2357. A Column Generation Algorithm for Solving the Multi-Model Green Logistics Problem
Invited abstract in session WB-17: Integer Programming, stream Combinatorial Optimization.
Wednesday, 10:30-12:00Room: Esther Simpson 2.08
Authors (first author is the speaker)
| 1. | Akane Seto
|
| Research & Development Group, Hitachi, Ltd. | |
| 2. | Takafumi CHIDA
|
| 3. | Stephen Maher
|
| GAMS Software GmbH | |
| 4. | Yuji Shinano
|
| Optimization, Zuse Institue Berlin |
Abstract
The logistics industry is focusing on reducing operational time and improving the quality of work for drivers by implementing strategies such as driver switching and container changes. Additionally, there is an effort to reduce carbon emissions by transitioning from petrol-consuming trucks to railways and electric vehicles (EVs). To achieve these goals, we propose the "Multi-Model Green Logistics Problem", which integrates five resources in long-haul transportation—drivers, trailer heads, trucks, containers, and trains—and two resources in local delivery— fixed-battery and replaceable-battery EVs. Given the complexity and scale of this problem, it is difficult to solve using conventional general-purpose solvers. Therefore, we have developed an algorithm that decomposes the problem into long-haul transportation and local delivery, employing column generation for each. The framework first solves the long-haul problem, then uses the solution to address the local delivery routing problem, iteratively refining the results. Our method synchronizes the five resources in long-haul transport by using time-expanded network. Additionally, calculation time is reduced by solving complex constraints such as charging times and amounts within sub-problems. Compared to conventional models using petrol trucks without driver switching and container changes, our approach significantly improves operational efficiency and reduces environmental impact.
Keywords
- Logistics
- Vehicle Routing
- Column Generation
Status: accepted
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