Interactive Fuzzy Multi-objective Ant Colony Optimization with Linguistically Quantified Decision Functions for Flexible Job Shop Scheduling Problems

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.

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