Determination of Weight Coefficients for Stochastic and Fuzzy Risks for Multimodal Transportation

Research shows that the risks of multimodal transportation in the Northern ports of the Azov and Black seas,in real time,can vary by large quantities.This can cause significant problems for dynamic management of transportation, providing that transportation costs and time are minimized. Therefore, it is essential to formulate a mathematical model to determine the integral risk of freight traffic involved as it could help minimize the need for additional computer resources for the operation of logistics machinery.Determining the value of the integral risk is further complicated by the fact that the mathematical apparatus used for calculating stochastic and fuzzy risks tend to differ from one another. Therefore, an additional tool developed for the unification of various mathematical apparatus was done. The main task was conversion of local risks weight factors to components of integral risks, determined in actual time. The mathematical model has been tested for the dynamic management of freigh ttraffic on the Black Sea ports -Mariupol, Odesa, Chornomorsk, Mikolaev, and Kherson. The route optimization was carried out for container and bulk cargoes, in particular, for grain cargoes. This allowed coverage for the whole range of risks that are inherent to multimodal transportation within the Azov-Black Sea region. The results confirmed that such an approach grants the possibility to choose routes with minimal transportation costs and time, as well as minimization of the use of computer resources

Modeling the Multicommodity Multimodal Routing Problem with Schedule-Based Services and Carbon Dioxide Emission Costs

We explore a freight routing problem wherein the aim is to assign optimal routes to move commodities through a multimodal transportation network. This problem belongs to the operational level of service network planning. The following formulation characteristics will be comprehensively considered: (1) multicommodity flow routing; (2) a capacitated multimodal transportation network with schedule-based rail services and time-flexible road services; (3) carbon dioxide emissions consideration; and (4) a generalized costs optimum oriented to customer demands. The specific planning of freight routing is thus defined as a capacitated time-sensitive multicommodity multimodal generalized shortest path problem. To solve this problem systematically, we first establish a node-arc-based mixed integer nonlinear programming model that combines the above formulation characteristics in a comprehensive manner. Then, we develop a linearization method to transform the proposed model into a linear one. Finally, a computational experiment from the Chinese inland container export business is presented to demonstrate the feasibility of the model and linearization method. The computational results indicate that implementing the proposed model and linearization method in the mathematical programming software Lingo can effectively solve the large-scale practical multicommodity multimodal transportation routing problem

Development of Models for Road-Rail Intermodal Freight Network Under Uncertainty

Freight activities are directly related to a country’s Gross Domestic Product and economic viability. In recent years, the U.S. transportation system supports a growing volume of freight, and it is anticipated that this trend will continue in the coming years. To support the projected increase in freight volume, an efficient, reliable, and low-cost freight logistics system is necessary to keep the U.S. competitive in the global market. In addition, intermodal transport is becoming an increasingly attractive alternative to shippers, and this trend is likely to continue as state and federal agencies are considering policies to induce a freight modal shift from road to intermodal to alleviate highway congestion and emissions. However, the U.S. intermodal freight transport network is vulnerable to various disruptions. A disruptive event can be a natural disaster or a man- made disaster. A number of such disasters have occurred recently that severely impacted the freight transport network. To this end, this dissertation presents four studies where mathematical models are developed for the road-rail intermodal freight transport considering the network uncertainties. The first study proposes a methodology for freight traffic assignment in large- scale road-rail intermodal networks. To obtain the user-equilibrium freight flows, gradient projection (GP) algorithm is proposed. The developed methodology is tested on the U.S. intermodal network using the 2007 freight demands for truck, rail, and road-rail intermodal from the Freight Analysis Framework, version 3, (FAF3). The results indicate that the proposed methodology’s projected flow pattern is similar to the FAF3 assignment. The second study formulates a stochastic model for the aforementioned freight traffic assignment problem under uncertainty. To solve this challenging problem, an algorithmic framework, involving the sample average approximation and GP algorithm, is proposed. The experiments consider four types of natural disasters that have different risks and impacts on the transportation network: earthquake, hurricane, tornado, and flood. The results demonstrate the feasibility of the model and algorithmic framework to obtain freight flows for a realistic-sized network in reasonable time. The third study presents a model for the routing of multicommodity freight in an intermodal network under disruptions. A stochastic mixed integer program is formulated, which minimizes not only operational costs of different modes and transfer costs at terminals but also penalty costs associated with unsatisfied demands. The routes generated by the model are found to be more robust than those typically used by freight carriers. Lastly, the fourth study develops a model to reliably route freight in a road-rail intermodal network. Specifically, the model seeks to provide the optimal route via road segments, rail segments, and intermodal terminals for freight when the network is subject to capacity uncertainties. The proposed methodology is demonstrated using a real-world intermodal network in the Gulf Coast, Southeastern, and Mid-Atlantic regions of the U.S

MULTI-OBJECTIVE SHORTEST PATH APPROACH FOR ROUTING AND SCHEDULING

Customers and goods are being transported at an ever-increasing rate, through complex and interconnected transportation systems. The need for efficiency in terms of economic and environmental aspects, to name a few, gives rise to optimisation problems that often include finding shortest paths. This is why shortest path problems are among the most studied combinatorial optimisation problems. The research presented in this thesis focuses on a class of shortest path problems that are multi-objective and where time window constraints are present. The thesis argues that such problems are best modelled through multigraphs, leading to the Multi-objective Shortest Path Problem on Multigraphs with Time Windows (MSPPMTW). The multigraph allows more detailed modelling of such problems, which is key to accessing the untapped potential for improved efficiency. Multiple aspects of the problem are investigated. The MSPPMTW has increased search space compared to the simple graph Multi-objective Shortest Path Problem, which is already NP-hard. Firstly, the increased computational complexity is investigated empirically, and a model is proposed for predicting the computational effort required, based on easily measurable metrics describing the problem instance, such as the number of parallel arcs or the size of the network. Secondly, a benchmark generation method is proposed for the MSPPMTW, based on the observations of the predictive model. Lastly, a memetic algorithm (with multiple variants based on different representation methods) is proposed to address the problem of finding solutions in short time budgets and scaling to higher numbers of parallel arcs. The memetic algorithm is tested on the proposed benchmark set and a real-world application, the airport ground movement problem. The thesis finds that the proposed memetic algorithm is comparable to the state of the art solution methods. The metaheuristic approaches also have higher promise for future applications in optimising broader transportation systems as a whol

Constraint Programming-Based Heuristics for the Multi-Depot Vehicle Routing Problem with a Rolling Planning Horizon

Der Transportmarkt ist sowohl durch einem intensiven Kostenwettbewerb als auch durch hohe Erwartungen der Kunden an den Service geprägt. Die vorliegende Dissertation stellt zwei auf Constraint Programming basierende heuristische Frameworks vor, die eine Reoptimierung bereits geplanter Touren zu festgelegten Zeitpunkten erlauben und so eine Reaktion auf die gesteigerte Wettbewerbsdynamik und den Kostendruck ermöglichen.Actors on the transportation market currently face two contrary trends: Cost pressure caused by intense competition and a need for prompt service. We introduce two heuristic solution frameworks to enable freight carriers to deal with this situation by reoptimizing tours at predefined points in time. Both heuristics are based on Constraint Programming techniques