個案資料

Atal Indore City Transport Service Limited operated Indore Bus Rapid Transit System, the main public transport system in the city of Indore, in the central Indian state of Madhya Pradesh. Indore Bus Rapid Transit System ran through an important section of the city, along a corridor 11.57 kilometres long with 21 bus stops. Operating the system involved meeting hourly demand for bus service—a task that required scheduling buses and their deployment to achieve the desired frequency. Indore Bus Rapid Transit System used modern technologies to identify passenger demand and the location of vehicles at all times during operation, and it used the data to plan and execute bus operations. Demand varied considerably between bus stops; therefore, management science techniques were needed for optimal planning. How could passenger service be maximized while optimizing the use of buses?

This exercise can be used in any undergraduate- or graduate-level course on operations management in a module dealing with process design and planning for a service operation. The exercise can also be used in an operations analytics and consulting course to work with a large-scale, real problem in the transportation sector. The use of spreadsheet modelling, spreadsheet simulation, and process analysis techniques like Little’s law can be explained as problem-solving tools. Participants should already be acquainted with techniques like simulation and queuing theory as used in operations management and related disciplines. The exercise introduces students to the challenges and options in operating large-scale public transportation services. Students will be able to use simple management science techniques such as Monte Carlo simulation and queuing theory. The use of modern technologies like global positioning systems and radio-frequency identification can be highlighted as a means for efficient capture of information and decision-making. The case also works well to demonstrate the concept of simulation and Little’s law for process planning. After completion of this exercise, students will be able to<ul><li>explore the significance of mass public transit in growing urban areas;</li><li>discuss various operations involved in managing a large-scale public transportation system;</li><li>learn about the use of modern technologies in managing complex operations;</li><li>explore competing objectives related to customer service and cost performance;</li><li>use the Monte Carlo simulation to determine bus frequency given passenger movement data; and</li><li>use queuing theory to determine the number of buses, scheduling, and deployment required to achieve the desired frequency of buses across each route.</li></ul>

• public transportation planning
• scheduling
• service operations