Another class of problems in fleet management aside from those discussed, is the one concerning air transport. This is characterized by network design and schedule construction, fleet assignment, aircraft routing, crew scheduling, revenue management, irregular operations, air traffic control and ground delay programs, gate assignment, fuel management, short term fleet assignment swapping. They were mostly solved by operation research techniques and the majority of applications utilized network-based models. 

The airline scheduling process is carried out sequentially so that flight, aircraft and schedules are created one after another over several months prior to the day of the operations. A detailed flight schedule might be based on marketing decisions. The first step in operational scheduling is the assignment of an aircraft fleet type to each flight and is based on the demand forecasts, the capacity and the availability of the aircrafts. After fleet assignment, an aircraft is assigned to each flight with respect to maintenance constraints such as aircraft routing. Crew scheduling can be broken down into two steps. The first phase is called crew pairing and it involves anonymous crew itineraries subject to constraints such as maximum allowed working time or flying time per duty. The second phase is crew rostering, and it involves assigning individual crew members to the itineraries. The goal of this scheduling process is to reduce costs. 

Fleet routing and fleet scheduling also affect costs but it determines the airline’s level of service and its competitive capability in the market. Network flow techniques are adopted for modeling and solving such complex mathematical problems. The full optimization problem can be hard so they are solved in parts sequentially. The output of one is input to the next. 

The limitations of the sequential approach were subsequently solved with an integrated approach that reduces costs even more. 

The fleet assignment problem deals with assigning aircraft types, each having a different capacity to the scheduled flights, based on equipment capabilities and availability, operational costs and potential revenues. When there are many flights each day, this problem becomes difficult. Some remediations include: 1) integrating the FAP with other decision processes such as schedule design, aircraft maintenance routing, and crew scheduling, 2) proposing solution techniques that introduces additional parameters and constraints into the traditional fleeting models, such as itinerary based demand forecasts and the recapture effect and 3) studying dynamic fleeting mechanisms that update the initial fleeting solution as departures approach and more information is gathered on demand patterns. In a few models, a non-linear integer multi-commodity network flow is formulated, and new branch-and-bound strategies are developed. 

Traffic disruptions are one characteristic of this problem space. This might lead to an infeasible aircraft and crew schedules on the day of the operations and the recovery to reasonable schedule must be attempted. The short-term recovery actions might increase operational costs, sometimes even higher than the planned costs. Recovery options could be factored into the scheduling at the design time and this approach is generally called robust scheduling. Sometimes this is articulated as a measure. For example, a non-robustness measure is used to penalize restricted aircraft changes according to the slack time during an aircraft change. 

Global stochastic models have been attempted to be solved with an iterative approach. The iterative approach yields a set of different solutions regarding the trade-offs between the costs and robustness whereas an integrated approach returns mostly one near-optimal solution for a given robustness penalty. Iterative approach is more favorable to a decision maker. When multiple airlines must coordinate, the models are formulated as multiple commodity network flow problems which can be solved by programs based on mathematical formulations. 

 

 Another class of problems in fleet management aside from those discussed, is the one concerning air transport. This is characterized by network design and schedule construction, fleet assignment, aircraft routing, crew scheduling, revenue management, irregular operations, air traffic control and ground delay programs, gate assignment, fuel management, short term fleet assignment swapping. They were mostly solved by operation research techniques and the majority of applications utilized network-based models.

The airline scheduling process is carried out sequentially so that flight, aircraft and schedules are created one after another over several months prior to the day of the operations. A detailed flight schedule might be based on marketing decisions. The first step in operational scheduling is the assignment of an aircraft fleet type to each flight and is based on the demand forecasts, the capacity and the availability of the aircrafts. After fleet assignment, an aircraft is assigned to each flight with respect to maintenance constraints such as aircraft routing. Crew scheduling can be broken down into two steps. The first phase is called crew pairing and it involves anonymous crew itineraries subject to constraints such as maximum allowed working time or flying time per duty. The second phase is crew rostering, and it involves assigning individual crew members to the itineraries. The goal of this scheduling process is to reduce costs.

Fleet routing and fleet scheduling also affect costs but it determines the airline’s level of service and its competitive capability in the market. Network flow techniques are adopted for modeling and solving such complex mathematical problems. The full optimization problem can be hard so they are solved in parts sequentially. The output of one is input to the next.

The limitations of the sequential approach were subsequently solved with an integrated approach that reduces costs even more.

The fleet assignment problem deals with assigning aircraft types, each having a different capacity to the scheduled flights, based on equipment capabilities and availability, operational costs and potential revenues. When there are many flights each day, this problem becomes difficult. Some remediations include: 1) integrating the FAP with other decision processes such as schedule design, aircraft maintenance routing, and crew scheduling, 2) proposing solution techniques that introduces additional parameters and constraints into the traditional fleeting models, such as itinerary based demand forecasts and the recapture effect and 3) studying dynamic fleeting mechanisms that update the initial fleeting solution as departures approach and more information is gathered on demand patterns. In a few models, a non-linear integer multi-commodity network flow is formulated, and new branch-and-bound strategies are developed.

