CNRS Production Engineering and Mechanical Production Laboratory

Metz, France

CNRS Production Engineering and Mechanical Production Laboratory

Metz, France

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Ezzeddine W.,CNRS Production Engineering and Mechanical Production Laboratory | Schutz J.,CNRS Production Engineering and Mechanical Production Laboratory | Rezg N.,CNRS Production Engineering and Mechanical Production Laboratory
Advances in Intelligent Systems and Computing | Year: 2015

Beds management is a complex problem in hospital centers. Many proposed models are based on reducing hospitalization cost and optimizing the use of resources. In this paper, a new model for bed planning is presented. This model takes into account the capacity of beds shared between two types of patients: scheduled and non-scheduled patients. Otherwise, the model manages also preventive maintenance actions during the planning horizon. © Springer International Publishing Switzerland 2015.


Li J.,CNRS Production Engineering and Mechanical Production Laboratory | Sava A.,CNRS Production Engineering and Mechanical Production Laboratory | Xie X.,Ecole Nationale Superieure des Mines de Saint - Etienne CMP
2007 European Control Conference, ECC 2007 | Year: 2015

This paper deals with inventory cost optimization of a multi-stage production-distribution system subject to non analytical constraints. These kind of constraints need to be evaluated by simulation. Therefore, the method that we propose addresses problems where both the performance evaluation and the constraints are evaluated via a stochastic, discrete-event simulation. It is based on random search in a neighborhood structure called the most promising area. A special attention is given to optimizing the allocation of the simulation budget. We show that under some assumptions, the algorithm converges to a set of local optimal solutions with probability 1. This approach is applied to cost optimization of a production-distribution system subject to fill rate specifications. Numerical experiments show that this new method can efficiently solve the discrete optimization problem proposed in this paper. © 2007 EUCA.


Schutz J.,CNRS Production Engineering and Mechanical Production Laboratory | Rezg N.,CNRS Production Engineering and Mechanical Production Laboratory
Computers and Industrial Engineering | Year: 2013

In case of leasing, the user rents equipment for a predetermined time. During this period, all maintenance actions are performed by the lessor. The aim of this research paper consists in determining an optimal maintenance policy for ensuring a minimum reliability, required by the customer. Two strategies are proposed: the first consists in performing preventive actions whenever the system reliability reaches a predefined reliability threshold. These actions are characterized by a reduction of the system age. The objective is therefore to determine the effectiveness factor of the optimal maintenance minimizing maintenance costs. For the second strategy, "improving" actions replace corrective actions during an interval to be determined to minimize maintenance costs. The first strategy will be solved using a numerical procedure and the second strategy uses an algorithm of discrete event simulation. © 2013 Elsevier Ltd. All rights reserved.


Dahane M.,CNRS Production Engineering and Mechanical Production Laboratory | Clementz C.,CNRS Production Engineering and Mechanical Production Laboratory | Rezg N.,CNRS Production Engineering and Mechanical Production Laboratory
Computers and Industrial Engineering | Year: 2010

This paper considers the problem of subcontracting constraints under a joint maintenance management and production control approach. Our objective is to study the effect of an unforeseen extension of subcontracting duration on a production system provider of subcontracting services. We will study a system with a production unit M subject to corrective maintenance actions, a result of random breakdowns. We perform a preventive maintenance action (PM) for each time T. Corrective and preventive maintenance have random durations. A buffer stock S with a capacity h, is built up with a production rate U in order to satisfy the constant demand d of the principal costumer, such as Umax > d. We note α = Umax - d, the maximum stock accumulation rate. In addition, the machine M is allocated to perform subcontracting tasks (ST) to a contractor production system, at each moment A1, for a useful duration A2, during which it is unavailable for our system. Thus, the machine must satisfy at the same time the constant demand (under a costumer-supplier relationship) and subcontracting tasks (under a contractor-subcontractor relationship). In this context, a mathematical model is developed to determine the impact of an unforeseen extension of subcontracting duration on the generated costs. We will determine the optimal values of T and h and we will discuss the impact of the extension of a ST with θ time units. © 2009 Elsevier Ltd. All rights reserved.


Sidibe I.B.,CNRS Production Engineering and Mechanical Production Laboratory | Adjallah K.H.,CNRS Production Engineering and Mechanical Production Laboratory
Advances in Safety, Reliability and Risk Management - Proceedings of the European Safety and Reliability Conference, ESREL 2011 | Year: 2012

Without any hypothesis, we develop in this paper a method for estimating 3-parameters Weibull based on the polynomial regression of lnln(1/R(t)). 1 Indeed, the theoretical difficulties in estimating of Weibull parameters often lead analysts to assume or to rely on information from experts in order to simplify parameters estimation to 1-2-parameters distributions. We explicitly show that the Weibull distribution have a polynomial behavior near a limit point t α. Afterward, we propose an analytical expression for each parameter of the distribution function. © 2012 Taylor & Francis Group.


