Abstract

This paper proposes an improvement of genetic algorithm (GA) for optimization of production planning and scheduling in the manufacturing industry. The problem is dynamic, combinatorial and multidimensional in nature with factors like production selection, production line allotment, manufacturing sequence, order quantity, etc., to be solved in sync. The efficiency and effectiveness of the proposed GA are demonstrated by a case study. Selection of parameters of the proposed GA is done using Taguchi experiment method. Performance comparison is done using six optimization solvers, namely pattern search solver, simulated annealing, tabu search, stochastic gradient descent, ant colony optimization and traditional genetic algorithm method.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.