Machine layout problem in flexible manufacturing systems
Operations Research
Cell formation using tabu search
Computers and Industrial Engineering - Collection of papers on Computer-Integrated Manufacturing
A tabu search approach to the cell formation problem
Computers and Industrial Engineering
Computers and Industrial Engineering
Genetic algorithm for maximizing the parts flow within cells in manufacturing cell design
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
A new branch-&-bound-enhanced genetic algorithm for the manufacturing cell formation problem
Computers and Operations Research
A simulated annealing algorithm for manufacturing cell formation problems
Expert Systems with Applications: An International Journal
CLASS: An algorithm for cellular manufacturing system and layout design using sequence data
Robotics and Computer-Integrated Manufacturing
Computers and Operations Research
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
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Cellular manufacturing systems (CMS) are used to improve production flexibility and efficiency. They involve the identification of part families and machine cells so that intercellular movement is minimized and the utilization of the machines within a cell is maximized. Previous research has focused mainly on cell formation problems and their variants; however, only few articles have focused on more practical and complicated problems that simultaneously consider the three critical issues in the CMS-design process, i.e., cell formation, cell layout, and intracellular machine sequence. In this study, a two-stage mathematical programming model is formulated to integrate the three critical issues with the consideration of alternative process routings, operation sequences, and production volume. Next, because of the combinatorial nature of the above model, an efficient tabu search algorithm based on a generalized similarity coefficient is proposed. Computational results from test problems show that our proposed model and solution approach are both effective and efficient. When compared to the mathematical programming approach, which takes more than 112h (LINGO) and 1139s (CPLEX) to solve a set of ten test instances, the proposed algorithm can produce optimal solutions for the same set of test instances in less than 12s.