Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Supply chain vs. supply chain: using simulation to compete beyond the four walls
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 2
How i2 integrates simulation in supply chain optimization
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 2
Proceedings of the 32nd conference on Winter simulation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
Production scheduling validity in high level supply chain models
Proceedings of the 33nd conference on Winter simulation
Distributed simulation with incorporated APS procedures for high-fidelity supply chain optimization
Proceedings of the 33nd conference on Winter simulation
Design of supply-chain logistics system considering service level
Computers and Industrial Engineering - Supply chain management
A Supply Network Model with Base-Stock Control and Service Requirements
Operations Research
Development of a Rapid-Response Supply Chain at Caterpillar
Operations Research
An exact schema theorem for adaptive genetic algorithm and its application to machine cell formation
Expert Systems with Applications: An International Journal
A fuzzy c-means based hybrid evolutionary approach to the clustering of supply chain
Computers and Industrial Engineering
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The current paper outlines a framework of a distributed hierarchical model for supply chain planning and scheduling optimization. The framework comprises three main modules: routing and sequence optimization, supply chain virtual clustering and supply chain order scheduling. It is envisaged that the hierarchical model can be used to realize management level strategies, facilitate planning and optimize the detailed operation schedules of various supply chain units in a supply chain. The detailed design of a multiple population search strategy (MPSS) based on genetic algorithm (GA) and tabu search (TS) for routing selection and operation sequence optimization is presented. Using the tabu search, the crossover and mutation rates of GA can be made adaptive to suit different stages of search. The results show that the MPSS is not only able to reach a better solution, but also able to reduce the computational time. The work has also demonstrated the possibility of adopting a hybrid approach that combines the strengths of the tabu search and genetic algorithms for the optimization of routing and sequence in a supply chain in order to achieve management level objectives such as minimizing cost and increasing the level of on-time delivery.