Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Network Models in Optimization and Their Applications in Practice
Network Models in Optimization and Their Applications in Practice
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
Algorithms for solving the single-sink fixed-charge transportation problem
Computers and Operations Research
Expert Systems with Applications: An International Journal
Synchronizing production and air transportation scheduling using mathematical programming models
Journal of Computational and Applied Mathematics
Hybrid genetic algorithm for multi-time period production/distribution planning
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
Nonlinear fixed charge transportation problem by minimum cost flow-based genetic algorithm
Computers and Industrial Engineering
Computers and Industrial Engineering
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In this paper, we consider the fixed-charge transportation problem (FCTP) in which a fixed cost, sometimes called a setup cost, is incurred if another related variable assumes a nonzero value. To tackle such an NP-hard problem, there are several genetic algorithms based on spanning tree and Prufer number representation. Contrary to the findings in previous works, considering the genetic algorithm (GA) based on spanning tree, we present a pioneer method to design a chromosome that does not need a repairing procedure for feasibility, i.e. all the produced chromosomes are feasible. Also, we correct the procedure provided in previous works, which designs transportation tree with feasible chromosomes. We show the previous procedure does not produce any transportation tree in some situations. Besides, some new crossover and mutation operators are developed and used in this work. Due to the significant role of crossover and mutation operators on the algorithm's quality, the operators and parameters need to be accurately calibrated to ensure the best performance. For this purpose, various problem sizes are generated at random and then a robust calibration is applied to the parameters using the Taguchi method. In addition, two problems with different sizes are solved to evaluate the performance of the presented algorithm and to compare that performance with LINGO and also with the solution presented in previous work.