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1

New model of planning and scheduling for job-shop production system with energy consideration

100%

Hassani Z. I. M. , El Barkany A. , El Abbassi I. , Jabri A.

Management and Production Engineering Review

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2019

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tom Vol. 10, No. 1

89--97

EN

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.

2

Planning and scheduling of production system in conditioning line: industrial application, optimizationand simulation approach

88%

Majdoub Hassani Z. I. , El Barkany A. , El Abbassi I. , Jabri A. , Darcherif A. M.

Management and Production Engineering Review

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2019

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tom Vol. 10, No. 4

3--10

EN

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.

3

A Robust Control Strategy for Microgrid Energy Using Fuzzy Logic

88%

Meliani M. , El Barkany A. , El Abbassi I. , Absi R. , Jeffali F.

Management and Production Engineering Review

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2023

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tom Vol. 14, No. 1

127--136

EN

This paper highlights the storage charging and discharging issue. The study objective is to manage the energy inputs and outputs of the principal grid at the same time in order to maximize profit while decreasing costs, as well as to ensure the availability of energy according to demand and the decisions to either save or search for energy. A fuzzy logic control model is applied in MATLAB Simulink to deal with the system’s uncertainties in scheduling the storage battery technology and the charging- discharging. The results proved that the fuzzy logic model has the potential to efficiently lower fluctuations and prolong the lifecycle.

4

Optimizing electricity costs during integrated scheduling of jobs and stochastic preventive maintenanceunder time-of-use electricity tariffs

88%

Sadiqi A. , El Abbassi I. , El Barkany A. , Darcherif M. , El Biyaali A.

Management and Production Engineering Review

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2019

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tom Vol. 10, No. 4

123--132

EN

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.

5

A genetic algorithm and B&B algorithm for integrated production scheduling, preventiveand corrective maintenance to save energy

88%

Sadiqi A. , El Abbassi I. , El Barkany A. , Darcherif M. , El Biyaali A.

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tom Vol. 11, No. 4

138--148

EN

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.