Fakhrzad, M., Goodarzian, F., Golmohammadi, A. (2019). Addressing a fixed charge transportation problem with multi-route and different capacities by novel hybrid meta-heuristics. Journal of Industrial and Systems Engineering, 12(1), 167-184.

Mohammad Bagher Fakhrzad; F. Goodarzian; A. M. Golmohammadi. "Addressing a fixed charge transportation problem with multi-route and different capacities by novel hybrid meta-heuristics". Journal of Industrial and Systems Engineering, 12, 1, 2019, 167-184.

Fakhrzad, M., Goodarzian, F., Golmohammadi, A. (2019). 'Addressing a fixed charge transportation problem with multi-route and different capacities by novel hybrid meta-heuristics', Journal of Industrial and Systems Engineering, 12(1), pp. 167-184.

Fakhrzad, M., Goodarzian, F., Golmohammadi, A. Addressing a fixed charge transportation problem with multi-route and different capacities by novel hybrid meta-heuristics. Journal of Industrial and Systems Engineering, 2019; 12(1): 167-184.

Addressing a fixed charge transportation problem with multi-route and different capacities by novel hybrid meta-heuristics

In most real world application and problems, a homogeneous product is carried from an origin to a destination by using different transportation modes (e.g., road, air, rail and water). This paper investigates a fixed charge transportation problem (FCTP), in which there are different routes with different capacities between suppliers and customers. To solve such a NP-hard problem, four meta-heuristic algorithms include Red Deer Algorithm (RDA), Stochastic Fractal Search(SFS), Genetic Algorithm (GA), and Simulated Annealing (SA) and two new hybrid meta-heuristics include hybrid RDA & GA (HRDGA) algorithm and Hybrid SFS & SA (HSFSA) algorithm are utilized. Regarding the literature, this is the first attempt to employ such optimizers to solve a FCTP. To tune up their parameters of algorithms, various problem sizes are generated at random and then a robust calibration is applied by using the Taguchi method. The final output shows that Simulated Annealing (SA) algorithm is the better than other algorithms for small-scale, medium-scale, and large-scale problems. As such, based on the Gap value of algorithms, the results of LINGO software shows that it reveals a better outputs in comparison with meta-heuristic algorithms in small-scale and simulated annealing algorithm is better than other algorithms in large-scale and medium-scale problems. Finally, a set of computational results and conclusions are presented and analyzed.

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