Traffic disruptions are one characteristic of this problem space. This might lead to an infeasible aircraft and crew schedules on the day of the operations and the recovery to reasonable schedule must be attempted. The short-term recovery actions might increase operational costs, sometimes even higher than the planned costs. Recovery options could be factored into the scheduling at the design time and this approach is generally called robust scheduling. Sometimes this is articulated as a measure. For example, a non-robustness measure is used to penalize restricted aircraft changes according to the slack time during an aircraft change.

Global stochastic models have been attempted to be solved with an iterative approach. The iterative approach yields a set of different solutions regarding the trade-offs between the costs and robustness whereas an integrated approach returns mostly one near-optimal solution for a given robustness penalty. Iterative approach is more favorable to a decision maker. When multiple airlines must coordinate, the models are formulated as multiple commodity network flow problems which can be solved by programs based on mathematical formulations.

 

 

In continuation of a set of types of problems in fleet management area, Urban public transport and dial-a-ride are more recent.

Urban public transport deserves to be called a class of problems by itself. It consists of determining ways to provide good quality of service to passengers with finite resources and operating costs. Their planning process often involves 1. Network route design, 2. Frequency setting and timetable development, 3. Vehicle scheduling and 4. Crew scheduling and rostering. Some state-of-the-art models involve tuning the routing and scheduling with minimization of passenger cost functions. Metaheuristics schemes that combine simulated annealing, tabu, and greedy search methods serve this purpose. One of the distinguishing features of this problem space is that customers often formulate two requests per day, specifying an outbound request from pick-up to drop-off and an inbound request for the round trip. Another feature is that the quality of service needs to be maximized while minimizing operating costs incurred to satisfy all the requests.

Dial-a-ride transport is a move toward more economical and greater flexibility of transport services. Demand responsive transportation systems require the planning of routes and customer pick-up and drop-off scheduling on the basis of received requests. It must deal with multiple vehicles with limited capacity and time-windows. The problem of working out optimal routes and times is referred to as the Dial-a-ride problem. As with many problem spaces in fleet management, this can be treated as NP-hard combinatorial optimization problem. Attempts to develop an optimal solution has been limited to simple and small-size problems.

Such a service may operate in a static or dynamic mode. In the static settings, all the customer requests are known beforehand, nd the system solves a tour each vehicle must make within the constraints of the pick-up and drop-off time window and minimizing the solution cost. In the dynamic mode, the customer requests arrive over time to a control station and the solution may change over time. Processing must also keep up with the incoming rate without interfering with the optimization cycle at the end of the service. The goal is two-fold: reduce overall costs and improve quality of service to customers. Several algorithms have been tried for this purpose. They include tabu search heuristics, dynamic programming, branch and cut, a heuristic two phase solution method, genetic algorithms and variable neighborhood search.

Even the users can be differentiated as well as the transportation modes. Some meta-heuristics such as vehicle waiting time with passenger onboard can also be used with branch-cut algorithms for this purpose.

Studying quality of service in this context has evolved into models which use various measurement scales. The quality of service provided by organizations also depend on the type of organization and the operational rules used.

 Fleet Management continued...

The need for fleet management arose from the requirements of passengers and freight transportation services. Usually, their fleet is considered heterogeneous because it includes a variety of vehicles. Some of the fleets must perform tasks that may be known beforehand or are done repetitively. Most of them respond to demand. The scale and size of the fleet can be massive.

Vehicle routing and scheduling is one such class of problems. A fleet of vehicles with limited capacity based at one or several depots must be routed serving a certain number of customers to minimize the number of routes, total traveling time and the distance traveled. Additional restrictions can specialize this class of problems with time windows where each customer is served in a specified time interval. This class of problems is central to the field of transportation, distribution, and logistics.

Dynamic fleet management is another class of problems. While classical fleet management problems address routing and scheduling plans, unforeseen events might force additional requirements. When communication is leveraged to get this additional information, real-time usage of fleet resources can be improved. The changes in vehicle location, travel time and customer orders can be used with an efficient re-optimization procedure for updating the route plan as dynamic information arrives. When reacting to real-time events leaves no time, it can be worked around by finding ways to anticipate future events in an effective way. Data processing and forecasting methods, optimization-simulation models, and decision heuristics can be included to improve comprehensive decision-support systems.

Another field of increasing interest is the urban freight transportation and the development of new organizational models for management of freight. As for any complex systems, city logistics transportation systems require planning at strategic, tactical, and operational levels. While wide area road networks require routing based on distances, that within the city logistics network demands time-dependent travel times estimates for every route section. While static approaches are well studied, time-dependent vehicle routing still appears to be unexplored. One of the ways to bridge this gap has been to use an integration framework that brings dedicated systems together for a holistic simulation that performs something like a dynamic router and scheduler.

