PENGEMBANGAN MODEL MATEMATIS DAN ALGORITMA GENETIK UNTUK MENYELESAIKAN WASTE COLLECTION ROUTING PROBLEM

• Main Author: MARIAH
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/39532
• Institution: Institut Teknologi Bandung

Health care facilities are one of facilities that cannot be separated from human life. But on the other hand, health care facilities also produce medical waste that is harmful to living things and the environment. Medical waste is currently annihilated by incineration and it usually conducted outside of health care facilities by medical waste processing companies. Because the treatment is in different locations, waste collection must be carried out from medical healthcare facilities to the incineration site. Collection of waste for the company itself requires a high cost. On the other hand, improper collection of waste can also lead to a buildup of waste in the waste processing location. Even though the collected waste has a safe time limit for storage where the waste must be treated immediately before the deadline so as not to cause pollution and endanger other living things. It is necessary to determine the appropriate collection route to solve this problem. Therefore, in this study, a mathematical model was developed to solve the problem of determining the Waste Collection Routing Problem. The mathematical model development is done by defining the study system, the relevant system, then looking for the relationship of aspects of the relevant system and modeling it while referring to the reference model. The model that has been developed is then verified and validated. This study also develops genetic algorithms to solve problems on a larger scale. This is because the models developed is one of NP Hard problems that their use is limited to small scale. The algorithms developed were then tested and verified. The measurement of algorithm performance is done by comparing the value of the objective function obtained from the mathematical model and from the genetic algorithm, and the algorithm performance value is 98.54%. The developed algorithm is also tested to be applicated in real systems at PT Jasa Medivest.