On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Ranking techniques in multi-criteria, genetic algorithm-based optimization
Ranking techniques in multi-criteria, genetic algorithm-based optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Distributed Manufacturing Scheduling Using Intelligent Agents
IEEE Intelligent Systems
Learning Multiclass Pattern Discrimination
Proceedings of the 1st International Conference on Genetic Algorithms
Techniques for reducing the disruption of superior building blocks in genetic algorithms
Techniques for reducing the disruption of superior building blocks in genetic algorithms
Software project management with GAs
Information Sciences: an International Journal
Integrated multiobjective optimization and a priori preferences using genetic algorithms
Information Sciences: an International Journal
A genetic algorithm calibration method based on convergence due to genetic drift
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Machine learning for dynamic multi-product supply chain formation
Expert Systems with Applications: An International Journal
| Hi-index | 0.07 |
Multiple supply chains management is a multiple criteria optimisation problem with a large and complex search space. It is a broad field in which the aim is to optimise and facilitate supply chain operations by balancing between quality improvement and cost reduction. This research proposes a dynamic adaptive Genetic Algorithm that includes a chromosome refinement procedure to adjust the structure and order of the genes within the chromosome. The method improves the search efficiency in a complex space by locating near-global optimal solutions. The proposed approach is implemented as a process parameter controller that allows the values of the production and quality control variables to be adjusted at the run-time. A case study involving various evaluation criteria and a number of variables in multiple supply chains within a mixed production problem domain is adopted to demonstrate the effectiveness of the proposed approach.