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30 August 2024, Volume 27 Issue 4 Previous Issue    Next Issue

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Quality Engineering and Production Reliability

Feature Selection for Aero-Engine Assembly Using Multi-objective Optimization LU Wenhao, KE Yongwei, GUO Yongqiang, SI Shubin 2024, 27 (4):  1-8.  doi: 10.3969/j.issn.1007-7375.240227 Abstract ( 1197 )   HTML ( 30 )   PDF (798KB) ( 156 )   Save Due to the complexity of assembly and testing processes in aero-engine manufacturing, the collected assembly data encompass a large number of assembly features, which seriously interferes with the accurate prediction of assembly quality. Selecting the key quality features of aero-engine assembly to achieve quality prediction becomes a highly challenging task. Therefore, to address this issue, a two-stage feature selection method for aero-engine assembly data based on multi-objective optimization is proposed. Firstly, the optimization objectives of feature selection are defined. In the first stage, the relevant features are selected based on the max relevance and min redundancy (MRMR) algorithm to calculate the mutual information of assembly features and testing indicators. This process filters out the most relevant features related to testing indicators while removing redundant features with interference effects. In the second stage, by introducing a population initialization strategy and adaptive genetic operators, a key quality feature selection process based on the improved non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ) is proposed to obtain the Pareto front of key quality feature subsets for aero-engine assembly. Finally, experimental results demonstrate that the proposed two-stage feature selection method has better applicability and effectiveness than traditional methods, which enhances the feature selection performance and improves the accuracy of quality prediction for aero-engine assembly. References | Related Articles | Metrics

Application of Gini Index-Based Deep Deconvolution in Mechanical Equipment Fault Diagnosis SHI Huifang, MIAO Yonghao, XIA Yu 2024, 27 (4):  9-18.  doi: 10.3969/j.issn.1007-7375.240076 Abstract ( 1467 )   HTML ( 12 )   PDF (2032KB) ( 66 )   Save Deconvolution methods are powerful tools for mechanical equipment fault diagnosis; however, traditional research still relies on shallow feature extraction, making it difficult to handle extremely low signal-to-noise ratios. To address this issue, by introducing the idea of feature learning into the traditional deconvolution theory, a Gini index (GI) based sparse deep deconvolution (GI-SDD) method is proposed for early fault diagnosis of mechanical equipment. First, a band-averaging strategy is adopted to initialize the input layer filter, providing direction for subsequent deconvolution. Next, GI that can represent sparse features of mechanical faults is utilized as the loss function to guide the training of the deep network. Weight optimization is implemented based on the generalized eigenvector algorithm (EVA), thereby learning weak fault features layer by layer. Finally, correlation coefficients and envelope kurtosis (EK) criteria are utilized to evaluate the fault information, reducing dimensionality to output the most significant fault components. Simulation and experiment results demonstrate that the proposed method is robust against strong background noise with fault features being greatly enhanced. Furthermore, the EK of the proposed method improves by 163.43% and 187.11% compared with traditional MED and MGID results respectively. References | Related Articles | Metrics

Quality-Reliability Coupled Modeling and Evaluation for Smart Phone Assembly Lines LUO Yi, ZHANG Ding, LIU Qiang 2024, 27 (4):  19-28.  doi: 10.3969/j.issn.1007-7375.230056 Abstract ( 854 )   HTML ( 20 )   PDF (1415KB) ( 182 )   Save A laboratory digital twin production line for assembling smart phones is built as a prototype testing platform to study the yield transmission and formation mechanisms of production lines caused by the causal relationship of factors in multiple assembly stages. Focusing on two yield issues in screw locking and dispensing processes, a coupled modeling method integrating product quality and equipment reliability for smartphone assembly processes is proposed with the consideration of causal relationships. A dynamic Bayesian network (DBN) model is developed considering causal factors across multiple assembly stages. Then, root cause tracing and importance evaluation affecting final yield formation are conducted. The feasibility and effectiveness of the proposed approache are tested and verified on the digital twin testing platform for assembling smart phones, providing performance evaluation support for yield loss prevention mechanisms and proactive maintenance decisions on production lines. References | Related Articles | Metrics

