Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
The use of simulation for construction elements manufacturing
Proceedings of the 30th conference on Winter simulation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Computers and Operations Research
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
GA-based decision support systems in production scheduling
International Journal of Intelligent Systems Technologies and Applications
A simulated annealing approach to a bi-criteria sequencing problem in a two-stage supply chain
Computers and Industrial Engineering
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The goal of production scheduling is to achieve a profitable balance among on-time delivery, short customer lead time, and maximum utilization of resources. However, current practices in precast production scheduling are fairly basic, depending heavily on experience, thereby resulting in inefficient resource utilization and late delivery. Moreover, previous methods ignoring buffer size between stations typically induce unfeasible schedules. Certain computational techniques have been proven effective in scheduling. To enhance precast production scheduling, this research develops a multi-objective precast production scheduling model (MOPPSM). In the model, production resources and buffer size between stations are considered. A multi-objective genetic algorithm is then developed to search for optimum solutions with minimum makespan and tardiness penalties. The performance of the proposed model is validated by using five case studies. The experimental results show that the MOPPSM can successfully search for optimum precast production schedules. Furthermore, considering buffer sizes between stations is crucial for acquiring reasonable and feasible precast production schedules.