Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
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 new approach to solve hybrid flow shop scheduling problems by artificial immune system
Future Generation Computer Systems - Special issue: Computational science of lattice Boltzmann modelling
A GA-based parameter design for single machine turning process with high-volume production
Computers and Industrial Engineering
Applying artificial immune system and ant algorithm in air-conditioner market segmentation
Expert Systems with Applications: An International Journal
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
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A hybrid classical approach to a fixed-charged transportation problem
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Nonlinear fixed charge transportation problem by minimum cost flow-based genetic algorithm
Computers and Industrial Engineering
Computers and Industrial Engineering
Hi-index | 12.05 |
This paper presents a mathematical model for a capacitated fixed-charge transportation problem in a two-stage supply chain network, in which potential places are candidate to be as distribution centers (DCs) and customers with particular demands. In contrast with the previous studies considered ample capacity for DCs, we consider the capacity for each DC. The presented model minimizes the total cost in such a way that some DCs are selected in order to supply demands of all the customers. To tackle such an NP-hard problem, we propose an artificial immune algorithm (AIA) and a genetic algorithm (GA) based on the spanning tree and Prufer number representation. We introduce a new method to calculate the affinity value by using an adjustment rate. Furthermore, we apply the Taguchi experimental design method to set the proper values of AIA and GA parameters in order to improve their performances. Finally, we investigate the impact of increasing the problem size on the performance of our proposed algorithms.