In this article we present an industrial application of our mathematical model that integrates

planning and scheduling. Our main objective is to concretize our model and compare the

reel results with the theoretical ones. Our application is realized on a conditioning line of

pharmaceutical products at the ECAM EPMI production laboratory. For this reason and to

save time, we used Witness simulation tool. It gives an overall idea of how the line works,

the Makespan of each simulation and it highlights areas for improvement. We looked for

the best resulting sequence which corresponds to the minest Makespan and total production

cost. Then this sequence is applied on the conditioning line of pharmaceutical products for

simulation. On the other hand, we program our mathematical model with the parameters of

the conditioning line under python in version 3.6 and we adopt a simulation/optimization

coupling approach to verify our model.

This paper reports a new multi-item planning and scheduling problem in a job-shop production

system with the consideration of energy consumption. A mixed integer linear programming

is proposed to integrate planning and scheduling with the consideration of energy

aspect. In this study a new operational constraint is considered in the tactical level because

of the huge interest given to energy consumption and its strong link existing with production

system. To evaluate the performance of this model, computational experiments are

presented, and numerical results are given using the software CPLEX and then discussed.

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.