Date of Award


Publication Type

Master Thesis

Degree Name



Mechanical, Automotive, and Materials Engineering

First Advisor

Lashkari, R.S. (Industrial and Manufacturing Systems Engineering)


Engineering, Industrial.



Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.


Note: appendices for this title available . This thesis proposes a multi-objective, mixed integer, non-linear programming model of cellular manufacturing systems (CMS) design to maximize the system reliability and minimize the total system cost simultaneously. The model involves multiple machine types, multiple machines for each machine type, multiple part types, and alternative process routes for each part type. Each process route consists of a sequence of operations. System reliability associated with machines along process routes can be improved by increasing the number of parallel machines subject to acceptable cost. Assuming machine reliability to follow a lognormal distribution, the CMS design problem is to optimally decide the number of each machine type, assign machines to cells, and select, for each part type, the process route with the highest overall system reliability while minimizing the total cost. Genetic algorithm is applied to solve this practical-sized CMS design problem. It finds a heuristic solution within a reasonable computational time.