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Existing research on the reliability of multi-state systems concentrated on one-dimensional systems. However, many two-dimensional systems widely exist in real-world applications such as collaborative robot supported matrix production systems and touch panel systems. Motivated by these practical systems, this research studies a two-dimensional sliding window system consisting of multi-state components which fails if the cumulative performance of any sliding window cannot meet a predetermined demand. The system reliability is evaluated by extending the universal generating function technique. In order to improve the system availability, the joint optimization problem of component maintenance and allocation is further studied. A case study on a collaborative robots supported matrix automotive assembly system is presented to illustrate the proposed model and algorithm.
Hui Xiao; Kunxiang Yi; Haitao Liu; Gang Kou. Reliability modeling and optimization of a two-dimensional sliding window system. Reliability Engineering & System Safety 2021, 215, 107870 .
AMA StyleHui Xiao, Kunxiang Yi, Haitao Liu, Gang Kou. Reliability modeling and optimization of a two-dimensional sliding window system. Reliability Engineering & System Safety. 2021; 215 ():107870.
Chicago/Turabian StyleHui Xiao; Kunxiang Yi; Haitao Liu; Gang Kou. 2021. "Reliability modeling and optimization of a two-dimensional sliding window system." Reliability Engineering & System Safety 215, no. : 107870.
This paper addresses a coordinated optimization problem of production and delivery operations in apparel supply chains. A fleet of heterogeneous vehicles are used to deliver the accessories produced on parallel machines to a number of apparel production plants. We consider the flexible vehicle departure time between the production and distribution. A novel hybrid intelligent solution framework is proposed to solve this problem, by decomposition the optimum-seeking process is simplified and the computational complexity is reduced. The effectiveness of proposed framework is evaluated by numerical experiments. Experimental results show that the proposed solution framework exhibits better optimization performance in terms of the solution quality and computational time than other state-of-the-art algorithms.
Zhaoxia Guo; Jingjie Chen; Guangxin Ou; Haitao Liu. Coordinated Optimization of Production and Delivery Operations in Apparel Supply Chains Using a Hybrid Intelligent Algorithm. Advances in Intelligent Systems and Computing 2018, 9 -15.
AMA StyleZhaoxia Guo, Jingjie Chen, Guangxin Ou, Haitao Liu. Coordinated Optimization of Production and Delivery Operations in Apparel Supply Chains Using a Hybrid Intelligent Algorithm. Advances in Intelligent Systems and Computing. 2018; ():9-15.
Chicago/Turabian StyleZhaoxia Guo; Jingjie Chen; Guangxin Ou; Haitao Liu. 2018. "Coordinated Optimization of Production and Delivery Operations in Apparel Supply Chains Using a Hybrid Intelligent Algorithm." Advances in Intelligent Systems and Computing , no. : 9-15.
With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH) technology. The proposed algorithm consists of three main stages: (1) training the basic classifier; (2) selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3) training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection) and GMDH-U (GMDH-based semi-supervised feature selection for customer classification) models.
Lintao Yang; Honggeng Yang; Haitao Liu. GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting. Sustainability 2018, 10, 217 .
AMA StyleLintao Yang, Honggeng Yang, Haitao Liu. GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting. Sustainability. 2018; 10 (1):217.
Chicago/Turabian StyleLintao Yang; Honggeng Yang; Haitao Liu. 2018. "GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting." Sustainability 10, no. 1: 217.
With the increasing environmental awareness, apparel manufacturers have begun to consider environmental issues in supplier evaluation and selection. It is crucial to assess suppliers based on their environmental performance along with other criteria for supplier selection. This paper addresses the green supplier evaluation and selection problem in global apparel manufacturing by developing a methodological framework for green supplier evaluation and selection based on the triple bottom line principle and a fuzzy multi-criteria decision-making (MCDM) model. First, a green supplier evaluation criteria hierarchy based on the triple bottom line principle is established based on comprehensive literature review, on-site investigation and policy analysis. Then, a fuzzy MCDM model is presented to evaluate and select the best material supplier. Finally, a sensitivity analysis is conducted to verify the effectiveness of the proposed framework. Results show that the proposed framework can handle green supplier evaluation and selection in apparel manufacturing effectively.
Zhaoxia Guo; Haitao Liu; Dongqing Zhang; Jing Yang. Green Supplier Evaluation and Selection in Apparel Manufacturing Using a Fuzzy Multi-Criteria Decision-Making Approach. Sustainability 2017, 9, 650 .
AMA StyleZhaoxia Guo, Haitao Liu, Dongqing Zhang, Jing Yang. Green Supplier Evaluation and Selection in Apparel Manufacturing Using a Fuzzy Multi-Criteria Decision-Making Approach. Sustainability. 2017; 9 (4):650.
Chicago/Turabian StyleZhaoxia Guo; Haitao Liu; Dongqing Zhang; Jing Yang. 2017. "Green Supplier Evaluation and Selection in Apparel Manufacturing Using a Fuzzy Multi-Criteria Decision-Making Approach." Sustainability 9, no. 4: 650.