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FAQ: Adaptive Supply Chain Model for Dynamic Demand and Emission Tax Challenges
TL;DR
Companies can gain competitive advantage by using dynamic production models to reduce carbon taxes and improve profitability while meeting emission regulations.
Researchers developed an optimal control model where production rate varies with time, using metaheuristic algorithms like EOA to optimize pricing, inventory, and emission costs.
This approach helps create more sustainable supply chains by reducing emissions while maintaining economic viability, making tomorrow's industrial operations greener and more coordinated.
The Equilibrium Optimizer Algorithm outperformed five other metaheuristic methods in solving complex supply chain problems with dynamic production and carbon constraints.
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The research develops an optimal control-based supply chain model that dynamically adjusts production rates to address fluctuating market demand and carbon emission tax pressures, aiming to improve coordination between manufacturers and retailers while reducing environmental impact.
It addresses the increasing complexity of modern supply chains where companies struggle to balance profitability with volatile demand influenced by seasonality, price changes, and consumer behavior, while also facing pressure from government carbon emission regulations and taxes.
Unlike most existing studies that assume constant production rates, this model treats production rate as an unknown time-dependent control variable that can be adjusted dynamically, and it integrates both price- and time-sensitive demand with emission policies.
Researchers from The University of Burdwan, Jahangirnagar University, and Tecnologico de Monterrey conducted the study, which was published in 2025 in Frontiers of Engineering Management (DOI: 10.1007/s42524-025-4110-6).
The study formulates a two-layer manufacturer-retailer supply chain model where market demand depends on both selling price and time, production rate is defined as a control variable, and carbon emission is modeled as a linear function of production intensity, with higher production generating proportionally higher emissions.
Six metaheuristic algorithms were tested: Artificial Electric Field Algorithm, Firefly Algorithm, Grey Wolf Optimizer, Sparrow Search Algorithm, Whale Optimizer Algorithm, and Equilibrium Optimizer Algorithm (EOA). The Equilibrium Optimizer Algorithm outperformed others in solution accuracy, convergence, and stability.
Results show that adapting production dynamically can reduce emissions while improving coordination between manufacturers and retailers, offering a practical path toward sustainable and economically viable supply chain operations that maintains profitability.
Sensitivity analysis demonstrated how variations in tax rate, production cost, or price elasticity influence profit and emission outcomes, confirming that dynamic production control offers a more realistic strategy than models using fixed production assumptions.
The full study is available in Frontiers of Engineering Management (DOI: 10.1007/s42524-025-4110-6) published by Springer, accessible at https://link.springer.com/journal/42524.
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