Several interesting algorithms have been proposed for the Rail and Intermodal problem space and this article continues the enumeration of those that were discussed earlier.

The real-time rail management of a terminus involves routing incoming trains through the station and scheduling their departures with the objective of optimizing punctuality and regularity of train service. The purpose is to develop an automated train traffic control system. The scheduling problem is modeled as a bicriteria job shop scheduling problem with additional constraints. The two objective functions in lexicographical order are the minimization of tardiness/earliness and the headway optimization.  The problem is solved in two steps.  A heuristic builds a feasible solution by considering the first objective function. Then the regularity is optimized. This works well for simulations of a terminus.

A simulation-based approach is also used for tactical locomotive fleet sizing. Their study shows the throughput increases with the number of locomotives up to a certain level. After that the congestion is caused by the movements of many locomotives in a capacity constrained rail network.

One correlation that seems to hold true is that the decisions on sizing a rail car fleet has a tremendous influence on utilizing that fleet. The optimum use of empty rail cars for demand response is one of the advantages to building a formulation and solving it to optimize the fleet size and freight car allocation under uncertainty demands.

This correlation has given rise to a model that formulates and solves for the optimum fleet size and freight car allocation. This model also provides rail network information such as yard capacity, unmet demands, and number of loaded and empty railcars at any given time and location. It is helpful to managers or decision makers of any train company for planning and management activities. A two-stage solution procedure for solving rail-car fleet.

Drayage operations are specific to rail. Intermodal transportation improves when these operations are considered. In the cities or urban areas, drayage suffers from random transit times. This makes fleet scheduling difficult. A dynamic optimization model could use real-time knowledge of the fleet’s position, permanently enabling the planner to reallocate tasks as the problem conditions change. Tasks can be flexible or well-defined. One application of this model was tried out on test data and then applied to a set of random drayage problems of varying sizes and characteristics.

Tactical design of scheduled service networks for transportation systems is one where different network coordinate and their coordination is critical to the success of the operations. For a given demand, a new model was proposed to determine departure times of the service such that the throughput time is minimized. This is the time that involves processing, inspection, move and queue times during demand. It could be considered a hybrid of some models discussed earlier such as the service network design that involves asset management and multiple fleet co-ordination to emphasize the explicit modeling of different vehicle fleets. Synchronization of collaborative networks and removal of border crossing operations have a significant impact on the throughput time for the freight.

Some more interesting algorithms for the Rail and Intermodal problem space, now follows.

There’s even an approach to do real-time management of a metro rail terminus. It involves routing incoming trains through the station and scheduling their departures with the objective of optimizing punctuality and regularity of train service. The purpose is to develop an automated train traffic control system. The scheduling problem is modeled as a bicriteria job shop scheduling problem with additional constraints. The two objective functions in lexicographical order, are the minimization of tardiness/earliness and the headway optimization.  The problem is solved in two steps.  A heuristic builds a feasible solution by considering the first objective function. Then the regularity is optimized. This works well for simulations of a terminus.

A simulation-based approach is also used for tactical locomotive fleet sizing. Their study shows the throughput increases with the number of locomotives up to a certain level. After that the congestion is caused by the movements of many locomotives in a capacity constrained rail network.

One correlation that seems to hold true is that the decisions on sizing a rail car fleet has a tremendous influence on utilizing that fleet. The optimum use of empty rail cars for demand response is one of the advantages to building a formulation and optimizing it to optimize the fleet size and freight car allocation under uncertainty demands.

 

Several interesting algorithms have been proposed for the Rail and Intermodal problem space. This is a complex system composed by different transport networks, infrastructures, different transport means and operators, such as dryage operators, terminal operators, network operators and others. Intermodal means there are lots of decision makers who must work in a coordinated manner for the system to run smoothly. If intermodal transport is to be developed, it will require more decision-making support tools to assist the decision-makers and stakeholders.

When train operations are perturbed, a new conflict free timetable must be recomputed such that the deviation from the original must be minimized. This scheduling problem is modeled with an alternative graph formulation and a branch and bound algorithm is developed. Some approaches use an integrated framework which deals with signal layout optimization, train scheduling optimization at microscopic level, and others.

Heuristic approaches include a look-ahead greedy heuristic and a global neighborhood search algorithm, in terms of railway total train delay. Scheduling additional train services to be integrated into the current timetables is a problem that is modeled as a hybrid job shop scheduling techniques that operate upon a disjunctive graph model of trains.

