Solving the Integrated Multi-Port Stowage Planning and Container Relocation Problems with a Genetic Algorithm and Simulation
The greater flow of containers in global supply chains requires ever-increasing productivity at port terminals. The research found in the literature has focused on optimizing specific parts of port operations but has ignored important features, such as the stack-wise organization of containers in a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8191 |
| _version_ | 1797411303630831616 |
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| author | Catarina Junqueira Anibal Tavares de Azevedo Takaaki Ohishi |
| author_facet | Catarina Junqueira Anibal Tavares de Azevedo Takaaki Ohishi |
| author_sort | Catarina Junqueira |
| collection | DOAJ |
| description | The greater flow of containers in global supply chains requires ever-increasing productivity at port terminals. The research found in the literature has focused on optimizing specific parts of port operations but has ignored important features, such as the stack-wise organization of containers in a container ship and port yard and the effects of interconnection among the operations in both places. The objective of this paper is to show the importance of designing an integrated plan of the container relocation problem at the port yard with the stowage planning problem for loading and unloading a ship through ports. Both individual problems are NP-Complete, and exact approaches for each problem are only able to find optimal or feasible solutions for small instances. We describe a simulation-optimization methodology that combines simulation, a genetic algorithm, and a new solution representation based on rules. The test results show that the solution from the integrated plan is mutually beneficial for port yards and ship owners. |
| first_indexed | 2024-03-09T04:44:07Z |
| format | Article |
| id | doaj.art-9260b4e3b1754ebbba6f5fcd2ae39eb9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:44:07Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9260b4e3b1754ebbba6f5fcd2ae39eb92023-12-03T13:17:48ZengMDPI AGApplied Sciences2076-34172022-08-011216819110.3390/app12168191Solving the Integrated Multi-Port Stowage Planning and Container Relocation Problems with a Genetic Algorithm and SimulationCatarina Junqueira0Anibal Tavares de Azevedo1Takaaki Ohishi2School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-852, BrazilSchool of Applied Sciences, University of Campinas, Limeira 13484-350, BrazilSchool of Electrical and Computer Engineering, University of Campinas, Campinas 13083-852, BrazilThe greater flow of containers in global supply chains requires ever-increasing productivity at port terminals. The research found in the literature has focused on optimizing specific parts of port operations but has ignored important features, such as the stack-wise organization of containers in a container ship and port yard and the effects of interconnection among the operations in both places. The objective of this paper is to show the importance of designing an integrated plan of the container relocation problem at the port yard with the stowage planning problem for loading and unloading a ship through ports. Both individual problems are NP-Complete, and exact approaches for each problem are only able to find optimal or feasible solutions for small instances. We describe a simulation-optimization methodology that combines simulation, a genetic algorithm, and a new solution representation based on rules. The test results show that the solution from the integrated plan is mutually beneficial for port yards and ship owners.https://www.mdpi.com/2076-3417/12/16/8191port logisticssimulation-optimizationgenetic algorithmevolutionary strategies |
| spellingShingle | Catarina Junqueira Anibal Tavares de Azevedo Takaaki Ohishi Solving the Integrated Multi-Port Stowage Planning and Container Relocation Problems with a Genetic Algorithm and Simulation Applied Sciences port logistics simulation-optimization genetic algorithm evolutionary strategies |
| title | Solving the Integrated Multi-Port Stowage Planning and Container Relocation Problems with a Genetic Algorithm and Simulation |
| title_full | Solving the Integrated Multi-Port Stowage Planning and Container Relocation Problems with a Genetic Algorithm and Simulation |
| title_fullStr | Solving the Integrated Multi-Port Stowage Planning and Container Relocation Problems with a Genetic Algorithm and Simulation |
| title_full_unstemmed | Solving the Integrated Multi-Port Stowage Planning and Container Relocation Problems with a Genetic Algorithm and Simulation |
| title_short | Solving the Integrated Multi-Port Stowage Planning and Container Relocation Problems with a Genetic Algorithm and Simulation |
| title_sort | solving the integrated multi port stowage planning and container relocation problems with a genetic algorithm and simulation |
| topic | port logistics simulation-optimization genetic algorithm evolutionary strategies |
| url | https://www.mdpi.com/2076-3417/12/16/8191 |
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