Computers and Industrial Engineering - Cellular manufacturing systems: design, analysis and implementation
A solution to the facility layout problem using simulated annealing
Computers in Industry - Special issue: ASI'96: life cycle approaches to production systems: management, control and supervision
An ant algorithm for the single row layout problem in flexible manufacturing systems
Computers and Operations Research
Part-machine grouping using weighted similarity coefficients
Computers and Industrial Engineering - Special issue: Group technology/cellular manufacturing
Forming part families by using genetic algorithm and designing machine cells under demand changes
Computers and Operations Research
A solution to the unequal area facilities layout problem by genetic algorithm
Computers in Industry - Special issue: Application of genetics algorithms in industry
Applying the sequence-pair representation to optimal facility layout designs
Operations Research Letters
A new mixed integer programming formulation for facility layout design using flexible bays
Operations Research Letters
Genetic application in a facility location problem with random demand within queuing framework
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing
Efficient metaheuristics for pick and place robotic systems optimization
Journal of Intelligent Manufacturing
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In this paper we solve a combined group technology problem with a facility layout problem (FLP). This new approach is called T-FLP. We have developed a hybrid algorithm containing three main steps. The first one, called MPGV (Machine Part Grouping with Volume) is a decomposition method that can create families of product and machine groups based on a volume data matrix. The second one consists on assigning machines to fixed locations, using as a constraint, the solution of the MPGV. This problem is solved as a Quadratic Assignment Problem (QAP). In the third step, we make a global evaluation of all the solutions. A loop on cells is performed using a minimum and maximum number of cells. This loop can choose the appropriate number of cells based on the best solution of a global evaluation. The hybrid algorithm is implemented with two different rules for taking into account the constraint of the MPGV solution. This has generated two methods called YMAY1 and YMAY2. In the MPGV we use a data oriented genetic algorithm. The QAP is solved with an Ant Colony Optimization mixed with a Guided Local Search (ACOGLS). This method has been used to solve a real industrial case. For estimating the efficiency of our method, we have compared our results with an optimal solution obtained by complete enumeration (an exact method).