An optimization-based analytics model for sustainable and blockchain-enabled supply chains in uncertain environments

Abstract

The carbon footprint is highly uncertain and directly impacts demand forecasting, with uncertainty arising from both positive and negative perspectives. This duality highlights the contrasting viewpoints of decision-makers during the decision-making process. This study employs generalized trapezoidal bipolar fuzzy numbers to manage uncertainty in carbon emissions and integrates blockchain technology to enhance demand forecasting in the supply chain. Additionally, we incorporate a warm-up process to minimize faulty items during production and consider investments in green technologies to reduce emissions from various activities. This paper provides insights into sustainability, operational efficacy, and profit maximization in uncertain ecological settings. We mathematically formulate the proposed scenario and uniquely calculate the concave combination of expected values from both positive and negative membership components. Optimality is derived, and a numerical analysis is performed to effectively clarify the theory, followed by an extensive sensitivity analysis of various parameters.