a b s t r a c tThis paper focuses on two major issues in cellular manufacturing, namely manpower allocation and cell loading. Although significant work has been done in both of these fields, research to minimize the Total Tardiness (TT) within a multi-cell environment has been limited. The methodology adopted in this paper is a two-step one. First, manpower allocation for operations within a cell is determined using mathematical models. This step also includes variations of the mathematical model for sharing of operators between operations and sharing of operators with restrictions. The models with little or no restrictions have found to yield a higher production rate than the model with no operator sharing allowed. The next step involves a mathematical model for cell loading. The performance measure examined in this phase is the total tardiness subject to total Crew Size restriction. The total tardiness is reduced as the total crew size increases. However, it was also found that selection of alternative cell configurations affect the results as well total crew size.

Author Bios: Gokhan Egilmez serves as an assistant professor of Industrial and Manufacturing Engineering at North Dakota State University. He also worked as postdoctoral research associate in the dept. of Civil, Environmental and Construction Engineering at University of Central Florida prior to joining department of IME at NDSU. He obtained a doctorate degree in Mechanical and Systems Engineering and two master degrees in Industrial and Systems Engineering and Civil Engineering at Ohio University between 2007 and 2012. Prior to his higher education, in the United States, he received his BS in Industrial Engineering at Istanbul Technical University, Turkey in 2007. His research interests include sustainability assessment of social, environmental, and economic aspects of engineered systems, transportation sustainability & safety, applied operations research and metaheuristic optimization, parametric & nonparametric statistical modeling and dynamic simulation modeling. Gokhan has various peer-reviewed research articles, book chapter and conference proceedings related to sustainable development, manufacturing system design & control, supply chain management, transportation safety and predictive modeling & machine learning. http://gokhanegilmez.wordpress.com/

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In this chapter, cell loading and family scheduling in a cellular manufacturing environment is studied. What separates this study from others is the presence of individual due dates for every job in a family. The performance measure is to minimize the number of tardy jobs. Family splitting among cells is allowed but job splitting is not. Even though family splitting increases number of setups, it increases the possibility of meeting individual job due dates. Two methods are employed in order to solve this problem, namely Mathematical Modeling and Genetic Algorithms. The results showed that Genetic Algorithm found the optimal solution for all problems tested. Furthermore, GA is efficient compared to the Mathematical Modeling especially for larger problems in terms of execution times. The results of experimentation showed that family splitting was observed in all multi-cell solutions, and therefore, it can be concluded that family splitting is a good strategy.

In this chapter, cell loading and family scheduling in a cellular manufacturing environment is studied. What separates this study from others is the presence of individual due dates for every job in a family. The performance measure is to minimize the number of tardy jobs. Family splitting among cells is allowed but job splitting is not. Even though family splitting increases number of setups, it increases the possibility of meeting individual job due dates. Two methods are employed in order to solve this problem, namely Mathematical Modeling and Genetic Algorithms. The results showed that Genetic Algorithm found the optimal solution for all problems tested. Furthermore, GA is efficient compared to the Mathematical Modeling especially for larger problems in terms of execution times. The results of experimentation showed that family splitting was observed in all multi-cell solutions, and therefore, it can be concluded that family splitting is a good strategy.