Designing the Intermodal Multiperiod Transportation Network of a Logistic Service Provider Company for Container Management

Lured by the promise of bigger sales, companies are increasingly looking to raise the volume of international trade. Consequently, the amount of bulk products carried in containers and transported overseas exploded because of the flexibility and reliability of this type of transportation. However, minimizing the logistics costs arising from the container flow management across different terminals has emerged asa major problem that companies and affiliated third-party logistics firms face routinely. The empty tankcontainer allocation problem occurs in the context of intermodal distribution systems management and transportation operations carried out by logistic service provider companies. This paper considers the time-evolving supply chain system of an international logistic service provider company that transports bulk products loaded in tank containers via road, rail and sea. In such system, unbalanced movements of loaded tank containers forces the company to reposition empty tank containers. The purpose of this paper is to develop a mathematical model that supports tactical decisions for flow management of empty tank containers. The problem involves dispatching empty tank containers of various types to the meet on-time delivery requirements and repositioning the other tank containers to storage facilities, depots and cleaning stations. To this aim, a mixed-integer linear programming (MILP) multiperiod optimization model is developed. The model is analyzed and developed step by step, and its functionality is demonstrated by conducting experiments on the network from our case study problem, within the boarders of Europe. The case study constitutes three different scenarios of empty tank container allocation. The computational experiments show that the model finds good quality solutions, and demonstrate that cost and modality improvements can be achieved in the network The sensitivity analysis employs a set of data from our case study and randomly selected data to highlight certain features of the model and provide some insights regarding the model’s behavior