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This dissertation presents the configuration and capacity management of manufacturing cells using cluster analysis. A heuristic based on cluster analysis is developed to solve cell formation in cellular manufacturing systems (CMS). The clustering heuristic is applied for cell formation considering processing requirement (CFOPR) as well as various manufacturing factors (CFVMF). The proposed clustering heuristic is developed by employing a new solving structure incorporating hierarchical and non-hierarchical clustering methods. A new similarity measure is constructed by modifying the Jarccard similarity and a new assignment algorithm is proposed by employing the new pairwise exchange method. In CFOPR, the clustering heuristic is modified by adding a feedback step and more exact allocation rules. Grouping efficacy is employed as a measure to evaluate solutions obtained from the heuristic. The clustering heuristic for CFOPR was evaluated on 23 test problems taken from the literature in order to compare with other approaches and produced the best solution in 18 out of 23 and the second best in the remaining problems. These solutions were obtained in a considerably short time and even the largest test problem was solved in around one and a half seconds. In CFVMF, the machine capacity was first ensured, and then manufacturing cells were configured to minimize intercellular movements. In order to ensure the machine capacity, the duplication of machines and the split of operations are allowed and operations are assigned into duplicated machines by the largest-first rule. The clustering heuristic for CFVMF proposes a new similarity measure incorporating processing requirement, material flow and machine workload and a new machine-part matrix representing material flow and processing time assigned to multiple identical machines. Also, setup time, which has not been clearly addressed in existing research, is discussed in the solving procedure. The clustering heuristic for CFVMF employs two evaluation measures such as the number of intercellular movements and grouping efficacy. In two test problems taken from the literature, the heuristic for CFVMF produced the same results, but the trade-off problem between the two evaluation measures is proposed to consider the goodness of grouping.

This dissertation presents the configuration and capacity management of manufacturing cells using cluster analysis. A heuristic based on cluster analysis is developed to solve cell formation in cellular manufacturing systems (CMS). The clustering heuristic is applied for cell formation considering processing requirement (CFOPR) as well as various manufacturing factors (CFVMF). The proposed clustering heuristic is developed by employing a new solving structure incorporating hierarchical and non-hierarchical clustering methods. A new similarity measure is constructed by modifying the Jarccard similarity and a new assignment algorithm is proposed by employing the new pairwise exchange method. In CFOPR, the clustering heuristic is modified by adding a feedback step and more exact allocation rules. Grouping efficacy is employed as a measure to evaluate solutions obtained from the heuristic. The clustering heuristic for CFOPR was evaluated on 23 test problems taken from the literature in order to compare with other approaches and produced the best solution in 18 out of 23 and the second best in the remaining problems. These solutions were obtained in a considerably short time and even the largest test problem was solved in around one and a half seconds. In CFVMF, the machine capacity was first ensured, and then manufacturing cells were configured to minimize intercellular movements. In order to ensure the machine capacity, the duplication of machines and the split of operations are allowed and operations are assigned into duplicated machines by the largest-first rule. The clustering heuristic for CFVMF proposes a new similarity measure incorporating processing requirement, material flow and machine workload and a new machine-part matrix representing material flow and processing time assigned to multiple identical machines. Also, setup time, which has not been clearly addressed in existing research, is discussed in the solving procedure. The clustering heuristic for CFVMF employs two evaluation measures such as the number of intercellular movements and grouping efficacy. In two test problems taken from the literature, the heuristic for CFVMF produced the same results, but the trade-off problem between the two evaluation measures is proposed to consider the goodness of grouping.

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Cell formation has received much attention from academicians and practitioners because of its strategic importance to modern manufacturing practices. Existing research on cell formation problems using integer programming (IP) has achieved the target of solving problems that simultaneously optimise machine-cell allocation and partmachine allocation. This thesis presents extensions of an IP model where part-machine assignment and cell formation are addressed simultaneously, and integration of inter-cell movements of parts and machine set-up costs within the objective function is taking place together with the inclusion of an ordered part machine operation sequence. The latter is identified as a neglected parameter for the Cell Formation problem. Due to the nature of the mathematical IP modelling for Cell Formation two main drawbacks can be identified: (a) Cell Formation is considered to be a complex and difficult combinatorial optimisation problem or in other words NP-hard (Non-deterministic Polynomial time hard) problem and (b) because of the deterministic nature of mathematical programming the decision maker is required to specify precisely goals and constraints. The thesis describes a comprehensive study of the cell formation problem where fuzzy set theory is employed for measuring uncertainty. Membership functions are used to express linguistically the uncertainty involved and aggregation operators are employed to transform the fuzzy models into mathematical programming models. The core of the research concentrates on the investigation and development of heuristic and . metaheuristic approaches. A three stage randomly generated heuristic approach for producing an efficient initial solution for the CF together with an iterative heuristic are first developed. Numerous data sets are employed which prove their effectiveness. Moreover, an iterative tabu search algorithm is implemented where the initial solution fed in is the same as that used in the descent heuristic. The first iterative procedure and the tabu search algorithm are compared and the results produced show the superiority of the latter over the former in stability, computational times and clustering results.