One approach develops a train slot selection model based on multicommodity network flow concepts for determining freight train timetables. This helps to schedule rail services along multiple interconnected routes. This model seeks to minimize operating costs incurred by the carriers and delays incurred by the shippers. The schedules and demand levels are ensured to be mutually consistent. When the model is embedded in a simulation, it can be used iteratively and together with the output of the scheduling solution.

Another approach solves the freight transportation on hybrid rail networks used to transport both passengers and freight. It uses a preferred timetable as input for each freight train. Some overrides are permitted such as specifying a path different from the one in the ideal timetable. Its objective is to introduce as many new freight trains as possible by assigning them timetables that are as close as possible to the ideal ones. An integer linear programming method is used in the model.

A third approach specifically considers the double-track train scheduling. It focuses on the high-speed passenger rail line in an existing network and minimizes both the expected wait times for high-speed trains and the total travel times of both speed trains. Using the priority for speed, the problem is translated as multi-mode resource project. It is then solved for scheduling with a branch and bound algorithm and a beam search algorithm.

 In continuation of a set of types of problems in fleet management science, Rail and inter-modal transportation have several noteworthy approaches to solutions.

Rail and inter-modal is a complex system composed by different transport networks, infrastructures, different transport means and operators, such as dryage operators, terminal operators, network operators and others. Intermodal means there are lots of decision makers who must work in a coordinated manner for the system to run smoothly. If intermodal transport is to be developed, it will require more decision-making support tools to assist the decision-makers and stakeholders.

When train operations are perturbed, a new conflict free timetable must be recomputed such that the deviation from the original must be minimized. This scheduling problem is modeled with an alternative graph formulation and a branch and bound algorithm is developed. Some approaches use an integrated framework which deals with signal layout optimization, train scheduling optimization at microscopic level, and others.

Heuristic approaches include a look-ahead greedy heuristic and a global neighborhood search algorithm, in terms of railway total train delay. Scheduling additional train services to be integrated into the current timetables is a problem that is modeled as a hybrid job shop scheduling techniques that operate upon a disjunctive graph model of trains.

One approach d              evelops a train slot selection model based on multicommodity network flow concepts for determining freight train timetables. This helps to schedule rail services along multiple interconnected routes. This model seeks to minimize operating costs incurred by the carriers and delays incurred by the shippers. The schedules and demand levels are ensured to be mutually consistent. When the model is embedded in a simulation, it can be used iteratively and together with the output of the scheduling solution.

Another approach solves the freight transportation on hybrid rail networks used to transport both passengers and freight. It uses a preferred timetable as input for each freight train. Some overrides are permitted such as specifying a path different from the one in the ideal timetable. It’s objective is to introduce as many new freight trains as possible by assigning them timetables that are as close as possible to the ideal ones. An integer linear programming method is used in the model.

A third approach specifically takes into account the double-track train scheduling. It focuses on the high-speed passenger rail line in an existing network and minimizes both the expected wait times for high speed trains and the total travel times of both speed trains. Using the priority for speed, the problem is translated as multi-mode resource project. It is then solved for scheduling with a branch and bound algorithm and a beam search algorithm.

The autonomous fleet management differs from these in that the destinations must be determined. If the dependencies can be expressed in quadratic form, then the steepest gradient method can point to the optimum destination.

In continuation of a set of types of problems in fleet management science, Rail and inter-modal transportation have several noteworthy approaches to solutions.

Several interesting algorithms have been proposed for the Rail and Intermodal problem space. This is a complex system composed by different transport networks, infrastructures, different transport means and operators, such as dryage operators, terminal operators, network operators and others. Intermodal means there are lots of decision makers who must work in a coordinated manner for the system to run smoothly. If intermodal transport is to be developed, it will require more decision-making support tools to assist the decision-makers and stakeholders.

When train operations are perturbed, a new conflict free timetable must be recomputed such that the deviation from the original must be minimized. This scheduling problem is modeled with an alternative graph formulation and a branch and bound algorithm is developed. Some approaches use an integrated framework which deals with signal layout optimization, train scheduling optimization at microscopic level, and others.

