A continuous review model for an inventory system with two supply modes
Management Science
Sole versus dual sourcing in stochastic lead-time (s,Q) inventory models
Management Science
Genetic algorithms in optimizing simulated systems
WSC '95 Proceedings of the 27th conference on Winter simulation
Simulation optimization methodologies
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Empirical comparison of search algorithms for discrete event simulation
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem
Engineering Applications of Artificial Intelligence
International Journal of Computer Applications in Technology
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In the design of modern supply chains, integrating supplier selection, order splitting, transportation allocation and inventory control is a challenging issue. Existing optimisation approaches handle the different problems separately and for the sake of solvability, neglect impact of strategic decisions on operational decisions and do not take into account uncertainties. In this paper, a simulation-based evolutionary multi-objective optimisation approach is proposed to deal with this problem. The approach consists of an optimiser and a simulator. The optimiser, based on a multi-objective genetic algorithm, is used to find best-compromised solutions with respect to various criteria, such as the total cost and customer service level. Candidate solutions are evaluated through simulation, which enables realistic evaluation taking into account uncertainties and dynamics along the whole supply chain. A simple case study from the textile industry is presented to illustrate the applicability of the proposed approach for the real-world applications.