Name
Abstract | ||
|---|---|---|
Modern multiobjective algorithms can be computationally inefficient in producing good approximation sets for highly constrained many-objective problems. Such problems are common in real-world applications where decision-makers need to assess multiple conflicting objectives. Also, different instances of real-world problems often share similar fitness landscapes because key parts of the data are the same across these instances. We we propose a novel methodology that consists of solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We propose three goal-based objective functions and show that on a real-world home healthcare planning problem the methodology can produce improved results in a shorter computation time. |
Year | Venue | Field |
|---|---|---|
2018 | ICORES | Mathematical optimization,Fitness landscape,Computer science,Goal programming,Pareto principle,Computation |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rodrigo Lankaites Pinheiro | 1 | 9 | 3.23 |
| Dario Landa Silva | 2 | 316 | 28.38 |
| Wasakorn Laesanklang | 3 | 1 | 0.70 |
| Ademir Aparecido Constantino | 4 | 15 | 5.76 |