Analysis of the Numerical Effects of Parallelism on a Parallel Genetic Algorithm
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
Selected Papers from AISB Workshop on Evolutionary Computing
Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach
Expert Systems with Applications: An International Journal
Computers and Operations Research
A genetic algorithm-based scheduler for multiproduct parallel machine sheet metal job shop
Expert Systems with Applications: An International Journal
Minimizing makespan in a flow-line manufacturing cell with sequence dependent family setup times
Expert Systems with Applications: An International Journal
Computers and Operations Research
Scheduling Cellular Manufacturing Systems Using ACO and GA
International Journal of Applied Metaheuristic Computing
Robotics and Computer-Integrated Manufacturing
Effect of solution representations on Tabu search in scheduling applications
Computers and Operations Research
Global optimization using a genetic algorithm with hierarchically structured population
Journal of Computational and Applied Mathematics
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This paper considers the problem of scheduling part families and jobs within each part family in a flowshop manufacturing cell with sequence dependent family setups times where it is desired to minimize the makespan while processing parts (jobs) in each family together. Two evolutionary algorithms-a Genetic Algorithm and a Memetic Algorithm with local search-are proposed and empirically evaluated as to their effectiveness in finding optimal permutation schedules. The proposed algorithms use a compact representation for the solution and a hierarchically structured population where the number of possible neighborhoods is limited by dividing the population into clusters. In comparison to a Multi-Start procedure, solutions obtained by the proposed evolutionary algorithms were very close to the lower bounds for all problem instances. Moreover, the comparison against the previous best algorithm, a heuristic named CMD, indicated a considerable performance improvement.