Genetic algorithms: foundations and applications
Annals of Operations Research
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Basic techniques for lot streaming
Operations Research
Computer
Genetic algorithms for product design
Management Science
Topological design of local-area networks using genetic algorithms
IEEE/ACM Transactions on Networking (TON)
Structural Properties of Lot Streaming in a Flow Shop
Mathematics of Operations Research
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Scheduling and lot streaming in flowshops with no-wait in process
Journal of Scheduling
Evolutionary algorithms for scheduling m-machine flow shop with lot streaming
Robotics and Computer-Integrated Manufacturing
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Handbook of Metaheuristics
A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study
Journal of Intelligent Manufacturing
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Lot-streaming is the process of splitting a job (lot) into a number of smaller sublots to allow the overlapping of operations between successive machines in a multi-stage production system. The use of sublots usually results in substantially shorter job completion times for the corresponding schedule. A new genetic algorithm (NGA) is proposed for an n-job, m-machine, lot-streaming flow shop scheduling problem with equal size sublots and limited capacity buffers with blocking in which the objective is to minimize total earliness and tardiness penalties. NGA replaces the selection and mating operators of genetic algorithms (GAs), which often lead to premature convergence, by new operators (marriage and pregnancy operators) and also adopts the idea of inter-chromosomal dominance and individuals' similarities. Extensive computational experiments have been conducted to compare the performance of NGA with that of GA. The results show that, on the average, NGA outperforms GA by 9.86 % in terms of objective function value for medium to large-scale lot-streaming flow-shop scheduling problems.