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
A GA-based parameter design for single machine turning process with high-volume production
Computers and Industrial Engineering
Scheduling of single stage assembly with air transportation in a consumer electronic supply chain
Computers and Industrial Engineering - Special issue: Logistics and supply chain management
Minimizing total earliness and tardiness on a single machine using a hybrid heuristic
Computers and Operations Research
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
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
A main issue in supply chain management is coordinating production and distribution decisions. To achieve effective logistics scheduling, it is critical to integrate these two functions and plan them in a coordinated way. The problem is to determine both production schedule and air transportation allocation of orders to optimize customer service at minimum total cost. In order to solve the given problem, two genetic algorithm (GA) approaches are developed. However, the effectiveness of most metaheuristic algorithms is significantly depends on the correct choice of parameters. Hence, a Taguchi experimental design method is applied to set and estimate the proper values of GAs parameters to improve their performance. For the purpose of performance evaluation of proposed algorithms, various problem sizes are utilized and the computational results of GAs are compared with each other. Moreover, we investigate the impacts of the rise in the problem size on the performance of our algorithms.