Component allocation and partitioning for a dual delivery placement machine
Operations Research
Sequencing of insertions in printed circuit board assembly
Operations Research
Scheduling problems and traveling salesman: the genetic edge recombination
Proceedings of the third international conference on Genetic algorithms
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Modern heuristic techniques for combinatorial problems
A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
A comparison of setup strategies for printed circuit board assembly
Computers and Industrial Engineering - Cellular manufacturing systems: design, analysis and implementation
Grouping genetic algorithms: an efficient method to solve the cell formation problem
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Production planning problems in printed circuit board assembly
Discrete Applied Mathematics
Cluster Analysis
Cluster analysis to minimize sequence dependent changeover times
Mathematical and Computer Modelling: An International Journal
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The goal of the printed circuit board job-batching problem is to minimize the total manufacturing time required to process a set of printed circuit board jobs on an insertion machine. We have developed four families of heuristics to solve this problem: the clustering family, the bin-packing family, a family of sequencing genetic algorithms, and a grouping genetic algorithm. Within each family of heuristics, we developed several variations. Some of the variations use techniques from the literature and some of the techniques we developed specifically for this problem. We test the variations and select a good performer from each family.