Production Decisions and Revenue Adjustment Incentive Mechanisms in Leased Manufacturing Systems Considering Equipment Protection LI Yaping, WU Jin, CHENG Yuhong, TAO Liangyan, LIU Sifeng 2024, 27 (4):  29-38,101.  doi: 10.3969/j.issn.1007-7375.240066 Abstract ( 1035 )   HTML ( 19 )   PDF (834KB) ( 73 )   Save To address the potential issue of lessees abusing equipment during cooperative production in leased manufacturing systems, a production decision-making model and an incentive mechanism of revenue adjustment are proposed considering equipment protection to develop the optimal production strategy. From the perspective of equipment protection, an optimization model is developed considering the usage rate and protection degree of the leased equipment by lessees with the objective of maximizing the total revenue of the leased manufacturing system. The model determines the optimal production strategy by defining the effort degree of equipment protection, the equipment usage rate and the state deviation between a new equipment and the equipment after maintenance. Then, an incentive mechanism about revenue adjustment is proposed using compensation to adjust the revenue of both parties. The mechanism ensures that the optimal strategy for independent decision of each party align with the systematic optimal strategy. In this way, the best compensation coefficient is determined and the revenue adjustment plan is developed. Case study shows that the system decision-making model identifies the optimal effort degree and preventive maintenance degree that maximize the total revenue. Additionally, the maximum total revenue obtained by the system decision model is always higher than that with independent decisions. Moreover, increasing lessee efforts in equipment protection and lessor preventive maintenance can reduce the average number of failures of leased equipment. References | Related Articles | Metrics

A Dynamic Opportunistic Maintenance Strategy for Electric Multiple Unit Systems Considering Disassembly Structures QUAN Hairui, WANG Hong, HE Yong, GAO Jining 2024, 27 (4):  39-47.  doi: 10.3969/j.issn.1007-7375.240014 Abstract ( 1725 )   HTML ( 11 )   PDF (846KB) ( 48 )   Save To study the impact of disassembly structures on the maintenance planning of electric multiple unit (EMU) systems, a disassembly hybrid graph model is used to analyze the optimal disassembly sequence for individual components. Additionally, a cost-effectiveness analysis method is employed to select multi-level maintenance strategies, and the optimal reliability threshold of components, considering disassembly costs and installation/ debugging costs, is calculated. On this basis, the concept of opportunity maintenance is introduced to address the problem of disassembly sequence planning for multiple components before maintenance. A multi-objective disassembly sequence optimization method is proposed, and a dynamic opportunistic maintenance strategy model considering waste of reliability value is established. Case analysis shows that compared to traditional sequential preventive maintenance strategies, the dynamic opportunistic maintenance strategy reduces downtime by 16 times through merging maintenance opportunities, effectively reducing downtime costs caused by excessive preventive maintenance and disassembly time. The total maintenance cost is saved by 16.6%, which can meet the economic requirements of maintenance departments and provide theoretical support for the study of maintenance strategies considering complex interdependencies in EMU systems. References | Related Articles | Metrics

