A granularity model for balancing the structural complexity of manufacturing systems equipment and layout |
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| Affiliation: | 1. Intelligent Manufacturing Systems (IMS) Center, Department of Industrial and Manufacturing Systems Engineering, University of Windsor, Windsor, ON, Canada;2. Mechanical and Industrial Engineering, University of Minnesota Duluth, Duluth, MN, USA;1. Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN 46202, USA;2. Department of Radiation Oncology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan;3. Department of Radiology, Osaka University Hospital, Suita, Osaka 565-0871, Japan;4. Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;1. Department of Medical Physics & Engineering, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan;2. Department of Radiation Oncology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan;3. Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN 46202, USA;1. The Turkish Air Force Academy, ?stanbul, Turkey;2. Energy Institute, ?stanbul Technical University, ?stanbul 34357, Turkey;3. Industrial Engineering Department, ?stanbul Technical University, ?stanbul 34357, Turkey;1. Faculty of Bioscience and Industry, SARI, Jeju National University, Jeju, 63243, Republic of Korea;2. Life Sciences Research Institute, Biomedic Co., Ltd., Bucheon, 14548, Republic of Korea;3. Research Institute for Subtropical Agriculture & Biotechnology, Jeju National University, Jeju, 63243, Republic of Korea |
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| Abstract: | The structural complexity of a manufacturing system results primarily from the complexity of its equipment and their layout. The balance between both complexity sources can be achieved by searching for the best system granularity level, which yields a manufacturing system with the least overall structural complexity. A new system granularity complexity index is developed to sum up and normalize the complexity resulting from the system layout complexity and the equipment structural complexity. A previously developed layout complexity index together with a code-based structural complexity assessment are used to determine the structural complexity of standalone pieces of equipment and to arrive at a balance between the two sources of complexity. Cladistics analysis is used to hierarchically cluster required pieces of equipment and bundle them into more integrated equipment and machines and demonstrate the possible different system granularity levels. The new developed model is a useful tool to create specific system configuration and layout alternatives based on system components adjacency, and then select the system design with the least overall structural complexity among those alternatives. The results of the presented case study clearly demonstrated this trade-off where decomposing manufacturing systems into a highly granular configuration with standalone machines maximizes system layout complexity and minimizes equipment complexity, while at a low level of granularity pieces of equipment are bundled into complex integrated machines, lines or cells but with a very simple system layout. |
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| Keywords: | Complexity Layout Granularity Cladogram Coding Classification |
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