Cell Design and Routing of Jobs in a Multisite Make-to-Order Enterprise. Gupta, M., P, J. C. B. R, & Dutta, P. In
Cell Design and Routing of Jobs in a Multisite Make-to-Order Enterprise [link]Paper  abstract   bibtex   
Make-to-order is a manufacturing process in which manufacturing starts once a customer's order is received. A large enterprise may have many such ``make-to-order" shops distributed geographically. The cost and time for executing a job in each of these shops may vary. Therefore, it is important for a multisite enterprise to judiciously decide on where to process the jobs. Ideally, an enterprise would like to minimize the cost while meeting the deadlines and at the same time maximize the utilization of the shops. The time to execute jobs can vary based on how the shops are laid out (the design of shops) and the decision of how jobs are routed (among the various shops). Predicting (or estimating) the likely turnaround time (and cost) for various jobs across the different shops enables the routing decision process. In this paper, we address the three important problems of (i) cell-design, (ii) turnaround time prediction, and (iii) routing of jobs across various shops. We propose (i) a novel approach based on graph partitioning and set cover heuristic to generate a set of cell designs for a shop, (ii) a framework based on machine learning techniques to predict the turnaround time of jobs across various shops, and (iii) a routing algorithm based on dynamic programming and local search heuristic to route jobs such that the overall cost is minimized
@inproceedings {icaps16-123,
    track    = {​​​Applications Track},
    title    = {Cell Design and Routing of Jobs in a Multisite Make-to-Order Enterprise},
    url      = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13176},
    author   = {Manoj Gupta and  Jagadeesh Chandra Bose R P and  Partha Dutta},
    abstract = {Make-to-order is a manufacturing process in which manufacturing starts once a customer's order is received. A large enterprise may have many such ``make-to-order" shops distributed geographically. The cost and time for executing a job in each of these shops may vary. Therefore, it is important for a multisite enterprise to judiciously decide on where to process the jobs. Ideally, an enterprise would like to minimize the cost while meeting the deadlines and at the same time maximize the utilization of the shops. The time to execute jobs can vary based on how the shops are laid out (the design of shops) and the decision of how jobs are routed (among the various shops). Predicting (or estimating) the likely turnaround time (and cost) for various jobs across the different shops enables the routing decision process. In this paper, we address the three important problems of (i) cell-design, (ii) turnaround time prediction, and (iii) routing of jobs across various shops. We propose (i) a novel approach based on graph partitioning and set cover heuristic to generate a set of cell designs for a shop, (ii) a framework based on machine learning techniques to predict the turnaround time of jobs across various shops, and (iii) a routing algorithm based on dynamic programming and local search heuristic to route jobs such that the overall cost is minimized},
    keywords = {}
}
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