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Table of Content

    30 June 2023, Volume 26 Issue 3 Previous Issue    Next Issue
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    System Analysis & Management Decision
    A Cost Service Equilibrium Model and Order Quantity Decision Considering Correlated Procurement Lead Time
    WANG Jian, ZHANG Jingzhi, ZHU Tingting
    2023, 26 (3):  1-7.  doi: 10.3969/j.issn.1007-7375.2023.03.001
    Abstract ( 894 )   HTML ( 242 )   PDF (570KB) ( 1580 )   Save
    Abnormal events may cause supply chain disruptions or order delays, with the result that an abnormal, procurement lead time (PLT) is usually longer than a normal one. Enterprises need to balance the cost and service level to develop the optimal ordering strategy to deal with abnormal events. A higher level of order quantity can reduce the number of orders and form a robust ordering strategy; a lower level of order quantity can form a flexible ordering strategy. The correlation of PLTs is defined as the mean of the differential multiplier terms between abnormal and normal PLTs. Based on the two correlated PLTs, a cost service equilibrium model is established for retailer inventory decisions to study the decision-making of order batches. Results show that when the cycle service level is high, PLTs correlation does not affect the ordering strategy of an enterprise; when the cycle service level is lower than a certain value, enterprises tend to order higher quantity, as PLTs correlation increases, so as to form a robust ordering strategy. Simultaneously, there are multiple service level thresholds for the impact of PLT fluctuations on order batch quantity. In different service level ranges, enterprises can adopt different optimal ordering strategies. This finding is significantly different from that only one service level threshold for the impact of PLT fluctuations on order quantity in the independent PLT hypotheses. As PLT fluctuations increase, enterprises may adopt different strategies from those with independent PLT hypotheses.
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    Supply Chain Emission Reduction Decisions Considering Overconfidence under Carbon Trading Policies
    SUN Licheng, YU Jinhan, WANG Yushi
    2023, 26 (3):  8-17,28.  doi: 10.3969/j.issn.1007-7375.2023.03.002
    Abstract ( 1112 )   HTML ( 17 )   PDF (1374KB) ( 1443 )   Save
    In order to study the influence of overconfidence psychology of supply chain members on supply chain emission reduction decisions under carbon trading policies, taking a supply chain composed of a low carbon manufacturer and a retailer as the studied object, four Stackelberg game models are established respectively, including complete rationality of supply chain members, manufacturer overconfidence only, retailer overconfidence only and overconfidence of both supply chain members. Comparative analysis of supply chain emission reduction strategy for each model is conducted to provide theoretical basis for enterprise emission reduction strategies. Results are as follows. 1) Under carbon trading policies, when the manufacturer is overconfident, its profits rise regardless of the market environment, while the retailer profits decrease slightly; when the retailer is overconfident, the manufacturer profits rise and the retailer profits decline when the market environment is good, otherwise when the market environment is bad. 2) Under different carbon trading prices, the retailer profits show different trends with the increase of manufacturer overconfidence. When the carbon trading price is high, the retailer profits increase first and then decrease with the increase of manufacturer overconfidence; when the carbon trading price is low, the retailer profits decrease with the increase of manufacturer overconfidence. 3) Compared with the situation of rational supply chain members, overconfidence of manufacturers or retailers improves the emission reduction rate of products, and this effect becomes large as carbon trading prices rise.
