Evolutionary Fleet Sizing in Static and Uncertain Environments with Shuttle Transportation Tasks-The Case Studies of Container Terminals [Application Notes]
| Autor: | Ian Jenkinson, Shayan Kavakeb, Trung Thanh Nguyen, Zaili Yang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
021103 operations research
Optimization problem Operations research Computer science 0211 other engineering and technologies Evolutionary algorithm Comparison results 02 engineering and technology Solver Sizing Theoretical Computer Science Travel time Artificial Intelligence Container (abstract data type) 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Simulation |
| Zdroj: | IEEE Computational Intelligence Magazine. 11:55-69 |
| ISSN: | 1556-603X |
| Popis: | This paper aims to identify the optimal number of vehicles in environments with shuttle transportation tasks. These environments are very common industrial settings where goods are transferred repeatedly between multiple machines by a fleet of vehicles. Typical examples of such environments are manufacturing factories, warehouses and container ports. One very important optimization problem in these environments is the fleet sizing problem. In real-world settings, this problem is highly complex and the optimal fleet size depends on many factors such as uncertainty in travel time of vehicles, the processing time of machines and size of the buffer of goods next to machines. These factors, however, have not been fully considered previously, leaving an important gap in the current research. This paper attempts to close this gap by taking into account the aforementioned factors. An evolutionary algorithm was proposed to solve this problem under static and uncertain situations. Two container ports were selected as case studies for this research. For the static cases, the state-of-the-art CPLEX solver was considered as the benchmark. Comparison results on realworld scenarios show that in the majority of cases the proposed algorithm outperforms CPLEX in terms of solvability and processing time. For the uncertain cases, a high-fidelity simulation model was considered as the benchmark. Comparison results on realworld scenarios with uncertainty show that in most cases the proposed algorithm could provide an accurate robust fleet size. These results also show that uncertainty can have a significant impact on the optimal fleet size. |
| Databáze: | OpenAIRE |
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