A5005807347- Forecasting Techniques and Applications
- Energy Load and Power Forecasting
- Stock Market Forecasting Methods
- Supply Chain and Inventory Management
- Advanced Statistical Process Monitoring
- Sustainable Supply Chain Management
- Multi-Criteria Decision Making
- Time Series Analysis and Forecasting
- Anomaly Detection Techniques and Applications
- Solar Radiation and Photovoltaics
- Power Systems and Renewable Energy
- Market Dynamics and Volatility
- Fault Detection and Control Systems
- Recycling and Waste Management Techniques
- Computational Physics and Python Applications
- Atmospheric and Environmental Gas Dynamics
- Bayesian Modeling and Causal Inference
- Big Data and Business Intelligence
- Parallel Computing and Optimization Techniques
- Modeling, Simulation, and Optimization
- Smart Grid Energy Management
- Data Analysis with R
- Global Energy Security and Policy
- Network Security and Intrusion Detection
- Quality and Supply Management
Queensland University of Technology
2025
The University of Queensland
2022-2024
Shahid Beheshti University
2020-2023
University of Tehran
2023
Monash University
2019-2022
University of Tabriz
2021
University of Newcastle Australia
2019-2020
Bu-Ali Sina University
2014-2015
Universidad Politécnica de Madrid
2015
Purpose: Increase of costs and complexities in organizations beside the increase uncertainty risks have led managers to use risk management order decrease taking deviation from goals. SCRM has a close relationship with supply chain performance. During years different methods been used by researchers manage but most them are either qualitative or quantitative. Supply operation reference (SCOR) is standard model for SCP evaluation which its metrics. In This paper combining quantitative metrics...
Hierarchical forecasting (HF) is needed in many situations the supply chain (SC) because managers often need different levels of forecasts at SC to make a decision. Top-Down (TD), Bottom-Up (BU) and Optimal Combination (COM) are common HF models. These approaches static ignore dynamics series while disaggregating them. Consequently, they may fail perform well if investigated group time subject large changes such as during periods promotional sales. We address problem predicting real-world...
In this paper, we describe our proposed methodology to approach the predict+optimise challenge introduced in IEEE CIS 3rd Technical Challenge. The predictive model employs an ensemble of LightGBM models and prescriptive analysis mathematical optimisation efficiently prescribe solutions that minimise average cost over multiple scenarios. Our ranked 1st 2nd prediction competition.
Traditionally, manufacturers use past orders (received from some downstream supply chain level) to forecast future ones, before turning such forecasts into appropriate inventory and production optimisation decisions. With recent advances in information sharing technologies, upstream (SC) companies may have access point of sales (POS) data. Such data can be used as an alternative source for forecasting. There are a few studies that investigate the benefits using versus POS SC forecasting;...
Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, transition towards carbon-free energy generation battery/load/production scheduling sustainable systems. Typically, these scenarios we want solve an problem depends on unknown future values, which therefore need be forecast. As problems their own right, relatively few research has been done this area. This...
Using machine learning in solving constraint optimization and combinatorial problems is becoming an active research area both computer science operations communities. This paper aims to predict a good solution for using advanced techniques. It extends the work of \cite{abbasi2020predicting} use models predicting large-scaled stochastic by examining more algorithms various costs associated with predicted values decision variables. also investigates importance loss function error criterion...
Renewable energy generation is of utmost importance for global decarbonization. Forecasting renewable energies, particularly wind power, challenging due to the inherent uncertainty in power generation, which depends on weather conditions. Recent advances hierarchical forecasting through reconciliation have demonstrated a significant increase quality forecasts short-term periods. We leverage cross-sectional and temporal structure turbines farms build cross-temporal hierarchies further...
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Prediction and optimisation are two widely used techniques that have found many applications in solving real-world problems. While prediction is concerned with estimating the unknown future values of a variable, optimising decision given all available data. These methods together to solve problems for sequential decision-making where often we need predict variables then use them determining optimal decisions. This paradigm known as forecast optimise has numerous applications, e.g., demand...
Hierarchical time series forecasting plays a crucial role in decision-making various domains while presenting significant challenges for modelling as they involve multiple levels of aggregation, constraints, and availability information. This study explores the influence distinct information utilisation on accuracy hierarchical forecasts, proposing evaluating locals range Global Forecasting Models (GFMs). In contrast to local models, which forecast each independently, we develop GFMs exploit...
Wind power forecasting is essential for managing daily operations at wind farms and enabling market operators to manage uncertainty effectively in demand planning. This paper explores advanced cross-temporal models their potential enhance accuracy. First, we propose a novel approach that leverages validation errors, rather than traditional in-sample covariance matrix estimation forecast reconciliation. Second, introduce decision-based aggregation levels reconciliation where certain horizons...