Green Supply Chain Management

Co-optimization of Low-carbon Production and Routing for Perishable Goods Considering Supply Disruptions in the Physical Internet ZHAO Pengyun, JI Shoufeng, LIU Hongyu, JI Yuanyuan 2024, 27 (4):  48-59.  doi: 10.3969/j.issn.1007-7375.240206 Abstract ( 55 )   HTML ( 11 )   PDF (1198KB) ( 54 )   Save To address the problem of uncertainty in facility capacity and transportation distances due to unexpected disruptions in supply networks of perishable goods, this study explores the co-optimization of low-carbon production and routing for perishables in the context of the globally open and shared physical internet (PI) environment. As an innovative logistics system characterized by connections, sharing, and adaptability, PI has great potential to enhance the resilience of perishables supply networks and reduce carbon footprints. A scenario-based two-stage stochastic programming model is developed based on the unique characteristics of perishables and the features of PI. To solve the model, a hybrid solution method integrating scenario generation and reduction with improved Benders decomposition is designed. Finally, a case study is carried out in the context of Liaoning Supply and Marketing Anbang Haider Food Supply Network to verify the feasibility of the model and the effectiveness of the algorithm. Sensitivity analysis results show that PI has significant advantages in reducing carbon footprints and enhancing supply chain resilience. Additionally, this study comprehensively analyzes the impacts of carbon emission trading mechanisms, facility capacity loss, increased transportation distances, and product lifecycle effects. This study not only enriches the theoretical foundation of low-carbon production and routing co-optimization in perishables supply networks but also provides important theoretical guidance for logistics companies in coping with uncertainty and pursuing carbon neutrality goals. References | Related Articles | Metrics

Financing On-chain Decision-Making in Agricultural Supply Chains Based on Blockchain and Consumer Sensitivity LIU Qiyou, LIN Liting, ZHANG Chengke 2024, 27 (4):  60-69.  doi: 10.3969/j.issn.1007-7375.230207 Abstract ( 45 )   HTML ( 17 )   PDF (956KB) ( 22 )   Save To solve the challenges of production-side funding shortages and consumption-side food safety concerns in the development of green agricultural supply chains, this study incorporates factors such as consumer sensitivity to agricultural product quality and information search cost into financing on-chain decision-making in agricultural supply chains. Game models of four different situations are established: e-commerce with and without blockchain technology, and internal and external financing for farmers within a supply chain. Through theoretical derivation and empirical simulation analysis, the impact of parameters, such as the sensitivity coefficients of agricultural product quality and information search cost, and the cost associated with blockchain and financing, on farmer choices of financing mode and e-commerce on-chain decisions, is explored. It is found that: with different on-chain decisions of e-commerce, when the external financing rate of farmers is greater than the opportunity cost of e-commerce funds, internal financing is better than external financing, otherwise, external financing is better than internal financing; under different financing modes of farmers, when the on-chain benefit of e-commerce is positive, adopting on-chain is the optimal decision for e-commerce; when the on-chain benefit is negative, and the financing cost saved by on-chain is not enough to offset this negative benefit, not adopting on-chain is the best decision. References | Related Articles | Metrics

Supporting Strategies of Agricultural Supply Chains Based on Contract Farming FENG Chun, YAN Jing, HE Zheng, FENG Yujie 2024, 27 (4):  70-81.  doi: 10.3969/j.issn.1007-7375.230082 Abstract ( 60 )   HTML ( 25 )   PDF (1131KB) ( 55 )   Save In order to study the supply chain support decisions of the government and retailers for agricultural products, three contract farming support models are established considering government production subsidies and retailer investment in poverty alleviation efforts, including the government subsidy model (GS model), the retailer poverty alleviation model (RH model), and the government-retailer joint support model (GR model). Through the comparative analysis of profit relationships and profit increment of each party in the three models, the optimal support decisions of the government and retailers are obtained. Results show that if the government provides production subsidies only to high-cost farmers, it is necessary for retailers to devote poverty alleviation efforts. Conversely, if retailers invest in poverty alleviation efforts for all farmers, the government should then provide production subsidies. Retailers poverty alleviation programs can consistently benefit all farmers. In a consumer-sensitive market, government subsidies provided later can benefit all farmers. In addition, in a market with low consumer sensitivity, the government needs to subsidize first, followed by retailer investment in poverty alleviation efforts. In a consumer-sensitive market, retailers are required to invest in poverty alleviation efforts first, followed by government subsidies. References | Related Articles | Metrics

