Computers and Operations Research
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Computers and Industrial Engineering - Supply chain management
Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach
Computers and Industrial Engineering - Supply chain management
A GA-based parameter design for single machine turning process with high-volume production
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Hybrid flexible flowshops with sequence-dependent setup times and machine availability constraints
Computers and Industrial Engineering
Guest editorial special issue on artificial immune systems
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Hybrid artificial intelligence approaches on vehicle routing problem in logistics distribution
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Computers and Electronics in Agriculture
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In this paper, two stages of supply chain network; distribution centers (DCs) and customers, are considered. There are customers with particular demands and potential places which are candidate to be as distribution centers. Each of the potential DCs can ship to any of the customers. Two types of costs are considered; opening cost, assumed for opening a potential DC plus shipping cost per unit from DC to the customers. The proposed model selects some potential places as distribution centers in order to supply demands of all customers. In order to solve the given problem, two algorithms, genetic algorithm and artificial immune algorithm, are developed. The Taguchi experimental design method is applied to select the optimum parameters with the least possible number of experiments. For the purpose of performance evaluation of proposed algorithms, various problem sizes are utilized and the computational results of the algorithms are compared with each other. Finally, we investigate the impacts of the rise in the problem size on the performance of our algorithms.