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Paper Information

Journal:   JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL   2019 , Volume 15 , Number 1; Page(s) 181 To 192.
 
Paper: 

An integrated approach for scheduling flexible job-shop using teaching– learning-based optimization method

 
 
Author(s):  BUDDALA RAVITEJA*, MAHAPATRA SIBA SANKAR
 
* Department of Mechanical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha 769008, India
 
Abstract: 
In this paper, teaching– learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic jobshop scheduling problem. There are two sub problems in FJSP. They are routing problem and sequencing problem. If both the sub problems are solved simultaneously, then the FJSP comes under integrated approach. Otherwise, it becomes a hierarchical approach. Very less research has been done in the past on FJSP problem as it is an NP-hard (non-deterministic polynomial time hard) problem and very difficult to solve till date. Further, very less focus has been given to solve the FJSP using an integrated approach. So an attempt has been made to solve FJSP based on integrated approach using TLBO. Teaching– learning-based optimization is a meta-heuristic algorithm which does not have any algorithm-specific parameters that are to be tuned in comparison to other meta-heuristics. Therefore, it can be considered as an efficient algorithm. As best student of the class is considered as teacher, after few iterations all the students learn and reach the same knowledge level, due to which there is a loss in diversity in the population. So, like many meta-heuristics, TLBO also has a tendency to get trapped at the local optimum. To avoid this limitation, a new local search technique followed by a mutation strategy (from genetic algorithm) is incorporated to TLBO to improve the quality of the solution and to maintain diversity, respectively, in the population. Tests have been carried out on all Kacem’ s instances and Brandimarte’ s data instances to calculate makespan. Results show that TLBO outperformed many other algorithms and can be a competitive method for solving the FJSP.
 
Keyword(s): Flexible job shop scheduling ,Local search ,Makespan ,Meta-heuristics ,Teaching–,learning-based optimization
 
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