A5101813917- Scheduling and Optimization Algorithms
- Advanced Manufacturing and Logistics Optimization
- Assembly Line Balancing Optimization
- Transportation Planning and Optimization
- Vehicle Routing Optimization Methods
- Traffic Prediction and Management Techniques
- Manufacturing Process and Optimization
- Metaheuristic Optimization Algorithms Research
- Advanced Multi-Objective Optimization Algorithms
- Transportation and Mobility Innovations
- Data Management and Algorithms
- Optimization and Packing Problems
- Environmental Impact and Sustainability
- Maritime Ports and Logistics
- Forecasting Techniques and Applications
- Supply Chain and Inventory Management
- Stock Market Forecasting Methods
- Digital Transformation in Industry
- Multi-Criteria Decision Making
- Urban and Freight Transport Logistics
- scientometrics and bibliometrics research
- Consumer Retail Behavior Studies
- Sustainable Supply Chain Management
- Complex Network Analysis Techniques
- Neural Networks and Applications
Sichuan University
2016-2025
Zhejiang Sci-Tech University
2024-2025
Beijing Institute of Technology
2023
Huazhong Agricultural University
2021-2022
Ocean University of China
2020-2021
Hong Kong Polytechnic University
2006-2012
Donghua University
2008
With the increasing environmental awareness, apparel manufacturers have begun to consider issues in supplier evaluation and selection. It is crucial assess suppliers based on their performance along with other criteria for This paper addresses green selection problem global manufacturing by developing a methodological framework triple bottom line principle fuzzy multi-criteria decision-making (MCDM) model. First, hierarchy established comprehensive literature review, on-site investigation...
Progress to-date towards the Sustainable Development Goals (SDGs) has fallen short of expectations and is unlikely to fully meet 2030 targets. Despite little chance imminent success, past assessments have mostly focused on short- medium-term evaluations, limiting ability explore longer-term effects systemic interactions with time lags delay. Here we undertake global systems modelling a view than previous drivers sustainability progress how they could emerge by 2030, 2050, 2100 under...
This paper proposes s-LSTM, a top-down deep learning model, for efficiently modelling and predicting the spatiotemporal dynamics of groundwater recharge. The model's effectiveness was evaluated compared with three bottom-up machine models using case study 246 bores in South Australia. results demonstrate that s-LSTM outperformed by 3.7%–28.9% prediction performance (based on root mean squared error) reduced training time 37.4%–99.5%. Furthermore, exhibited superior capabilities terms...
Abstract China’s long-term sustainability faces socioeconomic and environmental uncertainties. We identify five key systemic risk drivers, called disruptors , which could push China into a polycrisis: pandemic disease, ageing shrinking population, deglobalization, climate change, biodiversity loss. Using an integrated simulation model, we quantify the effects of these on country’s framed by 17 Sustainable Development Goals (SDGs). Here show that change would be two most influential...
This paper investigates a flexible assembly line balancing (FALB) problem with work sharing and workstation revisiting. The mathematical model of the is presented, its objective to meet desired cycle time each order minimize total idle line. An optimization developed tackle addressed problem, which involves two parts. A bilevel genetic algorithm multiparent crossover proposed determine operation assignment workstations task proportion shared being processed on different workstations....
Link travel speeds in road networks are fundamental data many research areas of traffic, transportation, and logistics. To support the these areas, we develop a dataset, containing on each link different time periods together with real network map. The dataset is collected from representative megacity Western China, Chengdu. this city involves urban structures. shows realistic variations randomness speeds. This enables data-driven decision-making problems transportation logistics areas.
Graph Machine Learning is essential for understanding and analyzing relational data. However, privacy-sensitive applications demand the ability to efficiently remove sensitive information from trained graph neural networks (GNNs), avoiding unnecessary time space overhead caused by retraining models scratch. To address this issue, Unlearning (GU) has emerged as a critical solution, with potential support dynamic updates in data management systems enable scalable unlearning distributed while...
The production and processing of textile apparel products involve the use various chemicals. These chemicals, such as alkylphenol ethoxylates (APEOs), are released during washing wearing may be potentially harmful to humans aquatic environment. primary objective this research was investigate effect laundering on nonylphenol (NPEOs) octylphenol (OPEOs) in textiles. We performed experiments nine unwashed samples. results showed that levels NPEOs OPEOs were significantly reduced after compared...
Meat production is a major contributor to global environmental degradation, including groundwater nitrate contamination driven by intensive fertilizer use and manure production. This study explores the implications of substituting conventional meat products (beef, poultry, pork) with alternative protein sources—plant-based, insect-based, cultured meat—using U.S. market as baseline. Employing an eXtreme Gradient Boosting (XGBoost) model, we quantify risk exceedance compare...