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Content Provider | IEEE Xplore Digital Library |
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Author | Li-Ning Xing Ying-Wu Chen Ke-Wei Yang |
Copyright Year | 2007 |
Description | Author affiliation: Dept. of Manage. Sci. & Eng., Nat. Univ., Changsha (Li-Ning Xing; Ying-Wu Chen; Ke-Wei Yang) |
Abstract | Scheduling for the flexible job shop is very important in the fields of production management and combinatorial optimization. It proposes an Ant Colony Optimization with Linguistically Quantified Decision Functions (ACO-LQDF) for the Flexible Job Shop Scheduling Problems (FJSSP) in this work. The novelty of the proposed approach is the interactive and fuzzy multi-objective nature of the Ant Colony Optimization (ACO) that considers the aspiration levels set by the decision maker (DM) for the objectives. The ACO's decision function is a linguistically quantified statement about acceptable distances between achieved objective values and aspiration levels. Linguistic quantifiers are represented by means of fuzzy sets. Our computational investigation indicates that this approach can tackle multi-objective FJSSP effectively. |
Starting Page | 801 |
Ending Page | 806 |
File Size | 1244430 |
Page Count | 6 |
File Format | |
ISBN | 9780769529998 |
DOI | 10.1109/FBIT.2007.18 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-10-11 |
Publisher Place | South Korea |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Ant colony optimization Fuzzy sets Job shop scheduling Technology management Processor scheduling Engineering management Optimized production technology Simulated annealing Dynamic scheduling Manufacturing |
Content Type | Text |
Resource Type | Article |
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