An Incentive-Based Decision-Support System for Sustainable Delivery Scheduling with GenAI-Assisted Interpretation

Companies are under increasing legal and societal pressure to reduce CO2 emissions from their delivery vehicles, while maximizing profit remains their prime objective. We study a problem where a company sends engineers with vehicles to customer sites to provide services. Customers request the service at their preferred time windows through a website or by calling a call center, and the company needs to allocate these service tasks to time windows and decide on how to schedule these tasks among its vehicles. We propose an approach to this problem that applies low-emission vehicle-scheduling techniques with dynamic pricing to reduce CO2 emissions and maximize profit. When a customer requests a service with a preferred time window, the company will provide the customer with different service time window options and their corresponding prices. Incentives are included in the prices to influence the customers to reduce CO2 emissions. Our approach solves the problem in two phases: the first phase solves time-dependent vehicle scheduling models with the objective of minimizing CO2 emissions, and the second phase solves a dynamic pricing model to maximize profit. Results show that our approach significantly reduces CO2 emissions and increases profits. A GenAI-based interpretation tool is used to translate the optimization outputs into actionable guidance for planners.