Decision Support for Optimizing Maintenance of School Elevators

Lyu Feixue

Abstract

With its advanced technology and vast experience in elevator manufacturing, elevator maintenance has satisfied customers' safety, performance, riding comfort, and quietness. CA engineering elevators maintenance company operates around 30 nos. South schools' elevators in Singapore. With the development of the Industry 4.0 concept. It gives more possibilities for the elevator industry. A new methodology to take the learnings of these earlier maintenance systems and incorporate the benefits of the new systems and technologies we now have access to was needed to create a new efficient and effective practice for elevator repairs to enhance overall building efficiency. It is required to be in its safest and fastest response in maintenance. It is essential to practice correct maintenance quickly to keep the elevators to function safely and correctly. Besides, as elevators minimize the cost and maximize the benefit are required to optimize preventive maintenance operations. Each technician's route requires precise and optimized planning. The Ministry of Education (MOE) has a large number of schools with elevators that require maintenance in order to operate them. Increasing demand for maintenance service, increasing pressure to manage and mitigate them effectively for elevators contractors. The maintenance operations require many qualified technicians traveling from school to school daily to inspect, maintain, and repair elevators. They are also required to respond to emergencies such as breakdowns. The maintenance of elevators has to be done under the principle's operational reliability at minimum cost. However, no efficient and economic study has addressed the method in the elevator maintenance field properly. Hence, elevator contractors who are willing to invest in optimizing models do not spend much time and money receiving optimal results. This project aims to develop decision support models to help the optimal assignment and scheduling of technicians to maintenance tasks in a large number of schools. Case studies will be conducted based on data and scenarios from a selected MOE school zone. Elevator contractors are expected to apply and use the optimizing model at any other decision-making problem, especially for the issues with multi-criteria factors that require decision making. This study initiates by identifying the various factors(risks) of elevator contractors faced with the fishbone diagram tool. The available approaches are also listed down after this stage. Next, evaluate and quantify those factors through an analytic model - Analytic Hierarchy Process (AHP). A Python code with GLPL and Gurobi solver is built to generate the optimal solution for an overall view.