Just-in-time delivery for green fleets: A feedback control approach

• Published in: Transportation research. Part D, Transport and environment Transport and environment, 2016-07, Vol.46, p.229-245
• Main Authors: Lee, Seokgi, Prabhu, Vittaldas V.
• Format: Article
• Language: English
• Subjects: Carbon dioxide; Emissions control; Feedback control; Fuel consumption; Fuels; Just-in-time delivery; Route planning; Searching; Transportation; Vehicle routing problem with soft time windows; Vehicles
• ISSN: 1361-9209; 1879-2340
• DOI: 10.1016/j.trd.2016.04.005

•Dynamic models for controlling vehicle speed and the departure time are developed.•just-in-time and fuel performances are examined for VRPSTW.•Impact of load weight and JIT penalty cost on a routing schedule is examined.•Drivers can flexibly change the routing schedules considering JIT and fuel performance. With increasing attention being paid to greenhouse gas (GHG) emissions, the transportation industry has become an important focus of approaches to reduce GHG emissions, especially carbon dioxide equivalent (CO2e) emissions. In this competitive industry, of course, any new emissions reduction technique must be economically attractive and contribute to good operational performance. In this paper, a continuous-variable feedback control algorithm called GEET (Greening via Energy and Emissions in Transportation) is developed; customer deliveries are assigned to a fleet of vehicles with the objective function of Just-in-Time (JIT) delivery and fuel performance metrics akin to the vehicle routing problem with soft time windows (VRPSTW). GEET simultaneously determines vehicle routing and sets cruising speeds that can be either fixed for the entire trip or varied dynamically based on anticipated performance. Dynamic models for controlling vehicle cruising speed and departure times are proposed, and the impact of cruising speed on JIT performance and fuel performance are evaluated. Allowing GEET to vary cruising speed is found to produce an average of 12.0–16.0% better performance in fuel cost, and −36.0% to +16.0% discrepancy in the overall transportation cost as compared to the Adaptive Large Neighborhood Search (ALNS) heuristic for a set of benchmark problems. GEET offers the advantage of extremely fast computational times, which is a substantial strength, especially in a dynamic transportation environment.