Dynamic Manufacturing Scheduling Under Real-Time Electricity Pricing Based on MILP and ARIMA

The scheduling of manufacturing equipment is critical in production facilities. Research on production scheduling has traditionally focused on component throughput and cycle time. However, the increase of electricity price in the United States following the market deregulation in 1990s has led to efforts to reduce energy cost via manufacturing scheduling. This paper explores the possibility of reducing electricity cost of a manufacturing facility subject to real time electricity pricing by dynamically changing operation schedules, while maintaining a pre-determined production throughput. A time series model is developed to forecast the hourly electricity price and time-indexed integer programming is used to determine the manufacturing schedule. The electricity price forecast is updated every hour based on the price history, and manufacturing schedule is updated according to the updated price forecast. A hypothetical flow line with 3 processes operating 16 hours per day is used as a case study. The line has a limited public buffer between processes and all machines in the shop have three operational states. With a throughput of 60 parts per day, the results suggest that it is possible to reduce the cost by 3.6% using an hourly forecast compared with a schedule based on a day-ahead price forecast.