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Khatab A.,Metz National School of Engineering | Rezg N.,University of Lorraine | Ait-Kadi D.,Interuniversity Research Center on Entreprise Networks
Journal of Intelligent Manufacturing | Year: 2011

In this paper we consider the block replacement policy (BRP) for a system operating over a random time horizon. Under such a policy, a system is replaced by a new one either at failure or at a given time interval. The optimality criterion is the expected total replacements cost. Conditions under which an optimal replacement period exits are given. It is shown that BRP over an infinite time horizon is obtained as a particular case of the presentwork.Anumerical example is given to illustrate the proposed replacement model. © Springer Science+Business Media, LLC 2009.


Ouzineb M.,Interuniversity Research Center on Entreprise Networks | Ouzineb M.,University of Montréal | Nourelfath M.,Interuniversity Research Center on Entreprise Networks | Nourelfath M.,Laval University | And 2 more authors.
Journal of Heuristics | Year: 2011

This paper develops an efficient heuristic to solve the non-homogeneous redundancy allocation problem for multi-state series-parallel systems. Non identical components can be used in parallel to improve the system availability by providing redundancy in subsystems. Multiple component choices are available for each subsystem. The components are binary and chosen from a list of products available on the market, and are characterized in terms of their cost, performance and availability. The objective is to determine the minimal-cost series-parallel system structure subject to a multi-state availability constraint. System availability is represented by a multi-state availability function, which extends the binary-state availability. This function is defined as the ability to satisfy consumer demand that is represented as a piecewise cumulative load curve. A fast procedure is used, based on universal generating function, to evaluate the multi-state system availability. The proposed heuristic approach is based on a combination of space partitioning, genetic algorithms (GA) and tabu search (TS). After dividing the search space into a set of disjoint subsets, this approach uses GA to select the subspaces, and applies TS to each selected subspace. The design problem, solved in this study, has been previously analyzed using GA. Numerical results for the test problems from previous research are reported, and larger test problems are randomly generated. These results show that the proposed approach is efficient both in terms of both of solution quality and computational time, as compared to existing approaches. © 2009 Springer Science+Business Media, LLC.


Ouzineb M.,Interuniversity Research Center on Entreprise Networks | Ouzineb M.,University of Montréal | Nourelfath M.,Interuniversity Research Center on Entreprise Networks | Nourelfath M.,Laval University | And 2 more authors.
Computers and Operations Research | Year: 2010

This paper develops an efficient heuristic to solve two typical combinatorial optimization problems frequently met when designing highly reliable systems. The first one is the redundancy allocation problem (RAP) of series-parallel binary-state systems. The design goal of the RAP is to select the optimal combination of elements and redundancy levels to maximize system reliability subject to the system budget and to the system weight. The second problem is the expansion-scheduling problem (ESP) of multi-state series-parallel systems. In this problem, the study period is divided into several stages. At each stage, the demand is represented as a piecewise cumulative load curve. During the system lifetime, the demand can increase and the total productivity may become insufficient to assume the demand. To increase the total system productivity, elements are added to the existing system. The objective in the ESP is to minimize the sum of costs of the investments over the study period while satisfying availability constraints at each stage. The heuristic approach developed to solve the RAP and the ESP is based on a combination of space partitioning, genetic algorithms (GA) and tabu search (TS). After dividing the search space into a set of disjoint subsets, this approach uses GA to select the subspaces, and applies TS to each selected subspace. Numerical results for the test problems from previous research are reported and compared. The results show the advantages of the proposed approach for solving both problems. © 2009 Elsevier Ltd. All rights reserved.

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