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
A genetic algorithm approach to cellular manufacturing systems
Computers and Industrial Engineering
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
An evolutionary algorithm for manufacturing cell formation
Computers and Industrial Engineering
A new branch-&-bound-enhanced genetic algorithm for the manufacturing cell formation problem
Computers and Operations Research
A hybrid grouping genetic algorithm for the cell formation problem
Computers and Operations Research
Brief paper: An improved differential evolution algorithm for the task assignment problem
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Group technology based adaptive cell formation using predator-prey genetic algorithm
Applied Soft Computing
Expert Systems with Applications: An International Journal
Grouping genetic operators for the delineation of functional areas based on spatial interaction
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Computers and Operations Research
Hi-index | 12.07 |
Cellular manufacturing (CM) is an important application of group technology (GT), a manufacturing philosophy in which parts are grouped into part families, and machines are allocated into machine cells to take advantage of the similarities among parts in manufacturing. The target is to minimize inter-cellular movements. Inspired by the rational behind the so called grouping genetic algorithm (GGA), this paper proposes a grouping version of differential evolution (GDE) algorithm and its hybridized version with a local search algorithm (HGDE) to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 40 problem instances from literature and compare the performance of GGA and GDE. We also compare the performance of both algorithms when they are tailored with a local search algorithm. Our computations reveal that the proposed algorithm performs well on all test problems, exceeding or matching the best solution quality of the results presented in previous literature.