of products; otherwise, manufacturers should choose to distribute new products online and not sell products through direct sales channels, simultaneously, they should play the showroom function of offline channels to obtain incremental benefits. Moreover, consumer showroom behavior can bring incremental demand for new product sales in B2C e-commerce, but it further intensifies the competition for new product demand between online and offline channels. Reference | Related Articles | Metrics

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A Decision Model for Order-Price Competition and Cooperation Considering Procurement with Supply Constraints JIA Tao, LI Yuanyuan, WANG Yuqiang, LIN Feng Industrial Engineering Journal    2024, 27 (5): 81-91.   DOI: 10.3969/j.issn.1007-7375.230210 Abstract （23）      PDF（pc） （1004KB）（4）       Knowledge map    In the context of upstream supply constraints, in order to ensure the effective operation of the supply chain, an decision model (Strategy 1) involving ordering and pricing decisions between the manufacturer and the brand owner is established in a supply chain where the brand owner adopts a "control" procurement mode, by introducing a supplier with capacity constraints and economies of scale. Furthermore, based on the classic competition-cooperation model, a game model (Strategy 2) is developed for the manufacturer to open direct sales channels. Through theoretical analysis of the game model, the equilibrium results in the supply network are obtained. Combining analytical and numerical analysis, the effects of competition-cooperation games and manufacturer strategic preferences are examined in two scenarios: the extreme high price procurement (for components) and the free-riding procurement. Different from the existing models, this study incorporates the component procurement processes of both manufacturers and brand owners, resulting in a circular supply structure and revealing the effect of exchanging orders through channels. The conclusions of this study can provide suggestions for the transformation and upgrading of manufacturers under supply constraints. Reference | Related Articles | Metrics

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A Value Co-creation Strategy in Closed-loop Supply Chains Considering Customer Engagement and Shareholding Cooperation GAO Juhong, QIU Xiaowen Industrial Engineering Journal    2024, 27 (5): 92-102.   DOI: 10.3969/j.issn.1007-7375.230196 Abstract （10）      PDF（pc） （1288KB）（4）       Knowledge map    How to develop a closed-loop supply chain for the recycling of waste products and achieve value co-creation is the key issue for the smart phone supply chain to promote circular economy and improve competitiveness. This paper introduces shareholding cooperation and customer engagement, analyzing value co-creation in closed-loop supply chains from multiple perspectives. It explores the effects of shareholding ratio, commission rate and customer engagement on the decisions of supply chain members, the recycling amount of waste products, the profit of supply chain members, the customer surplus, and the total value of the closed-loop supply chain. Results show that shareholding cooperation is beneficial to improving the service level of recycling platforms. When customer engagement with a recycling platform is high or the cost coefficient of recycling service level is low, shareholding cooperation can promote the recycling of waste products. Shareholding by manufacturers in recycling platforms is a profit strategy for recycling platforms; however, in the long term, manufacturers and recycling platforms should set a low shareholding ratio and a moderate commission rate when customer response is high. In this way, the profit of both parties increases, leading to stable shareholding cooperation and promoting the realization of value co-creation in closed-loop supply chains. Reference | Related Articles | Metrics

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Process Action Recognition and Analysis Based on Skeleton Sequences ZHANG Zhicong, CAI Yuchen, ZHANG Liangwei Industrial Engineering Journal    2024, 27 (5): 73-80.   DOI: 10.3969/j.issn.1007-7375.230240 Abstract （10）      PDF（pc） （4331KB）（3）       Knowledge map    To address the time-consuming, labor-intensive, and experience-dependent issues of traditional process action analysis methods in the field of industrial engineering, this paper proposes an intelligent detection method for process actions based on skeleton sequences using action recognition technology to replace traditional manual decomposition methods. A human body posture estimation model is built using a 2D camera and the MediaPipe framework to obtain skeleton sequences, and relevant evaluation metrics are introduced for action quantitative analysis. Also, a convolutional gated recurrent unit (CNN-GRU) based action classification model is trained using skeleton data. Experiments are conducted on a self-built process action dataset, demonstrating that the proposed CNN-GRU model achieves higher accuracy with fewer parameters compared with LSTM and GRU models. Furthermore, by comparing the inference results with standard operating procedures, abnormal actions are identified, providing an effective solution for process action recognition and analysis, which helps to standardize production operations and improve production efficiency. Reference | Related Articles | Metrics

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Scheduling of Home Healthcare Workers Carrying Medical Supplies with Mixed Time Windows LI Yanfeng, WANG Hairui Industrial Engineering Journal    2024, 27 (5): 23-32.   DOI: 10.3969/j.issn.1007-7375.240038 Abstract （14）      PDF（pc） （822KB）（3）       Knowledge map    The study focuses on the scheduling issue of home healthcare workers, incorporating constraints related to mixed time windows and medical supplies into the model. A branch-and-price algorithm is designed for solving the problem, with the branch process and labeling algorithm are improved within the algorithm. In the numerical experiments, the proposed branch-and-price algorithm is compared with the Adaptive Large Neighborhood Search (ALNS) algorithm and CPLEX in terms of performance. The branch-and-price algorithm finds optimal solutions in over 80% of the cases, whereas CPLEX achieves this in less than 20% of the cases. Moreover, in large-scale instances, the branch-and-price algorithm show an improvement of over 15% compared with ALNS algorithm. Sensitivity analysis is conducted on key parameters in the model. Experimental results indicate that different parameters for patient-caregiver matching and penalties for time window violations both impact operational costs; mixed time windows are more efficient than soft and hard time windows, with the total cost under mixed time windows being about 2% lower than that under hard time windows, while also avoiding extreme scenarios; the constraint of carrying medical supplies can affect the total cost by up to 40%, making it a necessary consideration for this constraint in the model. Reference | Related Articles | Metrics

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Integrated Surgical Scheduling Considering Intensive Care Resource YANG Fan, NIE Guangmeng, WANG Kexia, LI Hui, TAN Jiawen, XIE Xiaolei Industrial Engineering Journal    2024, 27 (5): 33-42.   DOI: 10.3969/j.issn.1007-7375.240210 Abstract （8）      PDF（pc） （574KB）（3）       Knowledge map    Integrated surgical scheduling refers to the coordination of various resources required for surgical patients, such as operating rooms, surgical staff, and hospital beds, during the scheduling process. The lack of intensive care resources, such as ICU beds equipped with monitoring equipment and staffed with medical personnel, poses a severe challenge to the healthcare system, such as delayed surgeries and postoperative health deterioration. The policies obtained using traditional surgical scheduling models often ignore the operating pressure of ICU resources. A bi-objective programming model was developed to simultaneously optimize the efficiency of the operating room and the operating pressure of the ICU. A modified genetic algorithm was proposed that enhances the flexibility of exploration by introducing diverse strategies to the mutation operator, and a repair operator was introduced to improve the feasibility of solutions under complex constraints. By employing a simulation-driven initialization approach, this study effectively overcomes the difficulty of generating a valid initial population in solving large-scale problems. Experiments based on real hospital data show that the algorithm obtains near-optimal solutions for small-scale arithmetic cases, and at the scale of 50 patients, the algorithm is able to reduce the maximum operating pressure of ICU by 10.00% to 28.56% compared to the surgery scheduling policy that only considers the operating room. Reference | Related Articles | Metrics