Optimizing the production scheduling of a single machine to minimize total energy consumption costs

• The fluctuations in energy prices have been considered to minimize energy consumption cost.
• A mathematical model to minimize energy consumption cost at machine level is proposed.
• The proposed algorithm for the model has the potential capability to be integrated into the factory scheduling.
• The high scalability of the algorithm allows running the production schedule in real time.

The rising cost of energy is one of the important factors associated with increased production costs at manufacturing facilities, which encourages decision-makers to tackle this problem in different manners. One important step in this trend is to reduce the energy consumption costs of production systems. Considering variable energy prices during one day, this paper proposes a mathematical model to minimize energy consumption costs for single machine production scheduling during production processes. By making decisions at machine level to determine the launch times for job processing, idle time, when the machine must be shut down, “turning on” time, and “turning off” time, this model enables the operations manager to implement the least expensive production scheduling during a production shift. To obtain ‘near’ optimal solutions, genetic algorithm technology has been utilized. Furthermore, to determine whether the heuristic solution provides the minimum cost and the best possible schedule for minimizing energy costs, an analytical solution has also been run to generate the optimal solution. Next, a comparison between the analytical solution and heuristic solutions is presented; for larger problems, the heuristic solution is preferable. The results indicate that significant reductions in energy costs can be achieved by avoiding high-energy price periods. This minimization process also has a positive environmental effect by reducing energy consumption during peak periods, which increases the possibility of reducing CO2 emissions from power generator sites.