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    Evolutionary Game Analysis of Lightweight Commercial Vehicle Supply Chains under External Effects of Carbon Emission Reduction and Overloading Prohibition
    XING Qingsong, ZHONG Wanqiu, PENG Xin, DENG Fumin
    2023, 26 (3):  18-28.  doi: 10.3969/j.issn.1007-7375.2023.03.003
    Abstract ( 1019 )   HTML ( 14 )   PDF (1676KB) ( 1426 )   Save
    Light commercial vehicles are the most promising feasible technique route of long-distance green freight transportation for highway traffic, after implementing the carbon emission policy under the "double carbon" target and the overloading prohibition policy. To promote the steady development of their supply chains, on the basis of analyzing evolutionary game decision-making behavior of different main bodies, this paper establishes a tripartite evolutionary game model with manufacturers, distributors, consumers as the main body. The behavior strategy of the main body in the evolutionary stable state is given. Furthermore, through establishing a system dynamics model, the strategy selection of stakeholders in different situations and the influence of external variable changes on the stability strategy of system evolution are analyzed. Results show that: there are two evolutionary forms of consumer demand and market structure transformation under the external effects of policies and regulations including induced and mandatory forms, which are related to the slow-release cycle of policies and regulations; when the slow-release cycle is long, in order to adapt to market structure changes, manufacturers actively participate in the product sector structure optimization before consumers, inducing consumer demand adjustment, and evolving the lightweight commercial vehicle supply chain to a stable state after a long period of time; when the slow-release cycle is short or non-existent, the benefit path dependence of consumers is blocked. Considering the sustainability of employment and operation, consumers are forced by the external effects of policies and regulations to adjust demand, participate in the light commercial vehicle supply chain and promote the participation of manufacturers and dealers through the demand "pull" and the policies and regulations external effect "push", ultimately achieving a stable state of the supply chain evolution in a relatively short period; finally, the dynamic adjustment in the slow-release cycle of policies and regulations should be synchronized with technical iterations, such that policies and regulations can play a better role on the external effects to form a two-way feedback mechanism on both sides of supply and demand, which is conducive to the evolution of a lightweight commercial vehicle supply chain to a stable state in a relatively short period of time.
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    Influence of Consumers' Pre-purchase Information Search Behavior on the Choice of Manufacturers' Platform Sales Modes
    LI Liang, LONG Liujiang, NIE Jiajia
    2023, 26 (3):  29-38,46.  doi: 10.3969/j.issn.1007-7375.2023.03.004
    Abstract ( 991 )   HTML ( 12 )   PDF (1198KB) ( 1397 )   Save
    In order to study the influence of consumer behavior on the choice of manufacturers' platform sales modes, a model of manufacturers' platform sales modes is established, incorporating consumers' pre-purchase information search behavior into the model. It is found that: if consumers' pre-purchase information search behavior is not taken into account, manufacturers choose the agency mode only when the platform draw ratio is small or large, and choose the distribution mode at other times; after considering consumers' pre-purchase information search behavior, the area where manufacturers choose the distribution mode expands; furthermore, the search behavior of consumers increases the profit of manufacturers under the same sales mode. At the same time, an increase of search probability decreases the wholesale and retail prices of supply chain members under the distribution mode, while the prices under the agency mode show non-monotonic changes.
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    Joint Decision on Pricing and Inventory Replenishment of Agri-food with Dual-channel Sales
    CHEN Jun, KANG Sha
    2023, 26 (3):  39-46.  doi: 10.3969/j.issn.1007-7375.2023.03.005
    Abstract ( 1279 )   HTML ( 42 )   PDF (724KB) ( 1650 )   Save
    The near-field competition has led a growing number of traditional retailers to sell fresh agri-food products with dual-channel sales, which causes a new joint optimization problem of pricing and inventory. Aiming at this problem, assuming that the online channel only sells fresh products, while the offline channel sells both fresh and deteriorating products, a joint decision model of pricing and inventory with demand dependent on price and inventory level is established, using the deteriorating inventory theory, with the objective of maximizing periodic profits. The properties of the existence of the optimal solution are analyzed. Based on the two-part solution method, a heuristic algorithm is designed to solve the optimal freshness period, sales cycle, pricing and profit. Finally, the sensitivity analysis of unit cost, demand rate, fresh-keeping period, deterioration rate is conducted. Results show that the retailer profits increase with the extension of the fresh-keeping period, and fluctuate slightly with the increase of the deterioration cost under dual-channel sales. The basic conditions for retailers to gain higher profits are lower fresh-keeping cost and deterioration cost, higher sales prices and a larger online market share.
