Hybrid Intelligent Algorithm for Determining Network Capacity with Transportation Time Reliability Constraints

Abstract

Motivated by a problem in the commodity transportation, a mathematical model is developed to calculate capacity of single-commodity network when the time reliability levels of transporting commodity between origin-destination pairs are constrained. We use a hybrid intelligent algorithm, in which genetic algorithm is embedded with Monte Carlo simulation to solve the optimization model. In the hybrid intelligent algorithm, the genetic algorithm is used to report the best path flow solutions and the Monte Carlo simulation is to check the feasibility of the chromosomes of genetic algorithm. With a computational experiment, the fact that network capacity decreases with the increase of the transportation time reliability level is validated. The efficacies of the developed procedures are examined by comparing the computational times of solving algorithm with that of previous work.

Keywords:

• network capacity
• transportation time reliability
• single-commodity flow
• genetic algorithm
• Monte Carlo simulation