The rapid global economic development of the world economy depends on the availability of
substantial energy and resources, which is why in recent years a large share of non-renewable
energy resources has attracted interest in energy control. In addition, inappropriate use of
energy resources raises the serious problem of inadequate emissions of greenhouse effect gases,
with major impact on the environment and climate. On the other hand, it is important
to ensure efficient energy consumption in order to stimulate economic development and
preserve the environment. As scheduling conflicts in the different workshops are closely
associated with energy consumption. However, we find in the literature only a brief work
strictly focused on two directions of research: the scheduling with PM and the scheduling
with energy. Moreover, our objective is to combine both aspects and directions of in-depth
research in a single machine. In this context, this article addresses the problem of integrated
scheduling of production, preventive maintenance (PM) and corrective maintenance (CM)
jobs in a single machine. The objective of this article is to minimize total energy consumption
under the constraints of system robustness and stability. A common model for the integration
of preventive maintenance (PM) in production scheduling is proposed, where the sequence
of production tasks, as well as the preventive maintenance (PM) periods and the expected
times for completion of the tasks are established simultaneously; this makes the theory put
into practice more efficient. On the basis of the exact Branch and Bound method integrated on the CPLEX solver and the genetic algorithm (GA) solved in the Python software,
the performance of the proposed integer binary mixed programming model is tested and
evaluated. Indeed, after numerically experimenting with various parameters of the problem,
the B&B algorithm works relatively satisfactorily and provides accurate results compared
to the GA algorithm. A comparative study of the results proved that the model developed
was sufficiently efficient.
Time-of-use (TOU) electricity pricing has been applied in many countries around the world
to encourage manufacturers to reduce their electricity consumption from peak periods to
off-peak periods. This paper investigates a new model of Optimizing Electricity costs during
Integrated Scheduling of Jobs and Stochastic Preventive Maintenance under time of-use
(TOU) electricity pricing scheme in unrelated parallel machine, in which the electricity price
varies throughout a day. The problem lies in assigning a group of jobs, the flexible intervals
of preventive maintenance to a set of unrelated parallel machines and then scheduling of jobs
and flexible preventive maintenance on each separate machine so as to minimize the total
electricity cost. We build an improved continuous-time mixed-integer linear programming
(MILP) model for the problem. To the best of our knowledge, no papers considering both
production scheduling and Stochastic Preventive Maintenance under time of-use (TOU) electricity
pricing scheme with minimization total Electricity costs in unrelated parallel machine.
To evaluate the performance of this model, computational experiments are presented, and
numerical results are given using the software CPLEX and MATLAB with then discussed.