An efficient genetic algorithm for the train scheduling problem with fleet management
| Autor: | Alexandre Mendes, Riley Clement, Claudio Sanhueza, Martin P. A. Jackson |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
Job shop scheduling Operations research business.industry Computer science Supply chain 05 social sciences 010501 environmental sciences 01 natural sciences Scheduling (computing) Rail transportation 0502 economics and business Genetic algorithm Train Coal business Selection (genetic algorithm) 0105 earth and related environmental sciences Fleet management |
| Zdroj: | CEC |
| DOI: | 10.1109/cec48606.2020.9185779 |
| Popis: | The Hunter Valley coal chain, located in New South Wales, Australia, is one of the most complex supply chains in the world. Coal orders are moved from the mines in the region to the terminals using a specific, complex rail infrastructure. These operations are scheduled by an experienced planning team at the Hunter Valley Coal Chain Coordinator. In this study, we propose an improved Genetic Algorithm to address the train scheduling problem. Our model considers several real-life operational constraints present in the coal supply chain and includes the selection of trains from an available fleet. Using a rail network with most of the real Hunter Valley railway infrastructure, we evaluate the strategy on test instances generated from actual train operations between 2017 and 2018. The objective of our strategy is to minimize total travel times. The algorithm was evaluated on instances with sizes between 60 and 180 jobs, and results show that the method can reach high-quality solutions - i.e. similar or better than those being currently used-in less than 2 minutes for the smaller instances, and 20 minutes for the larger ones. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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