Archive for category Optimization

Facility layout problem, especially with the unequal departmental area (UAFLP), is one of the problems studied in combinatorial optimization and has received the attention of many researchers in the past decade. The goal of UAFLP is to allocate departments into a facility to obtain the most efficient arrangement. The UAFLP study has a final objective to minimize the total cost of material handling between departments.

Competition in today’s business world is inevitable. Increasing the quality of productivity becomes one of the keys to success in facing competition with business management effectively and efficiently. This can happen by maximizing existing resources ie employees, machines, and other facilities. Therefore, the industry needs to be able to optimize production capability and effectiveness to face competitors. The production process becomes the key that needs to be managed effectively to minimize production costs with higher effectiveness.

Facility layout design has a close reinforcement to the size of the physical arrangement of elements in a manufacturing and service system, such as department, machinery, operational tools, and so on. The purpose of the facility layout design is the design with the minimum material handling cost. With proper facility layout, material handling costs can be reduced. In general, material handling contributes about 20-50 percent of the total. Reduction in the company’s operational costs, along with the increased efficiency of the production system becomes a necessity that every industry needs to do.

The problem of facilities layout that is often the researcher’s attention is Unequal Area Facility Layout Problem (UAFLP). Initially, UA-FLP was developed by Armor and Buffa (1963). They explain that there is a rectangular facility with a fixed Width and Height and several (n) departments that need to be allocated to the facility. There are some problems that have not found the optimal solution and require a long computation time. The goal is to reduce non-feasible solutions to reduce the complexity of possible solutions.

This research will develop a mathematical model using Mixed Integer Programming method based on Flexible Bay Structure. Some additional constraint functions will be attempted to be added to the model. Testing is done by comparing the effect of each additional constraint function that has different approaches in cutting the complexity of possible solutions. The comparison result of the combination of the constraint function used indicates which constraint function has a major influence in reducing the computation time of the model.

This research concludes that all simulated problem sets using additional constraints in the model can provide better computation time than before. The effect that occurs can be the value of the solution becomes not optimum or remain optimum. All computational time gives significant improvement.

This research is conducted by Randa Adi Saputra and Komaruddin