Andres G. Abad
Andres G. Abad received his B.Sc. in Applied Statistics with a minor in Computer Science from Escuela Superior Politecnica del Litoral (ESPOL), Guayaquil, Ecuador, in 2004. In 2008, he received a M.Sc. in Industrial and Operations Engineering from the University of Michigan, Ann Arbor. In 2010, he received a Ph.D. in the Department of Industrial and Operations Engineering at the University of Michigan, Ann Arbor.
His research interests include modeling and analysis of complex manufacturing systems, mathematical modelling of human’s operational and decision behaviours, and advanced statistical analysis of high dimensional data. He received the ScholarPOWER academic award in 2008 and 2009.
His research interests include modeling and analysis of complex manufacturing systems, mathematical modelling of human’s operational and decision behaviours, and advanced statistical analysis of high dimensional data. He received the ScholarPOWER academic award in 2008 and 2009.
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Papers by Andres G. Abad
Modeling of human operators’ performance in an assembly environment is a particularly difficult task, mainly because the variables involved come from very different sources. Furthermore, a model that successfully characterizes operator’s performance must include variables that are intrinsic and extrinsic to the operator.
In this work we will consider two intrinsic variables: the experience and time used to think before performing a task; and one extrinsic variable: the demand uncertainty.
The objective of this dissertation is to develop an enhanced general methodology for modeling and analyzing process complexity for mixed model assembly systems. The following fundamental research has been conducted:
(*) A set of complexity metrics are proposed for measuring the complexity of various elements in a manufacturing system. These metrics are proposed by constructing a linkage with the communication system framework. Unlike the existing complexity measures defined in the literature, this research is the first effort to include production quality into the measurement of how well a manufacturing system can handle the process complexity induced by the input demand variety.
(*) A systematic method is developed for efficiently and explicitly representing complex hybrid assembly system configurations by the use of algebraic expressions, which can overcome drawbacks of two widely used representation methods: block diagrams and adjacency matrices. By further extending the algebraic configuration operators, the algebraic performance operators are defined for the first time for the systematic evaluation of system performance metrics; these metrics include quality conforming rates for individual product types at each station, process capability for handling complexity, and production cycle time for various product types. Therefore, when compared to other methods, the proposed algebraic expression modeling method also has a unique merit in providing computational capability for automatically evaluating various system performance metrics.
(*) An integrated model is introduced for the first time to describe the effect of operator‟s factors on the process operation performance. The model includes intrinsic factors such as the operators‟ thinking time and experience; and extrinsic factors such as the choice task complexity induced by the product variety in mixed model assembly systems.
Modeling of human operators’ performance in an assembly environment is a particularly difficult task, mainly because the variables involved come from very different sources. Furthermore, a model that successfully characterizes operator’s performance must include variables that are intrinsic and extrinsic to the operator.
In this work we will consider two intrinsic variables: the experience and time used to think before performing a task; and one extrinsic variable: the demand uncertainty.
The objective of this dissertation is to develop an enhanced general methodology for modeling and analyzing process complexity for mixed model assembly systems. The following fundamental research has been conducted:
(*) A set of complexity metrics are proposed for measuring the complexity of various elements in a manufacturing system. These metrics are proposed by constructing a linkage with the communication system framework. Unlike the existing complexity measures defined in the literature, this research is the first effort to include production quality into the measurement of how well a manufacturing system can handle the process complexity induced by the input demand variety.
(*) A systematic method is developed for efficiently and explicitly representing complex hybrid assembly system configurations by the use of algebraic expressions, which can overcome drawbacks of two widely used representation methods: block diagrams and adjacency matrices. By further extending the algebraic configuration operators, the algebraic performance operators are defined for the first time for the systematic evaluation of system performance metrics; these metrics include quality conforming rates for individual product types at each station, process capability for handling complexity, and production cycle time for various product types. Therefore, when compared to other methods, the proposed algebraic expression modeling method also has a unique merit in providing computational capability for automatically evaluating various system performance metrics.
(*) An integrated model is introduced for the first time to describe the effect of operator‟s factors on the process operation performance. The model includes intrinsic factors such as the operators‟ thinking time and experience; and extrinsic factors such as the choice task complexity induced by the product variety in mixed model assembly systems.