Simulated annealing: theory and applications
Simulated annealing: theory and applications
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Computers and Operations Research
Scheduling to minimize weighted earliness and tardiness about a common due-date
Computers and Operations Research
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Minmax earliness/tardiness scheduling in identical parallel machine system using genetic algorithms
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Dynamic non-preemptive single machine scheduling
Computers and Operations Research
A production scheduling system for parallel machines in an electrical appliance
Proceedings of the 23rd international conference on on Computers and industrial engineering
Tabu search for total tardiness minimization in flowshop scheduling problems
Computers and Operations Research
A modified genetic algorithm for single machine scheduling
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms
Advanced planning and scheduling with outsourcing in manufacturing supply chain
Computers and Industrial Engineering - Supply chain management
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Engineering Applications of Artificial Intelligence
Computers and Industrial Engineering
Pedestrian routing system using genetic algorithms suitable for mobile ad hoc networks
MATH'06 Proceedings of the 10th WSEAS International Conference on APPLIED MATHEMATICS
Order acceptance using genetic algorithms
Computers and Operations Research
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Production scheduling seeks optimal combination of short manufacturing time, stable inventory, balanced human and machine utilization rate, and short average customer waiting time. Since the problem in general has been proven as NP-hard, we focus on suboptimal scheduling solutions for parallel flow shop machines where jobs are queued in a bottleneck stage. A Genetic Algorithm with Sub-indexed Partitioning genes (GASP) is proposed to allow more flexible job assignments to machines. Our fitness function considers tardiness, earliness, and utilization rate related variable costs to reflect real requirements. A premature convergence bounce is added to traditional genetic algorithms to increase permutation diversity. Finally, a production scheduling system for an electronic plant based on GASP is implemented and illustrated through real production data. The proposed GASP has demonstrated the following advantages: (1) the solutions from GASP are better and with smaller deviations than those from heuristic rules and genetic algorithms with identical partitioning genes; (2) the added premature convergence bounce helps obtain better solutions with smaller deviations; and (3) the consideration of variable costs in the fitness function helps achieve better performance indicators.