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    Optimal Decision Coordination of a Three-level Supply Chain for Agricultural Products Considering Service Levels and Promotion Efforts
    YANG Huaizhen, FU Guilin, LI Lei
    2023, 26 (3):  47-57.  doi: 10.3969/j.issn.1007-7375.2023.03.006
    Abstract ( 927 )   HTML ( 7 )   PDF (1301KB) ( 1408 )   Save
    Under the premise that sales prices, service levels and promotion effort levels have an effect on market demand, this study focuses on a three-level supply chain of agricultural products composed of a manufacturer, a TPL service provider and a retailer, in which the manufacturer is responsible for the production of agricultural products, the TPL service provider is responsible for the cold chain transportation, preservation and distribution of agricultural products, and the retailer is responsible for the promotion of agricultural products. A Stackelberg game decision-making model of the three-level supply chain of agricultural products is established with centralized and decentralized supply chains. The influence of factors such as freshness demand elasticity and promotion effort demand elasticity on the optimal decision-making of the agricultural product supply chain system is analyzed, and the optimal decision results between centralized decision-making and non-contracted decentralized decision-making are compared. On this basis, a contract of "revenue sharing + quantity discount + cost mutual burden" is established to coordinate the supply chain system. Finally, through the sensitivity analysis of the revenue sharing coefficient and transportation prices, it is found that when the contract parameters satisfy a specific relationship, it is conducive to the good coordination of the supply chain system and the optimal Pareto improvement of profits.
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    Financing Strategy Selection of Manufacturers in a Dual Channel Supply Chain with 3PL Enterprises
    SHAO Huaipeng, YU Wencheng
    2023, 26 (3):  58-66.  doi: 10.3969/j.issn.1007-7375.2023.03.007
    Abstract ( 949 )   HTML ( 6 )   PDF (759KB) ( 1573 )   Save
    To study the value of 3PL enterprises in alleviating the problem of supply chain capital constraints, a Stackelberg game model under two financing strategies including 3PL financing and retailer financing is established in a supply chain system consisting of retailers, 3PL enterprises and capital constrained manufacturers, considering factors such as the acceptance of direct sales channels, market competition intensity and financing interest rates. Then, the optimal pricing strategy of decision makers in a dual-channel supply chain is obtained. Results show that under two financing modes, manufacturers’ wholesale prices, direct channel prices, retailers’ prices, and freight cost of both channels are positively related to the intensity of market competition; direct channel prices and direct channel freight costs are positively related to direct channel acceptance, while retailers’ prices, manufacturers’ wholesale prices, and resellers’ freight costs are negatively related to it. 3PL enterprises providing financial services can effectively relieve the financial pressure on manufacturers. Moreover, the profit of manufacturers choosing 3PL financing is higher than that of choosing retailer financing, and 3PL enterprises can also benefit from providing financial services.
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    A Reliability Assessment and Preventive Maintenance Strategy of Wind Turbines Considering Natural Degradation and Shocks
    LI Haiyan, LI Yaping, HAN Tengfei
    2023, 26 (3):  67-74.  doi: 10.3969/j.issn.1007-7375.2023.03.008
    Abstract ( 842 )   HTML ( 14 )   PDF (837KB) ( 1354 )   Save
    The reliability of wind turbines is influenced by both natural degradation and external shocks. The Markov chain method is often used to model the natural degradation process of components. The existing related studies ignore the impact of external shocks on degradation, therefore, an improved Markov chain method is used to model the degradation process of components. An improved transition probability matrix is also proposed to describe the influence of external shocks on degradation, and a homogeneous Poisson process is used to describe the random shock process. On this basis, component reliability modeling is conducted, while a revenue model of preventive maintenance strategies is established with the preventive maintenance time being studied. Finally, using a key component of a wind turbine, i.e. the generator, as an example, the effectiveness of the proposed Markov chain modeling method, which considers both natural degradation and external shocks, as well as the economy of the preventive maintenance strategy is verified.
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    Flexible Extended Warranty Strategy of Components for Different Warranty Intervals Between the Whole Machine and Components
    MO Simin, DING Rui, WANG Huijuan, WANG Wenli, ZENG Jianchao
    2023, 26 (3):  75-85.  doi: 10.3969/j.issn.1007-7375.2023.03.009
    Abstract ( 771 )   HTML ( 5 )   PDF (618KB) ( 1372 )   Save
    For equipment products produced by manufacturers, there is a problem of different warranty intervals between a whole equipment machine and its components provided by suppliers. This problem brings high warranty cost to manufacturers, while also affecting consumers’ use and causing their economic losses. To address this issue, this paper investigates a flexible extended warranty strategy for the components provided by suppliers in equipment products. A profit function of suppliers and a cost function of manufacturers are designed respectively through analyzing several cases of different warranty intervals between a whole machine and components. Furthermore, an extended warranty model is proposed, where the objectives are the maximization of supplier’s unit time profit and the minimization of manufacturer’s unit time cost, and the decision variables include the unit time extend warranty price, the single maintenance price during non-extended warranty period, the start and end time of the flexible extended warranty strategy. The model is optimized and solved by Stackelberg game theory. The optimal extended warranty strategies under different prices of extend warranty and maintenance as well as under the maximum cost budget of manufactures are obtained respectively through theoretical and numerical value analysis.