Biofilms are formed in aquatic environments by the attachment of bacteria to submerged surfaces, to the air/liquid interface, and to each other. Although biofilms are associated with disease and biofouling, the robust nature of biofilms; for example, their ability to tolerate chemical and physical stresses, makes them attractive for beneficial biotechnology applications such as bioremediation and biofuels. Based on an understanding of diverse signals and regulatory networks during biofilm development, biofilms can be engineered for these applications by manipulating extracellular/intercellular signals and regulators. Here, we rewired the global regulator H-NS of Escherichia coli to control biofilm formation using random protein engineering. H-NS variant K57N was obtained that reduces biofilm formation 10-fold compared with wild-type H-NS (wild-type H-NS increases biofilm formation whereas H-NS K57N reduces it) via its interaction with the nucleoid-associated proteins Cnu and StpA. H-NS K57N leads to enhanced excision of the defective prophage Rac and results in cell lysis through the activation of a host killing toxin HokD. We also engineered another global regulator, Hha, which interacts with H-NS, to disperse biofilms. Hha variant Hha13D6 was obtained that causes nearly complete biofilm dispersal by increasing cell death by the activation of proteases. Bacterial quorum sensing (QS) systems are important components of a wide variety of engineered biological devices, since autoinducers are useful as input signals because they are small, diffuse freely in aqueous media, and are easily taken up by cells. To demonstrate that biofilms may be controlled for biotechnological applications such as biorefineries, we constructed a synthetic biofilm engineering circuit to manipulate biofilm formation. By using a population-driven QS switch based on the LasI/LasR system and biofilm dispersal proteins Hha13D6 and BdcAE50Q (disperses biofilms by titrating cyclic diguanylate), we displaced an existing biofilm and then removed the second biofilm. Persisters are a subpopulation of metabolically-dormant cells in biofilms that are resistant to antibiotics; hence, understanding persister cell formation is important for controlling bacterial infections. Here, we engineered toxin MqsR with greater toxicity and demonstrated that the more toxic MqsR increases persistence by decreasing the ability of the cell to respond to antibiotic stress through its RpoS-based regulation of acid resistance, multidrug resistance, and osmotic resistance systems.

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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.

Today’s rapid development of powerful tools for geneexpression analysis provides unprecedented resources forelucidating complex molecular events. The objective of this workhas been to apply, combine andevaluate tools for analysis of differential gene expressionusing atherosclerosis as a model system. First, an optimisedsolid-phase protocol for representational difference analysis(RDA) was applied to twoin vitromodel systems. Initially, The RDA enrichmentprocedure was investigated by shotgun cloning and sequencing ofsuccessive difference products. In the subsequent steps,combinations of RDA and microarray analysis were used tocombine the selectivity and sensitivity of RDA with thehigh-throughput nature of microarrays. This was achieved byimmobilization of RDA clones onto microarrays dedicated forgene expression analysis in atherosclerosis as well ashybridisation of labelled RDA products onto global microarrayscontaining more than 32,000 human clones. Finally, RDA wasapplied for the investigation of the focal localisation ofatherosclerotic plaques in mice usingin vivotissue samples as starting material. A large number of differentially expressed clones wereisolated and confirmed by real time PCR. A very diverse rangeof gene fragments was identified in the RDA products especiallywhen they were screened with global microarrays. However, themicroarray data also seem to contain some noise which is ageneral problem using microarrays and should be compensated forby careful verification of the results. Quite a large number of candidate genes related to theatherosclerotic process were found by these studies. Inparticular several nuclear receptors with altered expression inresponse to oxidized LDL were identified and deserve furtherinvestigation. Extended functional annotation does not liewithin the scope of this thesis but raw data in the form ofnovel sequences and accession numbers of known sequences havebeen made publicly available in GenBank. Parts of the data arealso available for interactive exploration on-line through aninteractive software tool. The data generated thus constitute abase for new hypotheses to be tested in the field ofatherosclerosis. <b>Keywords:</b>representational difference analysis, geneexpression profiling, microarray analysis, atherosclerosis,foam cell formation