{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/43778"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/43778","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"A computerized methodology for balancing and sequencing mixed model stochastic assembly lines","abstract":"A methodology for designing mixed model stochastic assembly line systems and a computer package to implement it for realistic problem sizes were developed. The methodology consists of three major steps: (1) generation of feasible sequences of feeding models into the line, (2) generation of feasible allocations (balances) of work elements to work stations, and (3) generation of combinations of sequences and allocations with the best ordering of elements within stations, calculation of total expected cost for each combination, and selection of the one with the least cost. For generating feasible balances, an exhaustive search procedure with a number of heuristic rules was used to ensure searching the whole feasible region in limited time. A cost model based on labor and incompletion costs is used to calculate the cost of each combination, and a recursive procedure to calculate incompletion probabilities for each element and incorporate them into the cost model was implemented. An example problem, its results, and the computer package listings are included.","abstract_html":"A methodology for designing mixed model stochastic assembly line systems and a computer package to implement it for realistic problem sizes were developed. The methodology consists of three major steps: (1) generation of feasible sequences of feeding models into the line, (2) generation of feasible allocations (balances) of work elements to work stations, and (3) generation of combinations of sequences and allocations with the best ordering of elements within stations, calculation of total expected cost for each combination, and selection of the one with the least cost. For generating feasible balances, an exhaustive search procedure with a number of heuristic rules was used to ensure searching the whole feasible region in limited time. A cost model based on labor and incompletion costs is used to calculate the cost of each combination, and a recursive procedure to calculate incompletion probabilities for each element and incorporate them into the cost model was implemented. 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