Just-in-time delivery for green fleets: A feedback control approach |
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| Institution: | 1. 1251 Memorial Drive 281, Coral Gables, FL 33146, Department of Industrial Engineering, University of Miami, FL, USA;2. 310 Leonhard Building, University Park, PA 16802, Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, PA, USA;1. Department of Computer Science and Information Engineering, National Chi Nan University, Taiwan #1, University Road, Pu-Li 545, Taiwan;2. Department of Information Management, National Chi Nan University, Taiwan #1, University Road, Pu-Li 545, Taiwan;1. Department of International Business and Asian Studies, Griffith Business School, Gold Coast Campus, Griffith University, QLD 4222, Australia;2. Department of Industrial Management, University of Tehran, Tehran, Iran;3. Department of Industrial Management, Faculty of Management and Accounting, Karaj Branch, Islamic Azad University, P.O. Box: 31485-313, Karaj, Iran;1. Department of Transportation and Communication Management Science and the Research Center for Energy Technology and Strategy, National Cheng Kung University, No. 1, University Road, Tainan 70101, Taiwan;2. Department of Transportation and Communication Management Science, National Cheng Kung University, No. 1, University Road, Tainan 70101, Taiwan;1. Department of Civil Engineering and Applied Mechanics, McGill University, 817 Sherbrooke St. W., Room 492, Montréal, Québec H3A 2K6, Canada;2. Department of Sociology, University of New Brunswick, New Brunswick, Canada;3. Department of Medicine, McGill University, Division of Clinical Epidemiology, McGill University Health Centre, QC H3A 1A1, Canada;4. Department of Geography, McGill University, 805 Sherbrooke St. W., Montreal, Quebec H3A 2K6, Canada;5. Department of Epidemiology and Biostatistics, McGill University, 805 Sherbrooke St. W., Montreal, Quebec H3A 2K6, Canada;6. Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Drive, Room 301D, Orlando, FL 32816, USA;7. Department of Civil Engineering, University of Toronto, 35 St George Street, Toronto, ON M5S 1A4, Canada;1. Departamento de Urbanismo, Universitat Politècnica de València, Valencia, España;2. Institut Cartogràfic Valencià, Valencia, España;3. Departamento de Alergia, IIS Hospital La Fe, Valencia, España;1. Department of Economics, Rochester Institute of Technology, 92 Lomb Memorial Drive, Rochester, NY 14623-5604, USA;2. Department of Spatial Economics, VU University, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands;3. A. Mickiewicz University, Poznan, Poland |
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| Abstract: | 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. |
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| Keywords: | Fuel consumption Just-in-time delivery Vehicle routing problem with soft time windows Feedback control |
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