This paper discusses the development of a multi-objective robust optimization model for facility layout design in the manufacturing systems. This proposed model considers the uncertainty of demand for each type of product. The facility layout design has three objectives, namely minimizing the expected total material handling cost, maximizing the total adjacency value between departments which is the functional relationship and adjacency requirements, and maximizing the utilization of the total area available. In addition, this proposed model considered the production process flow of each type of products, the value of adjacency between departments and the demand uncertainty for each type of product, such that the resulting facility layout is robust. The demand uncertainty for each type of products were expressed in the form of discrete scenarios with certain probabilities according to historical demand data. The demand uncertainty for each type of products has an impact on the uncertainty of the frequency of material movement between departments, which would affect the determination of the optimal position of one department against other departments. The proposed model of multi objective robust optimization for facility layout design had been solved using Gurobi Optimization - Python programming. Numerical experiment had been presented to show the results of applying the proposed model to assist facility planning managers in obtaining an efficient facility layout.

@inproceedings{oscm-2020-1796,
  title={MULTI OBJECTIVE ROBUST OPTIMIZATION MODEL FOR FACILITY LAYOUT DESIGN UNDER DEMAND UNCERTAINTY},
  year={2020},
  author={Dina Natalia Prayogo and Olyvia Novawanda},
  booktitle={Proceedings of the 10th International Conference on Operations and Supply Chain Management, 2020},
  doi={10.31387/oscm490264}
}

Dina Natalia Prayogo, Olyvia Novawanda (2020). MULTI OBJECTIVE ROBUST OPTIMIZATION MODEL FOR FACILITY LAYOUT DESIGN UNDER DEMAND UNCERTAINTY. In Proceedings of the 10th International Conference on Operations and Supply Chain Management, 2020. https://doi.org/10.31387/oscm490264