A fast taboo search algorithm for the job shop problem
Management Science
Computers and Industrial Engineering
Multi-objective scheduling of dynamic job shop using variable neighborhood search
Expert Systems with Applications: An International Journal
A multi-objective PSO for job-shop scheduling problems
Expert Systems with Applications: An International Journal
The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm
International Journal of Bio-Inspired Computation
IEEE Transactions on Evolutionary Computation
Intelligent water drops algorithm for rough set feature selection
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
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Multi-objective job shop scheduling (MOJSS) problems can be found in various application areas. The efficient solution of MOJSS problems has received continuous attention. In this research, a new meta-heuristic algorithm, namely the Intelligent Water Drops (IWD) algorithm is customized for solving the MOJSS problem. The optimization objective of MOJSS in this research is to find the best compromising solutions (Pareto non-dominance set) considering multiple criteria, namely makespan, tardiness and mean flow time of the schedules. MOJSS-IWD, which is a modified version of the original IWD algorithm, is proposed to solve the MOJSS problem. A scoring function which gives each schedule a score based on its multiple criteria values is embedded into the MOJSS-IWD's local search process. Experimental evaluation shows that the customized IWD algorithm can identify the Pareto non-dominance schedules efficiently.