Bi-Objective Optimization for Uniform Parallel Batch Machine Scheduling under Time-of-Use Tariffs

TL;DR: In this paper , a new bi-objective uniform parallel batch machine scheduling problem with different job sizes under ToU tariffs is explored, with the goal of minimizing the total electricity cost and the number of enabled machines.

Abstract: Time-of-Use (ToU) electricity pricing scheme has been widely implemented to alleviate the grid's peak load, under which manufacturing companies obtain a good opportunity to save energy cost through more reasonable production scheduling. As a typical production system, batch processing machine manufacturing system has been widely used in modern manufacturing industry because of its advantages in improving production efficiency and reducing production costs. In this work, a new bi-objective uniform parallel batch machine scheduling problem with different job sizes under ToU tariffs is explored, with the goal of minimizing the total electricity cost and the number of enabled machines. We first establish a mixed integer linear programming model, and then propose an improved model. Both models are solved by CPLEX using the $\varepsilon$-constraint method. The calculation results of randomly generated instances prove the effectiveness of the proposed model. At the same time, the calculation results show that the improved model is more effective than the original one.