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    System Modeling & Optimization Algorithm
    Identification Method of Product Quality Problems Based on Two-view Semi-supervised Learning
    YAO Chi, PAN Ershun
    2023, 26 (3):  86-94.  doi: 10.3969/j.issn.1007-7375.2023.03.010
    Abstract ( 823 )   HTML ( 11 )   PDF (684KB) ( 1548 )   Save
    Based on the abundant unstructured and unlabeled texts of consumer reviews in e-commerce websites, a two-view semi-supervised learning method is proposed to classify the reviews and identify the content related to product quality problems, so as to mine the hidden quality defects and dangers of products. Comprehensively considering the characteristics of vocabulary, emotion, domain and so on, the text view and non-text view are constructed, and the Co-training collaborative training algorithm is adopted to classify the reviews according to whether quality problems are involved. Taking the electric kettle as an example, the consumer reviews were crawled from an e-commerce website for empirical analysis. Results show that F1 score and AUC of the proposed method are 82.18% and 86.24%, respectively, which is significantly improved compared with the single view supervised learning classifier.
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    A Multi-AGV Charging Strategy of Intelligent Workshops Considering Batch Arrivals
    PENG Chengfeng, LI Zhantao, CHEN Qingxin, WANG Yongyang
    2023, 26 (3):  95-106.  doi: 10.3969/j.issn.1007-7375.2023.03.011
    Abstract ( 836 )   HTML ( 16 )   PDF (4942KB) ( 1416 )   Save
    Considering a production unit of a certain process with automatic guided vehicles (AGV) transporting materials and equipment in a smart workshop, the charging scheduling problem considering batch arrivals of jobs is studied. A mathematical model of multi-AGV charging scheduling for single process and multiple parallel machines is established with the average waiting time of jobs as the optimization objective, by analyzing the problem characteristics. A combined algorithm generation framework is proposed by dynamically decomposing the charging processes of AGVs. Then, 27 combined charging algorithms are generated by embedding priority rules. Comprehensive simulation test examples are designed to compare and analyze the effectiveness and performance of these generated algorithms in different environments. Through the study, it is found that the performance of the proposed charging algorithm can be improved by reducing the time of AGVs in charging stations. The result can provide guidance for production managers to develop charging strategies.
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    Home Health Care Scheduling with Multiple Time Windows Based on Branch and Price Algorithm
    LI Yanfeng, LUO Nan, XIANG Ting
    2023, 26 (3):  107-115,133.  doi: 10.3969/j.issn.1007-7375.2023.03.012
    Abstract ( 857 )   HTML ( 10 )   PDF (937KB) ( 1412 )   Save
    In order to reduce the scheduling cost of health care workers and improve customer satisfaction, the home health care scheduling problem is studied. Considering multiple time windows of customers' acceptable service and different preferences for different time windows, a mathematical model is established with the objective of minimizing total operating cost and maximizing the satisfaction degree. Based on the Dantzig-Wolfe decomposition principle, the model is reconstructed into a set partitioning master problem and a shortest path subproblem with multiple time Windows. The problem is solved by branch and price algorithm, where the column generation is embedded into a branch-and-bound framework. According to the characteristics of problems with multiple time windows, a random greedy algorithm to quickly obtain the initial solution and an improved label-setting algorithm to solve the subproblem are designed. The effectiveness of the proposed algorithm is verified by comparing with CPLEX through 50 testing numerical examples. Finally, by comparing the results with single time window and multiple time window examples, it is found that the scheduling cost can be effectively reduced when customers provide multiple acceptable service time windows.
