Name
Abstract | ||
|---|---|---|
The paper analyses the issues behind allocation and reordering strategies optimization for an existing automated warehouse for the steelmaking industry. Genetic Algorithms are employed to this purpose by deriving custom chromosome structures as well as ad-hoc crossover and mutation operators. A comparison between three different solutions capable to deal with multi-objective optimization are presented: the first approach is based on a common linear weighting function that combines different objectives; in the second one, a fuzzy system is used to aggregate objective functions, while in the last one the Strength Pareto Evolutionary Algorithm is applied in order to exploit a real multi-objective optimization. These three approaches are described and results are presented in order to highlight benefits and pitfalls of each technique. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3233/HIS-2010-0120 | Int. J. Hybrid Intell. Syst. |
Keywords | Field | DocType |
genetic algorithms,different objective,common linear weighting function,multi-objective optimization,different solution,ga-based solutions comparison,real multi-objective optimization,ad-hoc crossover,strength pareto evolutionary algorithm,warehouse storage optimization,aggregate objective function,reordering strategies optimization | Data mining,Weighting,Mathematical optimization,Crossover,Evolutionary algorithm,Computer science,Exploit,Multi-objective optimization,Fuzzy control system,Genetic algorithm,Pareto principle | Journal |
Volume | Issue | Citations |
7 | 4 | 2 |
PageRank | References | Authors |
0.37 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valentina Colla | 1 | 159 | 29.50 |
| Gianluca Nastasi | 2 | 25 | 4.61 |
| Nicola Matarese | 3 | 18 | 4.12 |
| Leonardo M. Reyneri | 4 | 52 | 9.12 |