A comprehensive review of simulation optimization methods in agricultural supply chains and transition towards an agent-based intelligent digital framework for agriculture 4.0

Abstract

Simulation-based optimization is a potent methodology that synergises simulation modelling with optimization techniques to enhance decision-making and system performance within complex contexts. This comprehensive review underscores the pivotal role of supply chain management in agriculture and elucidates the pivotal role of simulation-based optimization in optimizing processes, mitigating waste, and curbing supply chain vulnerabilities. The review not only identifies prevalent trends and patterns in simulation-based optimization applications within agricultural supply chains but also unearths existing gaps. Beyond analyzing the evolution of traditional-to-modern trends in supply chain problem-solving, this article delves into contemporary simulation-based optimization approaches. Noteworthy among these are agent-based simulation techniques for the simulation facet and artificial intelligence methodologies, including machine learning, for optimization endeavours. In a culminating stride, the article introduces a conceptual framework for a digital and intelligent agricultural supply chain, seamlessly integrating agent-based simulation and reinforcement learning strategies. Through optimization grounded in simulation, this digital twin engenders supply chain designs that hold substantial practicality and efficacy, particularly in the context of Agriculture 4.0. This review amalgamates insights from simulation-based optimization and agricultural supply chain management, culminating in a forward-looking framework poised to fortify the capabilities of Agriculture 4.0.