Scheduling trucks transportation in supply chain regarding cross docking using meta-heuristic algorithms
In today’s competitive world, the organizations decide to establish competitive benefits by making benefit from management sciences. One of the most important management sciences arisen lots of so useful matters is the supply chain. The supply chain management is the evolved result of warehousing ma...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
Allameh Tabataba'i University Press
2014-12-01
|
| Series: | Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī |
| Subjects: | |
| Online Access: | https://jims.atu.ac.ir/article_191_2ae16230b21c222d33c5cfb29082bf5b.pdf |
| _version_ | 1797368883236044800 |
|---|---|
| author | Ali Mohtashami Ali Fallahian-Najafabadi |
| author_facet | Ali Mohtashami Ali Fallahian-Najafabadi |
| author_sort | Ali Mohtashami |
| collection | DOAJ |
| description | In today’s competitive world, the organizations decide to establish competitive benefits by making benefit from management sciences. One of the most important management sciences arisen lots of so useful matters is the supply chain. The supply chain management is the evolved result of warehousing management and is regarded as one of the infrastructure and important concepts for implementing the career so that in many of them it is essentially tried to shorten the time between the customer’s order and the real time of delivering the goods. Cross docking is one of the most important alternatives for lowering the time in supply chain. The central aim of this paper is to focus on optimizing the planning of the trucks input and output aiming to minimize total time of operation inside the supply chain in designed model. Timing the transportation in this paper makes the time between sources and destinations, time of unloading and transferring the products minimized. To find the optimum answers to the question, genetic algorithms and the particle swarm optimization have been used. Then, these algorithms have been compared with the standards such as the implementation time and quality of answers with each other and then better algorithms in each standard identified. |
| first_indexed | 2024-03-08T17:39:05Z |
| format | Article |
| id | doaj.art-8cdd57573374433c8ecc40105a937cd8 |
| institution | Directory Open Access Journal |
| issn | 2251-8029 2476-602X |
| language | fas |
| last_indexed | 2024-03-08T17:39:05Z |
| publishDate | 2014-12-01 |
| publisher | Allameh Tabataba'i University Press |
| record_format | Article |
| series | Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī |
| spelling | doaj.art-8cdd57573374433c8ecc40105a937cd82024-01-02T11:15:51ZfasAllameh Tabataba'i University PressMuṭāli̒āt-i Mudīriyyat-i Ṣan̒atī2251-80292476-602X2014-12-0111315584191Scheduling trucks transportation in supply chain regarding cross docking using meta-heuristic algorithmsAli Mohtashami0Ali Fallahian-Najafabadi1Assistant Professor, Department of industrial management, Qazvin Branch, Islamic Azad University, Qazvin, IranDepartment of industrial management, Qazvin Branch, Islamic Azad University, Qazvin, IranIn today’s competitive world, the organizations decide to establish competitive benefits by making benefit from management sciences. One of the most important management sciences arisen lots of so useful matters is the supply chain. The supply chain management is the evolved result of warehousing management and is regarded as one of the infrastructure and important concepts for implementing the career so that in many of them it is essentially tried to shorten the time between the customer’s order and the real time of delivering the goods. Cross docking is one of the most important alternatives for lowering the time in supply chain. The central aim of this paper is to focus on optimizing the planning of the trucks input and output aiming to minimize total time of operation inside the supply chain in designed model. Timing the transportation in this paper makes the time between sources and destinations, time of unloading and transferring the products minimized. To find the optimum answers to the question, genetic algorithms and the particle swarm optimization have been used. Then, these algorithms have been compared with the standards such as the implementation time and quality of answers with each other and then better algorithms in each standard identified.https://jims.atu.ac.ir/article_191_2ae16230b21c222d33c5cfb29082bf5b.pdfsupply chain managementcross dockinggenetic algorithmparticle swarm optimization algorithm |
| spellingShingle | Ali Mohtashami Ali Fallahian-Najafabadi Scheduling trucks transportation in supply chain regarding cross docking using meta-heuristic algorithms Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī supply chain management cross docking genetic algorithm particle swarm optimization algorithm |
| title | Scheduling trucks transportation in supply chain regarding cross docking using meta-heuristic algorithms |
| title_full | Scheduling trucks transportation in supply chain regarding cross docking using meta-heuristic algorithms |
| title_fullStr | Scheduling trucks transportation in supply chain regarding cross docking using meta-heuristic algorithms |
| title_full_unstemmed | Scheduling trucks transportation in supply chain regarding cross docking using meta-heuristic algorithms |
| title_short | Scheduling trucks transportation in supply chain regarding cross docking using meta-heuristic algorithms |
| title_sort | scheduling trucks transportation in supply chain regarding cross docking using meta heuristic algorithms |
| topic | supply chain management cross docking genetic algorithm particle swarm optimization algorithm |
| url | https://jims.atu.ac.ir/article_191_2ae16230b21c222d33c5cfb29082bf5b.pdf |
| work_keys_str_mv | AT alimohtashami schedulingtruckstransportationinsupplychainregardingcrossdockingusingmetaheuristicalgorithms AT alifallahiannajafabadi schedulingtruckstransportationinsupplychainregardingcrossdockingusingmetaheuristicalgorithms |