Shorter product life-cycles, unpredictable demand, and customized products have forced manufacturing firms to operate more efficiently and effectively in order to adapt to changing requirements. Traditional manufacturing systems, such as job shops and flow lines, cannot handle such environments. Cellular manufacturing, which incorporates the flexibility of job shops and the high production rate of flow lines, has been seen as a promising alternative for such cases. Although cellular manufacturing provides great benefits, the design of cellular manufacturing systems is complex for real-life problems. Existing design methods employ simplifying assumptions which often deteriorate the validity of the models used for obtaining solutions. Two simplifying assumptions used in existing design methods are as follows. First, product mix and demand do not change over the planning horizon. Second, each operation can be performed by only one machine type, i.e., routing flexibility of parts is not considered. This research aimed to develop a model and a solution approach for designing cellular manufacturing systems that addresses these shortcomings by assuming dynamic and stochastic production requirements and employing routing flexibility. A mathematical model and an optimal solution procedure were developed for the design of cellular manufacturing under dynamic and stochastic production environment employing routing flexibility. Optimization techniques for solving such problems usually require a substantial amount of time and memory space, therefore, a simulated annealing based heuristic was developed to obtain good solutions within reasonable amounts of time. The heuristic was evaluated in two ways. First, different cellular manufacturing design problems were generated and solved using the heuristic. Then, solutions obtained from the heuristic were compared with lower bounds of solutions obtained from the optimal solution procedure. The lower bounds were used instead of optimal solutions because of the computational time required to obtain optimal solutions. The results show that the heuristic performs well under various circumstances, but routing flexibility has a major impact on the performance of the heuristic. The heuristic appears to perform well regardless of problem size. Second, known solutions of two CM design problems from literature were used to compare with those from the heuristic. The heuristic slightly outperforms one design approach, but substantially outperforms the other design approach. / Ph. D.

Product development is affected by uncertainties due to changing customer requirements, changing regulations, technological developments, long lead times, high product complexities, and geopolitical issues. Automation, increased flexibility of production, and reduced lead times are drivers that allow product development to be competitive in this scenario. Design engineers should be aware of production capabilities to facilitate early producibility assessments and to avoid late changes. Production preparation is identified as an important activity in the product development process, whereby the producibility of a product is assessed. In this thesis, the current state of production preparation during product development is investigated and a method is introduced supporting production preparation using production requirements. The work was carried out using the design research methodology framework and comprised four studies based on the four steps of the framework. The research clarification and descriptive study 1 phases aimed at developing understanding and were done by means of data collection at the companies through interviews and document studies. The next two steps were prescriptive study and descriptive study 2, which aimed at developing and evaluating the support. This was done through observation, workshops, and solution development. The production preparation process is supported by Design for Manufacturing and Assembly, failure mode and effects analysis, lesson-learnt documents, and computer-aided design, and the efficiency of the process is dependent on individual skills and knowledge. Tools to support common understanding, remove ambiguity in requirements, and enable collaboration between design and production engineers are needed. The developed method allows for the identification, definition, structuring, and sharing of production requirements, aligning with varying maturities of product and production systems during product development. This helps improve the collaboration between design and production engineers for production preparation.