Job Shop Scheduling Under Time-of-Use Electricity Prices

Job shop scheduling is an important problem in the operations of manufacturing systems and has great potential for improving electricity efficiency. This paper studies an energy-aware job shop scheduling problem that aims to minimize the total energy cost and total weighted tardiness by selecting an optimal job schedule, considering operation and other constraints. To efficiently model operation constraints and to consider different machine types, a new set of linear integer constraints is developed. The energy-aware job shop scheduling problem considering time-of-use (TOU) electricity prices is then formulated as a mixed-integer linear programming (MILP) problem and solved using the branch-and-cut method. Numerical results tested against three examples demonstrate the computational efficiency and solution quality of our method.