A genetic algorithm to solve the storage space allocation problem in a container terminal |
| |
| Authors: | Mohammad Bazzazi Nima Safaei Nikbakhsh Javadian |
| |
| Affiliation: | 1. Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran;2. Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Ont., Canada M5S 3G8;1. Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Baylor College of Medicine and Texas Children’s Hospital, Houston, TX;2. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Baylor College of Medicine and Texas Children’s Hospital, Houston, TX;3. Department of Pathology, Baylor College of Medicine and Texas Children’s Hospital, Houston, TX;4. Department of Anesthesiology and Obstetric and Gynecologic Anesthesiology, Texas Children’s Hospital, Houston, TX;5. Department of Neonatology, Texas Children’s Hospital, Houston, TX;6. Department of Radiology, Texas Children’s Hospital, Houston, TX;7. Operating Room, Texas Children’s Hospital, Houston, TX;8. Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX;9. Department of Urology, Baylor College of Medicine, Houston, TX;1. University of Puerto Rico – Mayagüez, Industrial Engineering Department, Call Box 9000, Mayagüez, Puerto Rico 00681-9000;2. University of Groningen, Faculty of Economics and Business, Department of Operations, P.O. Box 800, 9700 AV Groningen, The Netherlands;1. College of Management, Shenzhen University, Shenzhen 518060, China;2. Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong;3. Department of Business Management, Shenzhen Institute of Information Technology, Shenzhen 518172, China;1. Logistics Engineering and Simulation Laboratory, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China;2. Department of Industrial Engineering, Tsinghua University, Beijing 100084, China;3. Business Analytics and Optimization, University of Phoenix, Apollo Group, Inc., Phoenix, AZ 85040, USA |
| |
| Abstract: | In this paper, an efficient genetic algorithm (GA) is presented to solve an extended storage space allocation problem (SSAP) in a container terminal. The SSAP is defined as the temporary allocation of the inbound/outbound containers to the storage blocks at each time period with aim of balancing the workload between blocks in order to minimize the storage/retrieval times of containers. An extended version of a SSAP proposed in the literature is considered in this paper in which the type of container affects on making the decision on the allocation of containers to the blocks. In real-world cases, there are different types (as well as different sizes) of containers consisting of several different goods such as regular, empty and refrigerated containers. The extended SSAP is solved by an efficient GA for real-sized instances. Because of existing the several equality constraints in the extended model, the implementation of the GA in order to quick and facilitate achieve to the feasible solutions is one of the outstanding advantages of this paper. The performance of the extended model and proposed GA is verified by a number of numerical examples. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
