Some more comparisons with contemporary fleet management software follow the previous post.

This article surveys the contemporary automations and software available from the industry.

Most usages of fleet management software such as the open source mentioned above are in the areas of food delivery, emergency services, utility companies, construction, landscaping, public transportation, courier and package delivery services. The users use these software to reduce labor and gas costs, remain in compliance with state and federal regulations, locate and track fleet vehicles, manage vehicle maintenance, improve fleet and driver safety and optimize cost savings.

Commercial fleet management software like the Autosist is primarily a fleet inventory management system with a platform that charges per month per asset if paid annually.  They offer flexible pricing options no matter the size of the fleet which entices small businesses. With the focus on process automation, commercial software provides proprietary software resources and policies for fleet management.

These software applications make it easy to keep track of drivers in the field, plan routes as efficiently as possible to save on fuel costs and stay one step ahead of maintenance tasks. They support GPS vehicle tracking, timesheet tracking, shift and route assignments and group messaging with drivers.

Some are built directly on the cloud. For example, Samsara is a cloud-based fleet management solution that offers features such as GPS tracking, trailer tracking, dashboard camera, routing and dispatch and reefer monitoring. It helps to track the physical location of their fleets and monitor their drivers’ behavior to stay compliant with Electronic Log Book (ELD) and Federal Motor Carrier Safety Administration (FMCSA) regulations which encourage safer work environment for commercial motor vehicle drivers and truck drivers.

FleetIO for instance automates multiple complex management operations, including asset life cycles, fuel efficiency, safety reports, documents associated with the vehicle such as fuel and service receipts and tiles and insurance cards. Cross-platform access and programmability is key to intermodal network. Webhooks and APIs continue to help synchronize the data between disparate networks. There’s also a commenting, photo and notification element to add on, which allows for instant feedback.

Companies like Azuga even provide hardware for fleet managers to install on their vehicles. It tracks equipment and driver behavior. It believes in creating healthy competition among drivers and applies gamification in its driver rewards program to help reward drivers often to prevent churn.

Tracking and alerts are another feature of such commercial software. Some provide real-time tracking while others provide slower refresh but with detailed alerts.

 

 An earlier article introduced us to some of the algorithms and models in fleet management as applied to different problem spaces which included Vehicle routing and scheduling, dynamic fleet management, city logistics, urban public transport, Dial-a-ride transport, air-transport, Maritime transport and Rail and intermodal transport,

Most usages of fleet management software such as the open source mentioned above are in the areas of food delivery, emergency services, utility companies, construction, landscaping, public transportation, courier and package delivery services. The users use these software to reduce labor and gas costs, remain in compliance with state and federal regulations, locate and track fleet vehicles, manage vehicle maintenance, improve fleet and driver safety and optimize cost savings.

Commercial fleet management software like the Autosist is primarily a fleet inventory management system with a platform that charges per month per asset if paid annually.  They offer flexible pricing options no matter the size of the fleet which entices small businesses. With the focus on process automation, commercial software provides proprietary software resources and policies for fleet management.

These software applications make it easy to keep track of drivers in the field, plan routes as efficiently as possible to save on fuel costs and stay one step ahead of maintenance tasks. They support GPS vehicle tracking, timesheet tracking, shift and route assignments and group messaging with drivers.

An earlier article introduced us to some of the algorithms and models in fleet management as applied to different problem spaces which included Vehicle routing and scheduling, dynamic fleet management, city logistics, urban public transport, Dial-a-ride transport, air-transport, Maritime transport and Rail and intermodal transport,

This article surveys the contemporary automations and software available from the industry. We start with the open source which appear to address and automate complex software processes that include dispatch management, GPS-based vehicle tracking, route optimization, vehicle maintenance, and fuel management. Open source is particularly relevant to low-cost deployments. Everlance, Kuebix and Odoo are our picks for comparison.  Everlance is a mileage tracking and expense management software solution for businesses with small and large fleets. It uses GPS tools to keep a record of the trips taken by drivers to ensure location accuracy. Drivers can keep Everlance to track and report mileage rates on the field. Fleet Managers can track trip frequencies and suggest optimized routes to drivers. Everlance offers cloud-based deployments as well. Kuebix is a transportation management solution that enables fleet manager to keep track of dispatches. It features include dispatch management, fleet management, routing, shipping, and carrier management. Both cloud based and on-premise deployments are available. Odoo app suite is a customizable open source software suite that provides business solutions to a variety of industries, including fleet management. Odoo allows users to save service records, contracts, vehicle tags, and the make and model of the vehicles in their fleets. The reporting module has graphs and charts for visualization. Cloud based deployments as well as mobile applications are available.

Most usages of fleet management software such as the open source mentioned above are in the areas of food delivery, emergency services, utility companies, construction, landscaping, public transportation, courier and package delivery services. The users use these software to reduce labor and gas costs, remain in compliance with state and federal regulations, locate and track fleet vehicles, manage vehicle maintenance, improve fleet and driver safety and optimize cost savings.

Commercial fleet management software like the Autosist is primarily a fleet inventory management system with a platform that charges per month per asset if paid annually.  They offer flexible pricing options no matter the size of the fleet which entices small businesses. With the focus on process automation, commercial software provides proprietary software resources and policies for fleet management.

 

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.