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The focus of this thesis is on the precast concrete products manufacturing industry, which as one of the labour-intensive industries requires a substantial number of highly skilled operators in terms of crews to produce the final product. A crew is a group of multi-skilled chargehands and operators that have various skills and experience necessary to conduct an activity in a professional way. The high cost of skilled operators and the apparent inefficiencies of utilising such skilled operators in the industry are the major driving force. To achieve this, optimal crew allocation is required. Crew allocation is complex because of the multi-criteria nature of the problem and availability of thousands of possibilities and allocation alternatives. There is a gap in previous research efforts associated with crew allocation planning in the precast industry. Current practices suggest that the crew allocation process is carried out intuitively and the allocation of crews to production processes is subjective. This has led to high process-waiting times, improper allocation of skilled operators and ultimately higher production costs. In this context, the aim of this research is to propose an effective crew allocation methodology and a computer-based intelligent simulation model for its implementation. The objective of the approach is to guarantee a better workflow through minimising process-waiting time, optimising operator utilisation, and subsequently reducing the allocation cost. This research develops a holistic and integrated methodology for modelling crew allocation problems by reviewing state-of-art resource allocation techniques, structured interviews with production managers, site visits and a detailed case study. The methodology is developed using an IDEF0 process model and a generic process map for both the business and the production processes of the precast manufacturing system. A multi-layered genetic algorithm model is developed in conjunction with a process-simulation model to form a hybrid allocation system dubbed ‘SIM_Crew’. The model incorporates databases (Excel and MS Access), a simulation model (developed using Arena 12.0) and genetic algorithms (developed using Visual Basic for Applications) to facilitate the generation and evaluation of various “what-if” crew allocation scenarios. A number of performance criteria have been developed to evaluate the allocation plans. ‘SIM_Crew’ enables the investigation and analysis of allocating possible schedules and provides a facility to visualise the production processes. ‘SIM_Crew’ was validated using real life case study data and it was concluded that the allocation of crews to precast processes using genetic algorithm improves the throughput time and reduces the allocation cost as compared with real life production data. It is anticipated that future use of this research will solve the crew allocation problem in the precast industry.

Dilated cardiomyopathy (DCM) belongs to the heterogeneous group of heart muscle disorders called cardiomyopathies, characterize by left ventricular dilatation and reduce myocardial muscle contractility that leads to a reduced ejection fraction of the heart. There are several causes of DCM; genetic, toxic, metabolic, endocrine, infiltrative, and idiopathic disorders. Forty causative genes encoding for a variety of proteins have been identified up to date. The objective of this study is to identify potential biomarkers related to the disease process of DCM.Microarray and RNA-Seq profiles of cardiac tissue were used to identify differentially expressed genes (DEGs). Four Microarray datasets (GSE 3585, GSE3586, GSE9800, and GSE42955), and three RNA-Seq datasets were selected (GSE55296, GSE65466, and GSE71613) from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to explore the hub genes involved in the disease process of DCM.A total of 51 modules for microarray datasets and 15 modules for RNA-Seq datasets were identified using correlation network analysis. Four common statistically- significant genes were identified among these modules, including AP3M2, ECM2, ERBB2, and ZNF83. AP3M2 gene, which involves protein trafficking to lysosomes and specialized organelles, ECM2 gene, which affects in extracellular matrix protein function, ERBB2 gene, which involves in Erb-B2 Receptor Tyrosine Kinase 2 signaling pathway, and ZNF83 gene which involves in transcriptional regulation in the cell. A total of 9 hub genes that were differentially over-expressed significantly in cardiac tissue from RNA-Seq datasets, including EIF4GA which is related to viral myocarditis, HACD1, MYOM3, PTPN4, and NRBP1 are associated with muscular disorders, CELSR which play an essential role for planar cell polarity, SLC27A6 which is transporter involve in LCFA uptake process, SCMH1 involves in negative regulation of gene expression, and DCAF11 encodes WD repeat-containing protein, that involves in protein modification pathway .We identified four hub genes from microarray and nine hub genes from RNA-Seq datasets using weighted gene co-expression networks analysis. We propose that these hub genes play an essential role in DCM pathogenesis and disease progression and could be a useful tools as genetic markers for the disease. Further studies and validations of these hub genes are needed to confirm our findings and to improve our understanding of the disease process.