A multi-stage stochastic inventory management model for transport companies including several different transport modes

Abstract

ABSTRACT This paper deals with modeling and solving a multi-stage stochastic inventory management problem for transport companies, including several different transport modes. This model aims to determine the amount of stored cargo and transport containers and trucks to the appropriate locations. Due to the uncertainty of environmental conditions, the demand and transfer costs parameters are considered uncertainly in different scenarios in the proposed model. Since the uncertain model is insoluble, a new robust-fuzzy-probabilistic method has been used to control the model’s parameters. As a result, the final model minimizes the total cost of maintaining cargo and transporting trucks and containers. Three genetic algorithms, particle swarm optimization and gray wolf optimization have been employed to solve the proposed stochastic inventory management model. The results of the problem analysis show that the increase of uncertainty rate in different scenarios due to the increase in the amount of demand from cargo, transmission, and maintenance costs have increased. In return, the total network costs have also increased.