Variant-oriented Planning Models for Parts/Products Grouping, Sequencing and Operations

This research aims at developing novel methods for utilizing the commonality between part/product variants to make modern manufacturing systems more flexible, adaptable, and agile for dealing with less volume per variant and minimizing total changes in the setup between variants. Four models are developed for use in four important domains of manufacturing systems: production sequencing, product family formation, production flow, and products operations sequences retrieval. In all these domains, capitalizing on commonality between the part/product variants has a pivotal role. For production sequencing; a new policy based on setup similarity between product variants is proposed and its results are compared with a developed mathematical model in a permutation flow shop. The results show the proposed algorithm is capable of finding solutions in less than 0.02 seconds with an average error of 1.2%. For product family formation; a novel operation flow based similarity coefficient is developed for variants having networked structures and integrated with two other similarity coefficients, operation and volume similarity, to provide a more comprehensive similarity coefficient. Grouping variants based on the proposed integrated similarity coefficient improves changeover time and utilization of the system. A sequencing method, as a secondary application of this approach, is also developed. For production flow; a new mixed integer programing (MIP) model is developed to assign operations of a family of product variants to candidate machines and also to select the best place for each machine among the candidate locations. The final sequence of performing operations for each variant having networked structures is also determined. The objective is to minimize the total backtracking distance leading to an improvement in total throughput of the system (7.79% in the case study of three engine blocks). For operations sequences retrieval; two mathematical models and an algorithm are developed to construct a master operation sequence from the information of the existing variants belonging to a family of parts/products. This master operation sequence is used to develop the operation sequences for new variants which are sufficiently similar to existing variants. Using the proposed algorithm decreases time of developing the operations sequences of new variants to the seconds

Setup Optimization in High-Mix Surface Mount PCB Assembly

Siirretty Doriast

Differences in genotype and virulence among four multidrug-resistant <i>Streptococcus pneumoniae</i> isolates belonging to the PMEN1 clone

We report on the comparative genomics and characterization of the virulence phenotypes of four &lt;i&gt;S. pneumoniae&lt;/i&gt; strains that belong to the multidrug resistant clone PMEN1 (Spain&lt;sup&gt;23F&lt;/sup&gt; ST81). Strains SV35-T23 and SV36-T3 were recovered in 1996 from the nasopharynx of patients at an AIDS hospice in New York. Strain SV36-T3 expressed capsule type 3 which is unusual for this clone and represents the product of an in vivo capsular switch event. A third PMEN1 isolate - PN4595-T23 - was recovered in 1996 from the nasopharynx of a child attending day care in Portugal, and a fourth strain - ATCC700669 - was originally isolated from a patient with pneumococcal disease in Spain in 1984. We compared the genomes among four PMEN1 strains and 47 previously sequenced pneumococcal isolates for gene possession differences and allelic variations within core genes. In contrast to the 47 strains - representing a variety of clonal types - the four PMEN1 strains grouped closely together, demonstrating high genomic conservation within this lineage relative to the rest of the species. In the four PMEN1 strains allelic and gene possession differences were clustered into 18 genomic regions including the capsule, the blp bacteriocins, erythromycin resistance, the MM1-2008 prophage and multiple cell wall anchored proteins. In spite of their genomic similarity, the high resolution chinchilla model was able to detect variations in virulence properties of the PMEN1 strains highlighting how small genic or allelic variation can lead to significant changes in pathogenicity and making this set of strains ideal for the identification of novel virulence determinant

Value Stream Mapping for Formation of Product Families

Customers tend to have various needs, desires, and manufacturers are looking for ways to respond to these multiple needs efficiently and effectively. They try to offer their customers multiple products with the shortest delivery time and minimum cost while maintaining customers’ desired quality. One of the strategies that help manufacturers meet their customers\u27 needs is customization. However, to manage this strategy\u27s downsides, manufacturers need to maintain a particular variety level to reduce production costs and time. There are many methodologies to manage the variety. One of the most important ones is creating product families, that is possible to form through many different approaches; From considering BOM to process sequences. This research believes value stream is an effective means to form product families. This thesis, studies different family forming methodologies, and moves through investigating the value stream map of some products, then forms the families within a case study. Manufacturing data was collected from the past year and it was processed by process flow analysis, value stream maps and rank order clustering methods. Then, this research recognized the variants and calculated the similarity and volume coefficients to form product families. Finally, learning curve analysis evaluated the results of formed product families. The formed product families can help the manufacturer reduce waiting times and improve process cycle times

Analytical Modeling of Human Choice Complexity in a Mixed Model Assembly Line Using Machine Learning-Based Human in the Loop Simulation