A Pricing Strategy of Fresh Agricultural Product Supply Chains Considering Mixed Subsidies and Consumer Sensitivity LIANG Xi, WEI Yulian 2024, 27 (4):  82-92.  doi: 10.3969/j.issn.1007-7375.220250 Abstract ( 56 )   HTML ( 24 )   PDF (1050KB) ( 61 )   Save In a supply chain system of fresh agricultural products composed of a single supplier and a single e-commerce, parameters such as consumer sensitivity coefficient, government fresh-keeping technology subsidy and e-commerce green research and development (R&D) subsidy rate are introduced. The effects of government fresh-keeping technology subsidy, e-commerce green R&D subsidy and mixed subsidies on supply chain decision-making are compared and analyzed by Stackelberg game. Results show that increasing consumer sensitivity to freshness or green degree is conducive to improving the profits and social welfare of supply chain members across three modes; with moderate consumer sensitivity to green degree, subsidies provided by the government or e-commerce are conducive to improving social welfare, with mixed subsidies being the most advantageous; under the mode of mixed subsidies, the supplier maximizes efforts in freshness preservation, product green degree, product demand and its profit; with a moderate green R&D subsidy rate of e-commerce, mixed subsidies are most beneficial to fresh e-commerce. If the green R&D subsidy rate is excessively high, single government subsidies become more beneficial. References | Related Articles | Metrics

Dual Governance Strategies of Green Supply Chains from the Perspective of Joint Innovation GAO Peng, WANG Zhihua 2024, 27 (4):  93-101.  doi: 10.3969/j.issn.1007-7375.230119 Abstract ( 38 )   HTML ( 10 )   PDF (858KB) ( 43 )   Save To explore the synergistic effects between different environmental regulatory policies and maximize the motivation for green innovation among supply chain enterprises, game theory and decision optimization methods are used to investigate the market performance and social green performance of green supply chains with two governance strategies of government subsidies and carbon emission reduction regulations from the perspective of joint innovation. Considering carbon emission reduction constraints per unit green product, K-T conditions are introduced to establish and solve the government subsidy models with manufacturer individual innovation (IM) and joint innovation (UG). Results are then compared and analyzed. It is found that only when the carbon emission reduction constraint exceeds a certain threshold can the dual governance strategy be effective, otherwise only the subsidy strategy is effective. Compared to the single subsidy strategy, the dual governance strategy can achieve a higher level of carbon emission reduction per unit of product, which helps to increase the market demand for green products. However, it does not benefit the profits of manufacturers, and does not fully leverage the advantage of the joint innovation model. The two governance strategies have positive synergistic effects in improving consumer surplus and total social welfare, but negative synergistic effects in realizing the social value of joint innovation. References | Related Articles | Metrics

Strategic Decisions in Company + Farmer Supply Chains Coping with Distrust WEI Guangxing, PAN Yuanyuan 2024, 27 (4):  102-111,131.  doi: 10.3969/j.issn.1007-7375.230064 Abstract ( 65 )   HTML ( 21 )   PDF (1128KB) ( 23 )   Save The company + farmer supply chain is an important mode for improving operational efficiency of agricultural supply chains. However, there is a problem of distrust that farmers may not fully trust the market information provided by companies. A supply chain game model is developed to investigate the strategies for addressing farmer distrust in company information transmitting and farmer capacity planning decisions, and to analyze the impact of farmer distrust on operational decisions and farmer profits. Resutls are concluded as follows. Firstly, companies transmit market information strategically; farmer distrust prompts companies to not transmit accurate market information. Companies tend to transmit shrank information if they are more optimistic about market conditions than farmers, otherwise they transmit exaggerated information. Secondly, farmers adjust their productive capacity plans strategically. They increase the capacity if companies are more optimistic about market conditions than farmers, whereas they reduce the capacity. Thirdly, strategic information transmission by companies and strategic capacity adjustments by farmers can cope with the unfavorable effect of farmer distrust on both company profits and farmer capacity planning. However, farmer distrust inevitably leads to profit discrepancies, resulting in farmers consistently earning lower profits than expected. References | Related Articles | Metrics

