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Contribution Overview to the Evaluation and Development of Spare Parts Management Models: Meta-Heuristic and Probabilistic Methods

Bounou Oumaima, El Barkany Abdellah, El Biyaali Ahmed

Management and Production Engineering Review

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2021

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Vol. 12, No. 1

24--37

EN

The presence of the spare parts stock is a necessity to ensure the continuity of services. The supply of spare parts is a special case of the global supply chain. The main objective of our research is to propose a global spare parts management approach which allows decision makers to determine the essential points in stock management. Thus, it is important for the stock manager to evaluate the system considered from time to time based on performance indicators. Some of these indicators are presented in the form of a dashboard. The presentation of this chapter chronologically traces the progress of our research work. In the first part, we present the work related to the forecast of spare parts needs through parametric and statistical methods as well as a Bayesian modelling of demand forecasting. To measure the appreciation of the supply of spare parts inventory, the second part focuses on work related to the evaluation of the performance of the spare parts system. Thus, we concretize the link between the management of spare parts and maintenance in the third part, more precisely, in the performance evaluation of the joint -management of spare parts and maintenance, in order to visualize the influence of parameters on the system. In the last section of this chapter, we will present the metaheuristic methods and their use in the management of spare parts and maintenance and make an analysis on work done in the literature.

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

Sadiqi Assia, El Abbassi Ikram, El Barkany Abdellah, Darcherif Moumen, El Biyaali Ahmed

Management and Production Engineering Review

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2020

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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.

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The performance evaluation of the spare parts management: case study

Oumaima Bounou, El Barkany Abdellah, El Biyaali Ahmed

Management and Production Engineering Review

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2019

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Vol. 10, No. 2

37--49

EN

The supply chain of spare parts is the intersection between the supply chain, the after-sales and the maintenance services. Some authors have tried to define improvement paths in terms of models to satisfy the performance criteria. In addition, other authors are directed towards the integration of risk management in the demand forecasting and the stock management (performance evaluation) through probabilistic models. Among these models, the probabilistic graphical models are the most used, for example, Bayesian networks and petri nets. Performance evaluation is done through performance indicators. To measure the appreciation of the supply of the spare parts stock, this paper focuses on the performance evaluation of the system by petri nets. This evaluation will be done through an analytical study. The purpose of this study is to evaluate and analyze the performance of the system by proposed indicators. First, we present a literature review on Petri nets which is the essential tool in our modeling. Secondly, we present in the third section the analytical study of the model based on bath deterministic and stochastic petri networks. Finally, we present an analysis of the proposed model compared to the existing ones.

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

Sadiqi Assia, El Abbassi Ikram, El Barkany Abdellah, Darcherif Moumen, El Biyaali Ahmed

Management and Production Engineering Review

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2019

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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.