Schutz J.,CNRS Production Engineering and Mechanical Production Laboratory
IEEE International Conference on Industrial Engineering and Engineering Management | Year: 2014

This paper deals with production and maintenance plans. Among a set of proposed requests, the production system must satisfy several of them. Each request is characterized by a profit, products quantity and delivery date. The choice of the requests to perform must allow to maximize net profit. To achieve this objective, preventive maintenance plans and optimal production rates associated with each request must be computed jointly. To solve this problem, a great deluge algorithm is used. A numerical example is also given to illustrate the proposed model. © 2014 IEEE.


Hajej Z.,CNRS Production Engineering and Mechanical Production Laboratory | Rezg N.,CNRS Production Engineering and Mechanical Production Laboratory | Gharbi A.,École de Technologie Supérieure of Montreal
International Journal of Advanced Manufacturing Technology | Year: 2016

This paper proposes a new ecological joint production and maintenance policy, which considers an ecological aspect for a forecasting problem of unreliable manufacturing system subject to degradation. The manufacturing system is composed of a single machine producing one product type in order to satisfy a random demand under a given service level. On the other hand, the production system generates harmful emissions to the environment and may be sanctioned by an environmental tax. The objective of this paper is to propose an ecological production and maintenance policy (EPMP) optimization by calling a subcontracting in the context of a calling of tenders in order to satisfy the random demand and to decrease the carbon tax. In this context, we determine the economical production plan and the optimal maintenance strategy by defining a condition for the subcontracting cost in order to respect the EPMP optimization. To illustrate the highlight of the proposed policy, some numerical results are presented. © 2016 Springer-Verlag London


Cormier G.,Alfaisal University | Rezg N.,CNRS Production Engineering and Mechanical Production Laboratory
Journal of Quality in Maintenance Engineering | Year: 2011

Purpose: The purpose of this study is to gain some insights into the number of shortages resulting from two alternative demand allocation schemes between a contractor (machine M 1) and subcontractor (machine M 2), on the one hand, and from inventory accumulation, on the other hand. The shortages stem from random machine breakdowns, and each machine undergoes preventive maintenance. The motivation behind inventory accumulation is to allow demand to be fulfilled even when both machines are down. Design/methodology/ approach: The number of shortages stemming from all scenarios under consideration was established via computer simulation with the Arena © language. Findings: For demand allocation that remains unchanged for the duration of the planning horizon and constant reliability of M 1, it was found that, the less reliable M 2 is, the more biased in favour of M 1 will be the optimal demand allocation and the greater will be the number of shortages. Moreover, both dynamic demand reallocation over the planning horizon and inventory accumulation result in a substantial reduction in shortages. Research limitations/implications: The results are representative of the specific data, which were assumed in the simulation models. Nevertheless, this methodology is recommended for this type of analysis, as it is highly flexible and can take into account many practical considerations, which an analytical approach cannot. Practical implications - Within the context of unreliable production machines, the most important practical implication of this study is that the dynamic reallocation of demand between a contractor and subcontractor, along with inventory accumulation, both have the potential to yield important reductions in the number of shortages. Originality/value: The subject-matter of this paper was not previously reported in the literature. Furthermore, the insights gained as a result of this study can yield substantial benefits to companies in terms of improving their service levels as measured by reduced shortages. © Emerald Group Publishing Limited 1355-2511.


Hajej Z.,CNRS Production Engineering and Mechanical Production Laboratory | Rezg N.,CNRS Production Engineering and Mechanical Production Laboratory | Gharbi A.,École de Technologie Supérieure of Montreal
International Journal of Production Research | Year: 2014

In this paper, a forecasting production/maintenance optimization problem has been proposed with a random demand and single machine M1 on a finite horizon. The function rate of the machine M1 is depending on the production rate for each period of the forecasting horizon. In order to satisfy the customer, a subcontracting assures the rest of the production through machine M2 with transportation delay. An analytic formulation of the problem has been proposed using a sequential computation of the optimal production plan for which an optimal preventive maintenance policy has been calculated based on minimal repair. Firstly, we find, the optimal production plans of principal and subcontracting machines, which minimises the total production and inventory cost for the cases without and with returned products under service level and subcontracting transportation delay. Secondly, we determine a joint effective maintenance policy with the optimal production plan, which integrates the various constraints for the production rates, the transportation delay and the returned production deadline. Numerical results are presented to highlight the application of the developed approach and sensitivity analysis shows the robustness of the model. © 2014 Taylor and Francis.


Bouguerra S.,University of Tunis | Chelbi A.,University of Tunis | Rezg N.,CNRS Production Engineering and Mechanical Production Laboratory
International Journal of Production Economics | Year: 2012

Numerous products are sold with a warranty period and the possibility of buying an extended warranty for a given additional cost. The buyer has then to decide to take the extended warranty or not when purchasing the product. We develop in this paper a mathematical model to study the opportunity provided by the extended warranty for the buyer as well as for the manufacturer. The total average cost incurred by each side during the product's life cycle is expressed in order to determine the maximum extra cost the consumer should pay and the minimum price at which the manufacturer should sell the extended warranty. This is done under different options in terms of maintenance strategies adopted during the product's lifecycle. © 2011 Elsevier B.V. All Rights Reserved.

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