Abstract | ||
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The pure flowshop scheduling problem is here investigated from a perspective considering me uncertainty associated with the execution of shop floor activities. Being the flowshop problem is NP complete, a large number of heuristic algorithms have been proposed in literature to determine an optimal solution. Unfortunately, these algorithms usually assume a simplifying hypothesis: the problem data are assumed as deterministic, i.e. job processing times and the due dates are expressed through a unique value, which does not reflect the real process variability. For this reason, some authors have recently proposed the use of a fuzzy set theory to model the uncertainty in scheduling problems. In this paper, a proper genetic algorithm has been developed for solving the fuzzy flowshop scheduling problem. The optimisation involves two different objectives: the completion time minimisation and the due date fulfilment; both the single and multi-objective configurations have been considered. A new ranking criterion has been proposed and its performance has been tested through a set of test problems. A numerical analysis confirms the efficiency of the proposed optimisation procedure. |
Year | DOI | Venue |
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2003 | 10.1142/S0218488503002466 | INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS |
Keywords | Field | DocType |
fuzzy scheduling, flowshop, optimisation algorithms | Heuristic,Mathematical optimization,Job shop scheduling,Fair-share scheduling,Fuzzy logic,Flow shop scheduling,Fuzzy set,Rate-monotonic scheduling,Dynamic priority scheduling,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 6 | 0218-4885 |
Citations | PageRank | References |
18 | 0.92 | 2 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
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Giovanni Celano | 1 | 92 | 12.38 |
Antonio Costa | 2 | 72 | 7.14 |
Sergio Fichera | 3 | 102 | 11.40 |