Archives > Volume 15 | Number 4 | December 2020 > pp 377–389
Advances in Production Engineering & Management
Volume 15 | Number 4 | December 2020 | pp 377–389
A layered genetic algorithm with iterative diversification for optimization of flexible job shop scheduling problems
Amjad, M.K.; Butt, S.I.; Anjum, N.; Chaudhry, I.A.; Faping, Z.; Khan, M.
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A B S T R A C T
Flexible job shop scheduling problem (FJSSP) is a further expansion of the classical job shop scheduling problem (JSSP). FJSSP is known to be NP-hard with regards to optimization and hence poses a challenge in finding acceptable solutions. Genetic algorithm (GA) has successfully been applied in this regard since last two decades. This paper provides an insight into the actual complexity of selected benchmark problems through quantitative evaluation of the search space owing to their NP-hard nature. A four-layered genetic algorithm is then proposed and implemented with adaptive parameters of population initialization and operator probabilities to manage intensification and diversification intelligently. The concept of reinitialization is introduced whenever the algorithm is trapped in local minima till predefined number of generations. Results are then compared with various other standalone evolutionary algorithms for selected benchmark problems. It is found that the proposed GA finds better solutions with this technique as compared to solutions produced without this technique. Moreover, the technique helps to overcome the local minima trap. Further comparison and analysis indicate that the proposed algorithm produces comparative and improved solutions with respect to other analogous methodologies owing to the diversification technique.
A R T I C L E I N F O
Keywords • Scheduling; Flexible job shop scheduling problem (FJSSP); Complexity; Diversity; Combinatorial optimization; Genetic algorithm
Corresponding author • Amjad, M.K.
Article history • Received 19 May 2020, Revised 27 November 2020, Accepted 30 November 2020
Published on-line • 24 December 2020
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