Skip to Main content Skip to Navigation
New interface
Conference papers

Solving Fuzzy Job-Shop Scheduling Problems with a Multiobjective Optimizer

Abstract : In real-world manufacturing environments, it is common to face a job-shop scheduling problem (JSP) with uncertainty. Among different sources of uncertainty, processing times uncertainty is the most common. In this paper, we investigate the use of a multiobjective genetic algorithm to address JSPs with uncertain durations. Uncertain durations in a JSP are expressed by means of triangular fuzzy numbers (TFNs). Instead of using expected values as in other work, we consider all vertices of the TFN representing the overall completion time. As a consequence, the proposed approach tries to obtain a schedule that optimizes the three component scheduling problems (corresponding to the lowest, most probable, and largest durations) all at the same time. In order to verify the quality of solutions found by the proposed approach, an experimental study was carried out across different benchmark instances. In all experiments, comparisons with previous approaches that are based on a single-objective genetic algorithm were also performed.
Document type :
Conference papers
Complete list of metadata

Cited literature [28 references]  Display  Hide  Download
Contributor : Thanh-Do Tran Connect in order to contact the contributor
Submitted on : Tuesday, August 26, 2014 - 9:40:04 AM
Last modification on : Thursday, January 20, 2022 - 5:27:54 PM
Long-term archiving on: : Tuesday, April 11, 2017 - 8:28:59 PM


Files produced by the author(s)



Thanh-Do Tran, Ramiro Varela, Inés González-Rodríguez, El-Ghazali Talbi. Solving Fuzzy Job-Shop Scheduling Problems with a Multiobjective Optimizer. The Fifth International Conference on Knowledge and Systems Engineering (KSE), Oct 2013, Hanoi, Vietnam. ⟨10.1007/978-3-319-02821-7_19⟩. ⟨hal-01058073⟩



Record views


Files downloads