System Modeling and Optimization Algorithm

A Rolling Horizon Scheduling Method for Airport Ground Service Resources CHEN Qingxin, CHEN Guangjin, XU Guoning, YU Longshui 2024, 27 (4):  112-120.  doi: 10.3969/j.issn.1007-7375.220256 Abstract ( 1274 )   HTML ( 21 )   PDF (818KB) ( 50 )   Save In order to deal with the influence of the random flight arrivals on the scheduling of airport ground service resources, a rolling horizon scheduling method (RHSM) is designed according to the characteristics of the problem. Firstly, the original problem is decomposed into a series of subproblems according to the time axis. Then, with the objective of minimizing the total cost of flight delays and balancing the load of similar resources, a mathematical model of project-resource hierarchical scheduling is established to schedule the subproblems in the rolling horizon of each iteration. Finally, the solution of each subproblem is constructed into the approximate solution of the original problem. In this way, the complex problem is decomposed into several subproblems, which can be solved one by one, ensuring real-time resource scheduling. The performance of this method is verified by the data from three different scales on a certain day at Guangzhou Baiyun International Airport: 01:00-02:00, 15:00-16:00 and 13:00-21:00. Experimental results show that RHSM can deal with the randomness of flight arrival time; compared with the time window decoupled scheduling method, in small-scale data from 01:00-02:00, when the total solution time is the same, RHSM with appropriate parameters can reduce the total cost of flight delays by 36.79%; in medium and large-scale data from 15:00-16:00 and 13:00-21:00, RHSM has faster solution speed with smaller computer memory space. It can be concluded that the proposed method is feasible, and it is helpful to improve the quality and efficiency of airport ground services. References | Related Articles | Metrics

Joint Optimization of Emergency Resource Scheduling for Forest Fires Considering Road Obstruction WU Peng, WANG Lubing, CHU Chengbin 2024, 27 (4):  121-131.  doi: 10.3969/j.issn.1007-7375.230136 Abstract ( 620 )   HTML ( 9 )   PDF (1472KB) ( 44 )   Save A new joint optimization problem for emergency resource scheduling is studied to address the possible obstruction issue of transportation roads after forest fires. A mixed-integer linear programming model for joint scheduling of emergency resources for forest fires is established considering both resource constraints and road obstruction. The optimization objective is to minimize the fire rescue time to reduce the resource loss caused by forest fires. To quickly and efficiently solve the problem, an improved artificial bee colony algorithm with two encoding methods is designed according to the problem characteristics. Finally, experimental results from typical instances and stochastic simulation instances show that i) for real-life instances and small- to medium-scale simulation instances, the optimal fire rescue plan can be obtained within 5 minutes by using the commercial solver CPLEX; and ii) for large-scale forest fires, the proposed improved artificial bee colony algorithm outperforms the commercial solver CPLEX, achieving a higher-quality fire rescue plan in just 10 seconds. It can provide an effective fire rescue plan for emergency teams with road obstruction. References | Related Articles | Metrics

Charging and Discharging Coordinated Routing for Autonomous Electric Taxis Considering Future Operating Value ZENG Weiliang, HAN Yu, FU Hui 2024, 27 (4):  132-140,149.  doi: 10.3969/j.issn.1007-7375.230095 Abstract ( 558 )   HTML ( 15 )   PDF (1571KB) ( 100 )   Save Existing taxi scheduling models typically focus on the optimization of real-time cost while the potential impact of currently planned routes on future operating value is ignored, which is detrimental to continuous scheduling in autonomous driving environment. To this end, this paper proposes a route planning model focusing on long-term benefit, in which the estimated future operating value is incorporated into the real-time scheduling problem by reinforcement learning. Specifically, the model is solved by a neural network first to fit the state-value function for different temporal and spatial states of vehicles, after which a double neural network and the experience replay are used to accelerate the convergence of the algorithm. Through the simulation experiments on the road network of Shenzhen, it demonstrates that our model enables to accurately schedule the fleet in advance, serving more passengers and achieving greater operational profit. Additionally, the cost of fleet energy consumption can be reduced since the model can utilized the peak and off-peak characteristics of time-of-use electricity pricing and vehicle-to-grid (V2G) technology for charging and discharging. Compared to other scheduling models, the proposed model enables to increase the passenger response rate by 4% and the total profit by 25% in long-term operation, which also reduces energy consumption by 50% and passenger waiting time by 20%. References | Related Articles | Metrics

