Evolutionary Optimization in Master Production Scheduling: A Case Study
Over the last decade, the world has transformed from a marketplace with several large, almost independent market, to a highly integrated global market demanding a wide variety of products that comply with high quality, reliability, and environmental standards. Production scheduling system is one among the management tools that is widely used in manufacturing industries proving its capabilities and effectiveness through many success stories. Effective management of such production systems can be achieved by identifying customer service requirements, determining inventory levels, creating effective policies and procedures for the coordination of production activities. The aim of the current work is to benefit engineers, researchers and schedulers understand the factual nature of production scheduling in vibrant manufacturing systems and to reassure them to ponder how manufacture scheduling structures can be improved. A real world case of a large scale steel manufacturing industry is considered for this purpose and an evolutionary based meta-heuristic paradigm viz Teaching Learning Based Optimization method (TLBO) is used for creation of an effective Master Production Schedule (MPS) by the selection of optimum process parameters. The investigation on various data sets proved that the suggested algorithm is robust, automatic and efficient in finding an optimal master production schedule when compared to the currently followed conventional approach.
KeywordsMaster production scheduling Multi-objective optimization Discrete manufacturing Evolutionary algorithms Teaching-learning-based optimization
- 1.Wu, Z., Zhang, C., Zhu, X.: An ant colony algorithm for master production scheduling optimization. In: Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (2012)Google Scholar
- 3.Keesari, H.S., Rao, R.V: Optimization of job shop scheduling problems using teaching-learning-based optimization algorithm. OPSEARCH, 1–17 (2013)Google Scholar
- 9.Rao, R.: Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decis. Sci. Lett. 5(1), 1–30 (2016)Google Scholar