Name
Abstract | ||
|---|---|---|
We consider the fuzzy job shop problem, where uncertain durations are modelled as fuzzy numbers and the objective is to minimise the expected makespan. A recent local search method from the literature has proved to be very competitive when used in combination with a genetic algorithm, but at the expense of a high computational cost. Our aim is to improve its efficiency with an alternative rescheduling algorithm and a makespan lower bound to prune non-improving neighbours. The experimental results illustrate the success of our proposals in reducing both CPU time and number of evaluated neighbours. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-14264-2_23 | CAEPIA |
Keywords | Field | DocType |
non-improving neighbour,expected makespan,recent local search method,fuzzy job shop problem,fuzzy number,improving local search,genetic algorithm,high computational cost,cpu time,alternative rescheduling algorithm,lower bound,local search | Memetic algorithm,Mathematical optimization,Job shop scheduling,CPU time,Job shop,Fuzzy logic,Flow shop scheduling,Local search (optimization),Fuzzy number,Mathematics | Conference |
Volume | ISSN | ISBN |
5988 | 0302-9743 | 3-642-14263-X |
Citations | PageRank | References |
2 | 0.36 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jorge Puente | 1 | 171 | 13.16 |
| Camino R. Vela | 2 | 346 | 31.00 |
| Alejandro Hernández-Arauzo | 3 | 27 | 2.20 |
| Inés González-Rodríguez | 4 | 115 | 10.73 |