The objective of the cellular manufacturing is to simplify the management of the manufacturing industries. In regrouping the production of different parts into clusters, the management of the manufacturing is reduced to manage different small entities. One of the most important problems in the cellular manufacturing is the design of these entities called cells. These cells represent a cluster of machines that can be dedicated to the production of one or several parts. The ideal design of a cellular manufacturing is to make these cells totally independent from one another, i.e. that each part is dedicated to only one cell (i.e. if it can be achieved completely inside this cell). The reality is a little more complex. Once the cells are created, there exists still some traffic between them. This traffic corresponds to a transfer of a part between two machines belonging to different cells. The final objective is to reduce this traffic between the cells (called inter-cellular traffic). Different methods exist to produce these cells and dedicated them to parts. To create independent cells, the choice can be done between different ways to produce each part. Two interdependent problems must be solved: • the allocation of each operation on a machine: each part is defined by one or several sequences of operations and each of them can be achieved by a set of machines. A final sequence of machines must be chosen to produce each part. • the grouping of each machine in cells producing traffic inside and outside the cells. In function of the solution to the first problem, different clusters will be created to minimise the inter-cellular traffic. In this thesis, an original method based on the grouping genetic algorithm (Gga) is proposed to solve simultaneously these two interdependent problems. The efficiency of the method is highlighted compared to the methods based on two integrated algorithms or heuristics. Indeed, to form these cells of machines with the allocation of operations on the machines, the used methods permitting to solve large scale problems are generally composed by two nested algorithms. The main one calls the secondary one to complete the first part of the solution. The application domain goes beyond the manufacturing industry and can for example be applied to the design of the electronic systems as explained in the future research.

The objective of the cellular manufacturing is to simplify the management of the<p>manufacturing industries. In regrouping the production of different parts into clusters,<p>the management of the manufacturing is reduced to manage different small<p>entities. One of the most important problems in the cellular manufacturing is the<p>design of these entities called cells. These cells represent a cluster of machines that<p>can be dedicated to the production of one or several parts. The ideal design of a<p>cellular manufacturing is to make these cells totally independent from one another,<p>i.e. that each part is dedicated to only one cell (i.e. if it can be achieved completely<p>inside this cell). The reality is a little more complex. Once the cells are created,<p>there exists still some traffic between them. This traffic corresponds to a transfer of<p>a part between two machines belonging to different cells. The final objective is to<p>reduce this traffic between the cells (called inter-cellular traffic).<p>Different methods exist to produce these cells and dedicated them to parts. To<p>create independent cells, the choice can be done between different ways to produce<p>each part. Two interdependent problems must be solved:<p>• the allocation of each operation on a machine: each part is defined by one or<p>several sequences of operations and each of them can be achieved by a set of<p>machines. A final sequence of machines must be chosen to produce each part.<p>• the grouping of each machine in cells producing traffic inside and outside the<p>cells.<p>In function of the solution to the first problem, different clusters will be created to<p>minimise the inter-cellular traffic.<p>In this thesis, an original method based on the grouping genetic algorithm (Gga)<p>is proposed to solve simultaneously these two interdependent problems. The efficiency<p>of the method is highlighted compared to the methods based on two integrated algorithms<p>or heuristics. Indeed, to form these cells of machines with the allocation<p>of operations on the machines, the used methods permitting to solve large scale<p>problems are generally composed by two nested algorithms. The main one calls the<p>secondary one to complete the first part of the solution. The application domain goes<p>beyond the manufacturing industry and can for example be applied to the design of<p>the electronic systems as explained in the future research.<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished