Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A Joint Location-Inventory Model
Transportation Science
Warehouse-Retailer Network Design Problem
Operations Research
Stochastic Transportation-Inventory Network Design Problem
Operations Research
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
A bi-criteria model for the inventory aggregation problem under risk pooling
Computers and Industrial Engineering
Trade-offs Between Customer Service and Cost in Integrated Supply Chain Design
Manufacturing & Service Operations Management
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Hi-index | 12.05 |
Vendor-managed inventory (VMI) is one of the emerging solutions for improving the supply chain efficiency. It gives the supplier the responsibility to monitor and decide the inventory replenishments of their customers. In this paper, an integrated location-inventory distribution network problem which integrates the effects of facility location, distribution, and inventory issues is formulated under the VMI setup. We presented a Multi-Objective Location-Inventory Problem (MOLIP) model and investigated the possibility of a multi-objective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA2) for solving MOLIP. To assess the performance of our approach, we conduct computational experiments with certain criteria. The potential of the proposed approach is demonstrated by comparing to a well-known multi-objective evolutionary algorithm. Computational results have presented promise solutions for different sizes of problems and proved to be an innovative and efficient approach for many difficult-to-solve problems.