Abstract

This paper deals with issues of shaping and functioning of computer programs in the modelling and solving of multimodal transportation network problems. A methodology of an integrated use of a programming language for mathematical modelling is defined, as well as spreadsheets for the solving of complex multimodal transportation network problems. The paper contains a comparison of the partial and integral methods of solving multimodal transportation networks. The basic hypothesis set forth in this paper is that the integral method results in better multimodal transportation network rationalization effects, whereas a multimodal transportation network model based on the integral method, once built, can be used as the basis for all kinds of transportation problems within multimodal transport. As opposed to linear transport problems, multimodal transport network can assume very complex shapes. This paper contains a comparison of the partial and integral approach to transportation network solving. In the partial approach, a straight forward model of a transportation network, which can be solved through the use of the Solver computer tool within the Excel spreadsheet interface, is quite sufficient. In the solving of a multimodal transportation problem through the integral method it is necessary to apply sophisticated mathematical modelling programming languages which support the use of complex matrix functions and the processing of a vast amount of variables and limitations. The LINGO programming language is more abstract than the Excel spreadsheet, and it requires a certain programming knowledge. The definition and presentation of a problem logic within Excel, in a manner which is acceptable to computer software, is an ideal basis, for modelling in the LINGO programming language, as well as a faster and more effective implementation of the mathematical model. This paper provides proof for the fact that it is more rational to solve the problem of multimodal transportation networks by using the integral, rather than the partial method. U radu je obrađena problematika oblikovanja i funkcioniranja računalnih programa u modeliranju i rješavanju problema multimodalnih transportnih mreža. Definirana je metodologija integrirane uporabe programskog jezika za matematičko modeliranje i proračunske tablice u rješavanju problema složene multimodalne transportne mreže. Uspoređene su parcijalna i integralna metoda rješavanja multimodalnih transportnih mreža. Temeljna hipoteza postavljena u ovom radu je da se integralnom metodom postižu bolji učinci racionalizacije multimodalnih transportnih mreža, pri čemu se jednom izgrađeni model multimodalne transportne mreže temeljen na integralnoj metodi, može koristiti kao temelj za sve vrste transportnih problema u multimodalnom transportu. Za razliku od linearnih transportnih problema multimodalna transportna mreža može poprimiti vrlo složene oblike. U radu su uspoređeni parcijalni i integralni pristup rješavanja transportne mreže. Kod parcijalnog pristupa dovoljan je jednostavniji model transportne mreže koji se može riješiti uporabom računalnog alata Solver u sučelju proračunske tablice Excel. U rješavanju multimodalnog transportnog problema integralnom metodom, potrebno je koristiti sofisticirane programske jezike za matematičko modeliranje koji podržavaju uporabu složenih matričnih funkcija i procesiranje velikog broja varijabli i ograničenja. Programski jezik LINGO je apstraktniji u odnosu na proračunsku tablicu Excel i zahtijeva određena znanja o programiranju. Definiranje i prezentacija logike problema u Excelu na način prihvatljiv računalnom programu predstavlja idealnu osnovu za modeliranje u programskom jeziku LINGO, te bržu i učinkovitiju implementaciju matematičkog modela. U radu je dokazano da je problem multimodalne transportne mreže racionalnije rješavati integralnom, nego parcijalnom metodom.

Abstract

In recent years, intermodal transport has become an increasingly attractive alternative to freight shippers. However, the current intermodal freight transport is not as efficient as it could be. Oftentimes an empty container needs to be transported from the empty container depot to the shipper, and conversely, an empty container needs to be transported from the receiver to the empty container depot. These empty container movements decrease the freight carrier’s profit, as well as increase traffic congestion, decrease roadway safety, and add unnecessary emissions to the environment. To this end, our study evaluates a potential collaboration strategy to be used by carriers for domestic intermodal freight transport based on an optimization approach to reduce the number of empty container trips. A binary integer-linear programming model is developed to determine each freight carrier’s optimal schedule while minimizing its operating cost. The model ensures that the cost for each carrier with collaboration is less than or equal to its cost without collaboration. It also ensures that average savings from the collaboration are shared equally among all participating carriers. Additionally, two stochastic models are provided to account for uncertainty in truck travel times. The proposed collaboration strategy is tested using empirical data and is demonstrated to be effective in meeting all of the shipment constraints. Document type: Article

Abstract

Due to the geographical distance between producers and consumers, the impact of our current transport system on the environment is considerable. In an ideal, more sustainable transport system, several complementary transport modes are available, so that the most suitable route can always be selected. Because multiple transport modes and operators are involved, decision processes become more complex. That is why this research focuses on the development of innovative transport systems with realistic characteristics and planning limitations, in order to stimulate intermodal transport. Two planning tools have been developed to support decisions made by intermodal transport planners. It concerns on the one hand the integration of intermodal short and long distance routing and on the other hand the loading planning of trains. Both tools consist of fast planning algorithms that can handle realistic problem properties and can be integrated into transport management systems. The objective is to minimize transport costs and to make maximum use of available transport capacity.