A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty

Abstract Adoption of carbon regulation mechanisms facilitates an evolution toward green and sustainable supply chains followed by increased complexity. Through the development usage a multi-choice goal programming model solved improved algorithm, this article investigates sustainability strategies for regulations mechanisms. We first propose logistics that considers assorted vehicle types gas emissions involved with product transportation. then construct bi-objective minimizes total cost as objective function follows environmental considerations in second one. With our novel robust-heuristic optimization approach, we seek to support decision-makers comparison selection emission policies complex settings types, demand economic uncertainty. deploy case-study evaluate analyse two reduction policies, i.e., carbon-tax cap-and-trade policies. The results demonstrate methodology can efficiently deal uncertainty, especially large-scale problems. Our findings suggest governmental incentives policy would be more effective lowering pollution investing cleaner technologies adopting greener practices.