Simulation-based optimization is a potent methodology that synergises simulation modelling
with optimization techniques to enhance decision-making and system performance within …

Efficient water management in agriculture is essential for addressing the growing freshwater
scarcity crisis. Multi-Agent Reinforcement Learning (MARL) has emerged as a promising …

Spatial variation of soil moisture as affected by topography and soil textural patterns is an
important control on variability of yields in agricultural fields. Site-specific irrigation …

This paper addresses the complex constraints and multi-objective requirements faced in the
operation process of the Cascade Canal System (CCS), and proposes the Multi-Objective …

Efficient water management in agriculture is important for mitigating the growing freshwater
scarcity crisis. Mixed-integer Model Predictive Control (MPC) has emerged as an effective …

This study proposes a Semi-centralized Multi-agent Reinforcement Learning (SC-MARL)
approach for irrigation scheduling in agricultural fields, which are characterized by spatial …

This paper presents an optimization method for hybrid energy systems based on Model
Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov …

The industrial automation sector is rapidly evolving, with a growing need for advanced
control strategies to enhance the efficiency and precision of robotic systems. Model …

Integration of process design and control of chemical process flowsheets (CPFs) is a key
focus in chemical engineering, receiving extensive research attention. The main objective in …

Efficient irrigation scheduling is essential for optimizing water usage, maintaining crop
health, and promoting sustainability in agriculture. This study integrates advanced soil …