Job Shop Scheduling Using Bat Algorithm

Abstract

The Job shop scheduling problems are used to identify the time at which each operation is to be processed on each machine. Then the calculation becomes more complicated. There are many methods available for solving the Job Shop Scheduling Problems (JSSP). Among these some are based on job priority rules and others are based on combinatorial optimization using meta-heuristics. Hence many single pass heuristics were developed to get a reasonably good solution. But these methods are applicable only for a single objective. Later on many Meta-heuristics were developed. These meta-heuristics are applicable for both single objectives and also for multi-objectives. Meta heuristic algorithms have proved most efficient algorithms to solve various JSSP so far. Meta-heuristics do not give guarantee optimal solutions but reasonably good solutions can be found for large sized problems. By using meta-heuristics we can get a better solution. In this paper, an Improved Bat Algorithm was developed to solve job shop scheduling problems. Hence computer programming is necessary. A program for the bat algorithm is written in 'C' language and the robustness of this bat algorithm is tested on 10 benchmark JSSP instances. The results show that the proposed approach performs in terms of solution quality and computational efficiency.

Cite as

Dr. O. Mahesh, M. Narasimha Rao, B. Vivek Sindhoor, R. Pavan Kalyan, & K. Mohan Krishna. (2023). Job Shop Scheduling Using Bat Algorithm. Journal of Advanced Research in Industrial Engineering, 5(1), 1–8. https://doi.org/10.5281/zenodo.7821275