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The platooning of trucks can be considered to be a potential approach to mitigate some of the negative effects that trucking can have on traffic streams. This paper proposes a cooperative distributed approach for forming/modifying platoons of trucks based on consensus algorithms. In this approach, trucks exchange information about their current status in real time, and the platoon is formed in consecutive iterations. This distributed consensus-based algorithm is compared with a centralized optimization-based algorithm for truck platooning, in which the trucks move with a set of predetermined speeds for a definite amount of time to form a platoon. The two approaches are tested and compared using various scenarios generated based on real data collected on a highway in Basel, Switzerland. Based on the results, the consensus-based algorithm proved to be a more general scheme that is able to form platoons even in cases with large initial separation of trucks. This algorithm is able to handle complex situations using its capability to form partial platoons.

Nowadays the majority of goods passing through seaports are transported by road, resulting in a large number of empty movements and high total costs. This paper proposes an optimization model for the cooperative planning of multiple truck carrier operations in a seaport environment for maximizing the total profit derived from their cooperation. A compensation mechanism is introduced to motivate carriers to share their trips. Time windows, trip deadlines and fleet sizes are considered. The planning approach is evaluated using real data sets from the Italian port of Genoa. Numerous scenarios are tested and an extensive computational analysis is reported.

The recent development of Intelligent Transportation Systems offers the possibility of cooperative planning of multi-actor systems in a distributed framework, by enabling prompt exchange of information among actors. This paper proposes a modeling framework for cooperation in intermodal freight transport chains as multi-actor systems. In this framework, the problem of optimizing freight transportation is decomposed into a suitable set of sub-problems, each representing the operations of an actor which are connected using a negotiation scheme. A Discrete Event model is developed which optimizes the system on a rolling horizon basis to account for the dynamics of intermodal freight transport operations. This framework allows for an event driven short/medium term planning of intermodal freight transport chains. The proposed methodology is evaluated using a realistic case study, and the results are compared against the First-Come-First-Served strategy, highlighting the significance of cooperation in systems operating close to capacity.

Trucks typically use the same infrastructure as cars do, and this imposes negative effects on the overall traffic operations. To mitigate such effects as well as their negative environmental impacts, platooning of trucks can be considered a potential solution. For platooning a set of trucks, two main strategies are typically assumed: a catch-up strategy (i.e., the upstream trucks accelerate to catch up with the leading trucks) and a slow-down strategy (i.e., the leading trucks decelerate so that the upstream trucks can catch up and platoon with them). The behavior of trucks when platooning with either of these two strategies was analyzed. Then a hybrid platooning strategy was developed that combined the two approaches in an optimal manner. The performance of the hybrid strategy was further analyzed and compared with that of the individual approaches. Results show that the hybrid strategy is the most general scheme capable of forming a platoon in various cases. Moreover, it is the fastest strategy considering the required time for platooning. In regard to performance, the hybrid strategy is capable of forming a platoon using the maximum platooning speeds of trucks. These characteristics make it a suitable strategy for large-scale deployment in truck platooning.

Shunting is an important component of single-wagon-load railway operations. It consists of splitting incoming freight trains and forming new outgoing trains by reassembling the wagons from different incoming trains. The efficiency of shunting operations has a major impact on the performance of rail freight services. An event-based model is proposed for sorting trains for shunting at a hump shunting yard, which is capable of handling real-time changes in the system. In this model, various events are designed for managing changes in the system over time. At each time step, the optimal sequence of trains for shunting is determined to maximize the throughput of the shunting yard. The model was tested in a benchmark study and was shown to be an improvement over the previous solutions as well as solutions based on the first-in, first-out strategy.

This paper proposes a heuristic approach for planning the activity of multiple carriers cooperating with the goal of eliminating empty truck trips while maximizing the cost saving resulting from their collaboration. The approach foresees three main phases: in the first step, the transportation demand is decomposed in two parts based on freight flows trade-off; in the second step, a linear optimization model, which takes into account compensation mechanisms among carriers, allows to combine two by two trips belonging to different carriers in order to decrease the number of empty movements. In this second phase, the importance of customers is explicitly taken into account by assigning to each trip a preference value. Finally, in the third step, a second optimization problem enables assigning, for each carrier, trucks to trips with the goal of minimizing their travel costs. The proposed heuristic approach has been tested on some instances and the results obtained are analyzed and discussed in the paper.

The developments of telecommunication and intelligent transportation technologies have led to an increase in the safety level of freight transportation and have enabled making the routing decisions based on real-time information and an updated condition of the network, the suppliers, and the receivers. This paper proposes a dynamic decentralized optimization case, in which the network of freight distribution, presented as a distributed Supply Chain (DSC), expands or changes temporally. The proposed optimization problem should be solved repeatedly on a rolling horizon basis which enables optimizing a certain time period in advance. At each step, the problem is modeled using discrete event systems (DESs) and a transportation model is proposed for transferring freights between any two nodes of the supply chain. This model supports the decision making process for routing and scheduling of delivery shipments that arrive dynamically at a supplier, which is of major importance and directly affects the performance of a freight distribution system.

Managing distributed supply-chains takes a lot of effort for planning, scheduling, monitoring, and controlling. The current study proposes and examines an approach for optimizing the transport problem inside such chains, using a decentralized approach. In this approach, models are developed for optimization of the internal operations of suppliers, logistic terminals and the transportation of components between them. The performance of these models are connected using Lagrangian relaxation, to satisfy the global objective of minimizing lateness of delivery to end users. This approach is analyzed in a case study and the simulation results are presented.

Nowadays, there is a growing agreement that sustainable mobility is about effectively interconnecting transport systems which have to provide a door-to-door service. Intermodal transportation of freights is an important issue in this context, due to the increasing amount of freight movements all over the world. The current study proposes and examines an approach for optimizing such transport chains. In this approach, the freight distribution corridor is decomposed into the different elements with individual functions that interact two-by-two based on a cooperative scheme relying on information flow in the corridor. The methodology is evaluated by means of an example corridor and the results are presented.

This paper proposes a heuristic decomposition approach for scheduling truck trips with the goal of satisfying the demand of a container single carrier while minimizing its costs. The approach foresees three phases: a pre-processing step, in which the demand is decomposed in two parts and a set of rules for combining trips are defined, and two optimization phases. The first mixed linear integer optimization model allows to combine trips two by two in order to decrease the number of empty or partially empty trips, while the second optimization problem enables assigning trucks to trips with the goal of minimizing travel costs. The efficiency of the proposed methodology is analyzed using a real simple case over a local network and the results are presented.

Carsharing services allow users to benefit from the advantages of a private car without the costs of owning one. One-way systems provide users with a higher level of service than traditional carsharing systems in terms of flexibility because users do not need to return to the station of origin. Moreover, the added option to leave the vehicle at any free parking area, which is not necessarily a station, increases the flexibility offered by the one-way system. Introduction of such improvements to the carsharing system, however, leads to a vehicle relocation problem, which should be addressed carefully to avoid concentration of vehicles in certain areas. This paper reports on a study of this issue with the use of discrete event systems (DESs), which allowed an easy representation of the complex dynamics of the carsharing system. A user-based methodology was proposed on the basis of an optimal relocation policy in a rolling horizon framework. This methodology not only offers greater flexibility to users, it also maximizes operator benefits by reducing the number of required staff to relocate vehicles among the stations and determines the minimum number of vehicles needed to satisfy system demand. The DES model was applied to a case study to evaluate the proposed approach. The results showed a significant decrease in the rejection rate from the worst scenario (no relocation) to the best (relocation of all vehicles by their users). The paper concludes with suggestions for additional research and improvements to this study.