Multi-objective Batch Scheduling in Collaborative Multi-product Flow Shop System by using Non-dominated Sorting Genetic Algorithm

Batch scheduling is a well-known topic that has been studied widely with various objectives, methods, and circumstances. Unfortunately, batch scheduling in a collaborative flow shop system is still unexplored. All studies about batch scheduling that are found were in a single flow shop system where all arriving jobs come from single door. In a collaborative flow shop system, every flow shop handles its own customers although joint production among flow shops to improve efficiency is possible. This work aims to develop a novel batch scheduling model for a collaborative multi-product flow shop system. Its objective is to minimize make-span and total production cost. This model is developed by using non-dominated sorting genetic algorithm (NSGA II) which is proven in many multi objective optimization models. This model is then compared with the non-collaborative models which use NSGA II and adjacent pairwise interchange algorithm. Due to the simulation result, the proposed model performs better than the existing models in minimizing the make-span and total production cost. The make-span of the proposed model is 10 to 17 percent lower than the existing non-collaborative models. The total production cost of the proposed model is 0.3 to 3.5 percent lower than the existing non-collaborative models.

@article{Kusuma2021,
  title     = {Multi-objective Batch Scheduling in Collaborative Multi-product Flow Shop System by using Non-dominated Sorting Genetic Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {9},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Purba Daru Kusuma},
  doi       = {10.14569/IJACSA.2021.0120939},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120939}
}