In today’s competitive environment, integrated decision-making in supply chains that simultaneously encompasses economic, operational, and environmental dimensions has gained critical importance. This research presents a Mixed Integer Linear Programming (MILP) model that, integrates order acceptance, single-machine production scheduling, and multi-vehicle transportation planning within a unified framework. The key distinction of this model is the simultaneous incorporation of carbon emission costs alongside traditional production costs, transportation costs, and tardiness penalties. The proposed model, considering realistic constraints such as non-preemptive order processing, vehicle capacity limitations, and precedence scheduling requirements, provides optimal decisions for order selection, processing sequence determination, vehicle allocation, and delivery scheduling. The model’s objective function seeks to maximize net profit by calculating the difference between revenue from accepted orders and the total of production costs, transportation costs, tardiness penalties, and environmental costs arising from CO₂ emissions. Model validation was conducted through solving number of problem instances using the CPLEX solver under two different scenarios. Results demonstrate that increasing flexibility in transportation decisions (increasing the number of vehicles from one to two units) leads to a consistent improvement of 0.02% to 0.36% in the objective function value, without changing the number of accepted orders. These findings confirm the economic efficiency of the integration approach and the importance of incorporating environmental considerations in strategic supply chain decision-making. This research, by combining strategic, operational, and environmental decisions, provides a comprehensive framework for developing sustainable decision support systems in supply chain management.