Coordinated Optimization of Pipe Network Layout and Pipe Diameters in a Multi-source Irrigation System LI Yanfeng, CHEN Saiyue 2024, 27 (4):  141-149.  doi: 10.3969/j.issn.1007-7375.220255 Abstract ( 699 )   HTML ( 22 )   PDF (861KB) ( 58 )   Save Water-saving irrigation is the key to accelerating agricultural modernization and improving irrigation efficiency. A mixed integer programming model is established for a multi-source irrigation system with the objective of minimizing the cost of system flow distribution and pipe installation, to determine the pipe network topology and connecting pipe sizes, where the distribution of water flow is considered. A hybrid heuristic algorithm is designed to integrate local searching with an accurate algorithm. It coordinates the optimization of the layout and design for pipe networks in two stages. The connections between nodes and connecting pipe diameters are analyzed, while the flow to demand nodes is allocated. Test examples with different scales verifies the effectiveness of the proposed coordinated optimization algorithm in reducing construction cost of a multi-source irrigation system. References | Related Articles | Metrics

Time-Cost Tradeoff Optimization for Scheduling Repetitive Projects with Fuzzy Activity Durations ZOU Xin, CHEN Danhao, ZHANG Lihui 2024, 27 (4):  150-160,170.  doi: 10.3969/j.issn.1007-7375.230104 Abstract ( 1050 )   HTML ( 7 )   PDF (1221KB) ( 28 )   Save In order to reduce the impact of uncertainty on project performance objectives and improve the robustness of scheduling, the discrete time-cost tradeoff problem for repetitive projects with fuzzy activity durations is investigated. A fuzzy chance-constrained programming model considering the risk preferences of decision makers is developed by means of fuzzy risk measurement to determine the optimal execution modes for all activities (i.e., mode list), thereby to minimize the project budget while meeting the pre-speci?ed risk levels of project delays and cost overruns. Given a known mode list, a forward recursive process for calculating the membership functions of fuzzy project duration and fuzzy total cost is proposed, while an improved genetic algorithm based on electromagnetic mechanism (GA-EM) for searching the optimal mode list is designed accordingly. The effectiveness of the algorithm is veri?ed using a real-life engineering case, and the computational performance of the algorithm is analyzed via numerical experiments. Results show that GA-EM can provide a fuzzy schedule that satisfies the given levels of schedule delays and cost overrun risks, with the average and maximum percentage deviations in the budget not exceeding 0.096% and 0.239%, respectively. References | Related Articles | Metrics

Study on Puzzle-Based Storage System for the Transformation of Plane Smart Warehouses YU Lina, CAI Zhuojie, LIN Jie, YU Jing 2024, 27 (4):  161-170.  doi: 10.3969/j.issn.1007-7375.230112 Abstract ( 754 )   HTML ( 12 )   PDF (1182KB) ( 69 )   Save In response to low space utilization in traditional plane warehouses and the high costs associated with converting them into automated storage and retrieval systems (AS/RS), a study is conducted on a puzzle-based storage system (PBS system) suitable for the transformation of plane warehouses. This system aims to maximize warehouse space utilization while ensuring high picking efficiency. To address the complexity of the retrieval process inherent in a PBS system, an algorithm for diagonal optimal picking paths based on useful points is proposed. This algorithm utilize a useful point movement strategy according to the locations of goods to enhance computational efficiency, thereby reducing the number of picking movements and the operation time. Results indicate that the proposed method can achieve the shortest picking time for single items, single empty slots, and any storage location within milliseconds. Comparative analysis with other algorithms reveals that the proposed algorithm has significant advantages in terms of solution efficiency and stability of the optimal number of picking steps. Analysis of actual application cases confirms that the space utilization of plane warehouses increases by 100% after transformation based on PBS systems, demonstrating its practical value. References | Related Articles | Metrics