1. Solving the multiple level warehouse layout problem using ant colony optimization
- Author
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Jean-Paul Arnaout, Caline ElKhoury, Gamze Karayaz, Işık Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, İşletme Bölümü, Işık University, Faculty of Economics and Administrative Sciences, Department of Management, and Karayaz, Gamze
- Subjects
0209 industrial biotechnology ,Mathematical optimization ,Multiple level warehouse layout problem ,Computer science ,Strategy and Management ,0211 other engineering and technologies ,Computational intelligence ,Metaheuristic ,02 engineering and technology ,Management Science and Operations Research ,Ant colony optimization ,020901 industrial engineering & automation ,Storage (materials) ,Management of Technology and Innovation ,Numerical Analysis ,021103 operations research ,Ant colony optimization algorithms ,Port (computer networking) ,Warehouse ,Storage material ,Computational Theory and Mathematics ,Modeling and Simulation ,Storage assignment ,Ground floor ,Statistics, Probability and Uncertainty ,Warehouses - Abstract
This paper addresses the multiple level warehouse layout problem, which involves assigning items to cells and levels with the objective of minimizing transportation costs. A monthly demand and an inventory requirement are associated with every item type along with vertical and horizontal unit transportation costs. The warehouse has one port to transport items vertically from ground floor to the other levels, where each item must be assigned to exactly one cell on the assigned level. An ant colony optimization (ACO) algorithm is adapted to this NP-complete problem and its performance is evaluated by comparing its solutions to the ones obtained using genetic algorithms (GA) as well as the optimal solutions for small problems. The computational results reflected the superiority of ACO in large-size problem instances, with a marginally better performance than GA in smaller ones, while solving the tested instances within a reasonable computational time. Furthermore, ACO was able to attain most of the known optimal solutions for small-size problem instances. Publisher's Version
- Published
- 2019