Despite the recent advances in manufacturing automation, the role of human involvement in manufacturing systems is still regarded as a key factor in maintaining higher adaptability and flexibility. In general, however, modeling of human operators in manufacturing system design still considers human as a physical resource represented in statistical terms. In this paper, we propose a human in the loop (HIL) approach to investigate the operator???s choice complexity in a mixed model assembly line. The HIL simulation allows humans to become a core component of the simulation, therefore influencing the outcome in a way that is often impossible to reproduce via traditional simulation methods. At the initial stage, we identify the significant features affecting the choice complexity. The selected features are in turn used to build a regression model, in which human reaction time with regard to different degree of choice complexity serves as a response variable used to train and test the model. The proposed method, along with an illustrative case study, not only serves as a tool to quantitatively assess and predict the impact of choice complexity on operator???s effectiveness, but also provides an insight into how complexity can be mitigated without affecting the overall manufacturing throughput

RULE EXTRACTION TO ESTABLISH CRITERIA FOR MINICELL DESIGN IN MASS CUSTOMIZATION MANUFACTURING

Minicell-based manufacturing system is used in identifying best minicell designs. The existing method of minicell design generates best minicell designs by designing and scheduling minicells simultaneously. While in this research designing of minicells and scheduling of jobs in minicells is done separately. This research evaluates the effectiveness of hierarchical approach and compares with simultaneous method. Minicell designs with respect to average flow times and machine capacities and both are identified in a multi-stage flow shop environment. Rules for the extraction of good minicell designs in mass customization manufacturing systems are also established

Hierarchical Clustering Approach for Product Variety Management

Continually evolving customer’s needs has contributed to an increase in demand for product variety over the recent decades. Proliferation of product variants affects different aspects of product life cycle which increases the complexity of managing product variety. In this context, the notion of grouping and classification based on similarity within a family of product is the key in managing product variety. This research proposes hierarchical clustering as solution approach that is intuitively relevant and it focuses on progressively grouping the elements that share high similarity with each other. In this research, three types of product variety-related problems are investigated. The first problem concerns with designing product architecture in a way to support product variety. Design Structure Matrix (DSM) is used to visualize product architecture and to develop a new matrix-based clustering approach based on hierarchical cluster analysis. The challenge is that there are numerous possible product architectures even for a product with few components. One unique advantage of the proposed method lies in supporting “overlapping components” which is not directly addressed by the conventional techniques in cluster analysis. The second problem focuses on structuring supply chain network in case of product variety that indicates the precedence orders of suppliers and sub-assemblers. The challenge is that the number of possible structures of supply chain network grows dramatically with the increase in the number of product variants. The solution approach is based on hierarchical clustering, in which the tree structure is applied to construct the supply chain network. The core technique is to investigate the coupling values between the module variants and characterizing the grouping condition in the structuring process. The third problem is to develop semi-finished products to reduce production costs. The challenge is that the possible solution space can increase exponentially with increase in the number of elements (e.g. components) in the problems. In the solution approach, the basic information of product variety is captured in a matrix format, specifying the component requirements for each product variant. Then, hierarchical clustering is applied over the components with the consideration of demands. The key stage is similarity analysis, in which problem-specific information can be incorporated in the clustering process. In summary, the proposed method can be a practical tool for tackling product variety-related problems. It yields good quality results in a limited time. Thus, it can be used to obtain better results than other algorithms when the amount of time available to perform the task is limited

Knowledge Discovery Models for Product Design, Assembly Planning and Manufacturing System Synthesis

The variety of products has been growing over the last few decades so that the challenges for designers and manufacturers to enhance their design and manufacturing capabilities, responsively and cost-effectively are greater than ever. The main objective of this research is to help designers and manufacturers cope with the increasing variety management challenges by exploiting the data records of existing or old products, along with appropriate Knowledge Discovery (KD) models, in order to extract the embedded knowledge in such data and use it to speed-up the development of new products. Four product development activities have been successfully addressed in this research: product design, product family formation, assembly sequencing and manufacturing system synthesis. The models and methods developed in this dissertation present a package of knowledge-based solutions that can greatly support product designers and manufacturers at various stages of the product development and manufacturing planning stages. For design retrieval; using efficient tree reconciliation algorithms found in Biological Sciences, a novel Bill of Materials (BOM) trees matching method was developed to retrieve the closest old design and discover components and structure shared with new product design. As a further application to BOM matching, an enhanced BOM matching method was also developed and used for product family formation. A new approach was introduced for assembly sequencing, based on the notion of consensus trees used in evolutionary studies, to overcome the critical limitation of individual assembly sequence retrieval methods that are not able to capture the assembly sequence data for a given new combination of components that never existed before in the same product variant. For manufacturing system synthesis; a novel Integer Programming model was developed to extract association rules between the product design domain and manufacturing domain to be used for synthesizing a manufacturing/assembly system for new products. Examples of real products were used to demonstrate and validate the developed models and comparisons with related existing methods were carried out to demonstrate the advantages of the developed models. The outcomes of this research provide efficient, and easy to implement knowledge-based solutions for facilitating cost-effective and rapid product development activities