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    An Interdependence Balance Algorithm for Solving 0-1 Knapsack Problems
    LUO Yabo, TENG Hongxi
    2023, 26 (3):  116-123.  doi: 10.3969/j.issn.1007-7375.2023.03.013
    Abstract ( 903 )   HTML ( 8 )   PDF (697KB) ( 1456 )   Save
    To extend the methods for solving the 0-1 knapsack problem, one of classical NP hard problems, simulating the natural mechanism of achieving dynamic equilibrium by interdependence and mutual restriction among species in an ecosystem, this paper puts forward a novel bionic algorithm: the interdependence balance algorithm. The algorithm describes the designed variables with the population size, uses the interdependence relationship as the optimization driving force, and aims to achieve a steady-state of a system. Self-growth functions, interdependence functions and growth functions are designed to describe the variation patterns of the designed variables and promote the optimization process of solutions. The results of the interdependence balance algorithm for solving 10 different scale 0-1 knapsack problems are compared with the literature data in recent years. Results show that the algorithm can obtain the currently known optimal solution in 8 different scale problems, which verifies the convergence and solution performance of the interdependence balance algorithm, and shows that the algorithm is effective and competitive for solving 0-1 knapsack problems.
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    Research on Short-term Crude Oil Schedule Based on Improved SPEA2
    WANG Shujuan, HOU Yan, TENG Shaohua, ZHU Qinghua
    2023, 26 (3):  124-133.  doi: 10.3969/j.issn.1007-7375.2023.03.014
    Abstract ( 863 )   HTML ( 8 )   PDF (1082KB) ( 1542 )   Save
    To solve the multi-objective optimization problem of short-term crude oil scheduling, on the basis of four objectives in existing optimization models including the number of charging tanks, the times of charging tank switching, the mixing cost of crude oil in pipelines and the mixing cost of charging tank bottoms in a crude oil scheduling process, the model established in this paper adds an optimization objective of the energy consumption cost of crude oil transferring through pipelines, which is more coincident to real production. The extreme value archive set is introduced into Strength Pareto Evolutionary Algorithm 2 (SPEA2), while Multi-Objective Grey Wolf Optimizer (MOGWO) algorithm is combined to guide the update of the extreme value archive set to enhance the global search ability of the algorithm; the cosine similarity is adopted to prune the extreme value archive set to improve the diversity of individuals in the set. The modified algorithm is compared with several representative evolutionary multi-objective optimization algorithms, and the experimental results show that the proposed one has better performance in solving the short-term scheduling problem of crude oil.
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    A Wafer Release Planning Simulation Optimization Method Based on Allocated Clearing Functions
    YU Yihao, ZHANG Zhengmin, GUAN Zailin
    2023, 26 (3):  134-142.  doi: 10.3969/j.issn.1007-7375.2023.03.015
    Abstract ( 783 )   HTML ( 8 )   PDF (1039KB) ( 1376 )   Save
    In order to improve the solution accuracy and convergence effect of traditional simulation optimization methods, a two-stage simulation optimization method based on segmented linearly allocated clearing functions (ACF) is proposed to solve the wafer release planning, which is used to decide the optimal release rates for different types of products in each planning cycle. The method first constructs a segmented linear fitting model to fit ACF. A mathematical release model based on ACF is established and solved with the objective of minimizing production costs to quickly obtain an approximate global optimal solution as a high quality initial solution. Then, a discrete-event simulation model is developed combining with simulated annealing algorithm to form a simulation optimization framework. A satisfactory solution that meets the production reality is obtained by performing a neighbourhood search based on the initial solution. Experimental results show that the two-stage simulation optimization method based on segmented linearly ACF saves more than 27% of production cost compared to the comparison methods and the current enterprise methods.
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    Wafer Cycle Time Prediction of IPSO-LSTM Considering Feature Learning
    ZHANG Lantian, SHI Yuqiang
    2023, 26 (3):  143-150.  doi: 10.3969/j.issn.1007-7375.2023.03.016
    Abstract ( 852 )   HTML ( 8 )   PDF (1221KB) ( 1514 )   Save
    In order to promote the application of big data technology in manufacturing workshops, aiming at the problem that the temporality and strong noise of massive manufacturing data have an effect on prediction accuracy of cycle time in complex wafer manufacturing process, a cycle time prediction method based on improved particle swarm optimization-long short term memory (IPSO-LSTM) considering feature learning is proposed. A combination of denoising auto-encoder and sparse auto-encoder are used to construct a deep auto-encoder to enhance the abilities of feature learning and noise resisting. IPSO algorithm is used to optimize the parameters of LSTM, overcoming time dependence and improving the performance of the prediction model. Experiments verifies that the prediction accuracy of the proposed approach is better than that of traditional machine learning methods, of which the average absolute error is less than 3%. The effectiveness of the feature learning method is analyzed, which is introduced into traditional prediction methods, such as support vector regression and multi-layer perceptron, resulting an R-square being increased by 1.46% and 1.05%, respectively, and providing a new solution for effective prediction of wafer processing cycle time.