Heuristic approaches include a look-ahead greedy heuristic and a global neighborhood search algorithm, in terms of railway total train delay. Scheduling additional train services to be integrated into the current timetables is a problem that is modeled as a hybrid job shop scheduling techniques that operate upon a disjunctive graph model of trains.

One approach presented a two phased train set routing algorithm to cover a weekly train timetable with minimal working days of a minimal number of train sets. The first step involves relaxing maintenance requirements and obtaining minimum cost routes by solving the polynomial relaxation. Then maintenance-feasible routes are generated from the crossovers of the minimum cost routes. This pragmatic approach seems particularly effective for the high-speed railways system which is simpler, with fewer end stations and higher frequency of trains.

Corman et al addressed the problem of train conflict detection and resolution that became quite popular among traffic controllers. This approach proposed a family of techniques referred to as the Railway Optimization by Means of Alternative graphs and it involved effective rescheduling algorithms and local rerouting strategies in a tabu search scheme. A fast heuristic and a truncated branch and bound algorithm are alternate for computing train schedules within a short computation time. The effectiveness of using different neighborhood structures can be investigated for train rerouting. Tabu search is known to be faster than ROMA. Another approach proposed a train slot selection model based on multicommodity network flow concepts for determining freight train timetables for scheduling rail services along multiple interconnected routes. The model seeks to minimize operating costs incurred by carriers and delays incurred by shippers while ensuring that the schedules and demands are mutually consistent.

 

Another problem space that defines fleet management in its own way is Rail and intermodal transport. This is a complex system composed by different transport networks, infrastructures, different transport means and operators, such as dryage operators, terminal operators, network operators and others. Intermodal means there are lots of decision makers who must work in a coordinated manner for the system to run smoothly. If intermodal transport is to be developed, it will require more decision-making support tools to assist the decision-makers and stakeholders.

For example, the operational level involves the day-to-day management decisions about the load order of trains and barges, redistribution of railcars or push barges and load units. The fleet comprises of load units. The assignment of a set of trailers and containers to the available flatcars that can move the equipment is a classic problem in this space. Routing involves a lot  more than that. While the minimum cost path algorithm was common to road network, in this case, the routing decision is a mere modal choice problem for specific trajectories between beginning and end points and involving specific freight volumes and specific time constraints.

Container transportation is a major component of intermodal transportation carried out by a combination of truck, rail and ocean shipping. Fleet management covers  the whole range of planning and management issues from procurement of power units and vehicles to vehicle dispatch and scheduling of crews and maintenance operations. But rail transport is characterized by different kinds of trains travelling on the network and the subdivision of resources between passenger and freight trains. The train scheduling and routing must consider their timetables.

When train operations are perturbed, a new conflict free timetable must be recomputed such that the deviation from the original must be minimized. This scheduling problem is modeled with an alternative graph formulation and a branch and bound algorithm is developed. Some approaches use an integrated framework which deals with signal layout optimization, train scheduling optimization at microscopic level, and others.

 

Another problem space that defines fleet management in its own way is Maritime support. This is not restricted to sea borne transportation and includes water-borne transportation as well. Ships and ports, their logistics and containers management and interconnections among vessels characterize this field.

There are three modes of transportation in maritime – liner, industrial and tramp. Adjustments to fleet size and mix, fleet deployment, ship routing and scheduling are some of the tactical problems. As with the airline transportation discussed earlier, the issue of robustness needs to be addressed here as well as disruptions are common. Therefore, robustness is factored into the optimization models used for planning.

Methodologies used towards solutions include input parameters, deterministic models that incorporate penalties and stochastic optimization models. One of the established techniques uses fleet composition and mix routing while another uses decision support methodology for strategic planning in tramp and industrial shipping. This involves simulation and optimization, where a Monte-Carlo simulation framework is built around an optimization-based decision support system and short term routing and scheduling using a rolling horizon principle where information is revealed as time goes by. This helps with a wide range of strategic planning problems.

When it comes to ferry scheduling, the analogy from public transit system discussed earlier hold true. The common objective is the minimization of costs and maximization of customer satisfaction. Decreasing the operational costs and reducing the travel time and waiting time for passengers requires reevaluation and improvements to the ferry schedule. There is a ‘logit’ model that determines’ the passengers’ service choices. This is used by the formulation to determine the best mixed-fleet operating strategy, including interlining schemes, so as to minimize the objective function that combines both the operator and passengers’ performance measures.