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    Bearing Fault Diagnosis Using Sparrow Algorithm to Optimize Broad Learning Systems
    CHEN Guanglin, YU Liya, ZHANG Chenglong, ZHOU Peng, LI Xiaoyu
    2023, 26 (3):  151-158.  doi: 10.3969/j.issn.1007-7375.2023.03.017
    Abstract ( 914 )   HTML ( 14 )   PDF (1524KB) ( 1481 )   Save
    Health monitoring and fault diagnosis of rolling bearings can ensure continuous and effective work of mechanical equipment. When using deep learning to model the massive and complex data in the context of industrial big data, it needs a lot of computational resources, resulting in problems such as training stagnation or difficulty in training. This paper attempts to use Broad Learning Systems to replace deep learning for bearing fault diagnosis. To address the problem that the classification effect of a broad learning system is limited by the choice of its own hyperparameters, the sparrow search algorithm in metaheuristic algorithms is used to optimize the hyperparameters of a Broad Learning System to improve the accuracy of the broad learning system. The optimized model is applied to the bearing dataset from Western Reserve University and compared with various neural network models to verify the fault diagnosis capability of the proposed method.
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    An Opportunistic Maintenance Strategy for Wind Turbines under External Environment Shocks
    CHENG Hui, LIU Qinming, YE Chunming, YANG Xiaoyan
    2023, 26 (3):  159-167.  doi: 10.3969/j.issn.1007-7375.2023.03.018
    Abstract ( 811 )   HTML ( 2 )   PDF (805KB) ( 1289 )   Save
    Considering the characteristics of complex external operating environment and expensive maintenance cost of wind turbines, an opportunistic maintenance strategy for wind turbines under external environment shocks is proposed. A total degradation model is established by using Gamma process to represent the natural degradation process of the systems and using non-homogeneous Poisson process to describe stochastic shocks caused by the external environment. The maintenance probability is determined and the expected maintenance cost is formulated with the maintenance threshold and the sudden failure threshold as dual constraints. Then, the optimal expected maintenance cost is calculated through the optimal opportunistic maintenance threshold interval. An example is given to analyze the relationship between the expected total maintenance cost and the opportunistic maintenance threshold interval with different influencing factors. Results show that the total degradation of a wind turbine subsystem considering external stochastic shocks is much higher than that considering natural degradation, which is more coincident to the actual operation of the wind turbines.
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    A Method of Identifying Critical Stations for Urban Rail Transit under Bidirectional Interruptions
    HU Junhong, ZHANG Wenjie, TANG Rui, WEN Chengwei
    2023, 26 (3):  168-174.  doi: 10.3969/j.issn.1007-7375.2023.03.019
    Abstract ( 715 )   HTML ( 7 )   PDF (628KB) ( 1450 )   Save
    Since the existing methods of identifying critical stations for urban rail transit networks seldom consider the process of train operation interchange adjustment in the situation of bidirectional disruptions, a critical station identification method of urban rail transit networks under bidirectional disruptions is proposed. Assuming that the station failure situation is a bidirectional disruption of operation, the node failure range is determined based on the distribution of turnback stations in the network. Evaluation indicators are selected from three aspects: network connectivity, network topology and station attributes, while the importance of urban rail transit network stations is comprehensively evaluated by TOPSIS. The metro network in Chengdu system is selected as an example to identify the critical stations. Results show that the critical stations of Chengdu metro network are mainly distributed in the interchange stations where the ring line and the ray line intersect. The importance of some non-interchange stations in the identification results is increased due to the inclusion of other important stations in the turnback section. The proposed method can identify some stations that are easily ignored by the original node removal method, making the evaluation results more realistic.
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