Urban public transport deserves to be called a class of problems by itself. It consists of determining ways to provide good quality of service to passengers with finite resources and operating costs. Their planning process often involves 1. Network route design, 2. Frequency setting and timetable development, 3. Vehicle scheduling and 4. Crew scheduling and rostering. Some state-of-the-art models involve tuning the routing and scheduling with minimization of passenger cost functions. Metaheuristics schemes that combine simulated annealing, tabu, and greedy search methods serve this purpose. One of the distinguishing features of this problem space is that customers often formulate two requests per day, specifying an outbound request from pick-up to drop-off and an inbound request for the round trip. Another feature is that the quality of service needs to be maximized while minimizing operating costs incurred to satisfy all the requests.

Fleet Management

 

The need for fleet management arose from the requirements of passengers and freight transportation services. Usually, their fleet is considered heterogeneous because it includes a variety of vehicles. Some of the fleets must perform tasks that may be known beforehand or are done repetitively. Most of them respond to demand. The scale and size of the fleet can be massive.

The complexity is clearer in the case of public transport which usually has a scheduled transportation network. They use techniques and ideas from mathematics as well as computer science. Tools and concepts include graph and network algorithms, combinatorial optimizations, approximations and online algorithms, stochastic and robust optimization. Newer models and algorithms can improve the productivity of resources, efficiency, and network capacity. One of the ways to do that has been to leverage a database and use parameterized queries. The order of the data in the database provides just the right framework for the query methods to return an accurate and complete set of results. The results might differ on consistency levels, responsiveness and coverage depending on whether the relational, batch or streaming mode was used.

When the transportation problems were modeled, they were often treated as combinatorial optimization problems which included vehicle routing, scheduling, and network design. These are notoriously difficult to solve, even in a static context. This led to the need for a human dispatcher in many fleet management scenarios. Emergence of powerful computing including meta-heuristics, distributed and parallel computing has now made that somewhat easier. One of the main challenges is the need to handle dynamic data.

Vehicle routing and scheduling is one such class of problems. A fleet of vehicles with limited capacity based at one or several depots must be routed serving a certain number of customers to minimize the number of routes, total traveling time and the distance traveled. Additional restrictions can specialize this class of problems with time windows where each customer is served in a specified time interval. This class of problems is central to the field of transportation, distribution, and logistics.

Mathematical formulations of this class of problems have bounded certain parameters and changed the criteria to obtain approximate solutions instead of optimal ones because the class of problems is inherently an NP-hard problem. In the last fifteen years, an incremental amount of metaheuristic algorithms has been designed. These include simulated annealing, genetic algorithms, artificial neural networks, tabu search, ant colony optimization, Greedy Randomized adaptive search procedure, Guided local search and variable neighborhood search along with several hybrid techniques. Local search is the most frequently used heuristic technique for solving combinatorial optimization problems. Sequential search is a general technique for the efficient exploration of local search neighborhoods. One of its key concepts is the systematic decomposition of moves, which allows pruning options within the local search based on associated partial gains.

 

One of the benefits of migrating workloads to the public cloud is the savings in cost. There are many cost management functionalities available from the AWS management console but this article focuses on the a pattern that works well across many migration projects.

This pattern requires us to configure user-defined cost allocation tags. For example, let us consider the creation of detailed cost and usage reports for AWS Glue Jobs by using AWS cost explorer. These tags can be created for jobs across multiple dimensions and we can track usage costs at the team, project or cost center level. An AWS Account is a prerequisite. AWS Glue jobs uses other AWS Services to orchestrate ETL (Extract, Transform and Load) jobs to build data warehouses and data lakes. Since it takes care of provisioning and managing the resources that are required to run our workload, the costs can vary. The target technology stack comprises of just these AWS Glue Jobs and AWS Cost Explorer.

The workflow includes the following:

1.       A data engineer or AWS administrator creates user-defined cost-allocation tags for the AWS Glue jobs

2.       An AWS administrator activates the tags.

3.       The tags report metadata to the AWS Cost Explorer.

The steps in the path to realize these savings include the following:

1.       Tags must be added to an existing AWS Glue Job

a.       This can be done with the help of AWS Glue console after signing in.

b.       In the “Jobs” section, the name of the job we are tagging must be selected.

c.       After Expanding the advanced properties, we must add new tag.

d.       The key for the tag can be a custom name and the value is optional but can be associated with the key.

2.       The tags can be added to a new AWS Glue Job once it has been created.

3.       The administrator activates the user-defined cost allocation tags.

4.       The cost and usage reports can be created for the AWS Glue Jobs. These include:

a.       Selecting a cost-and-usage report from the left navigation pane and then creating a report.

b.       Choosing “Service” as the filters and applying them. The tags can be associated with the filters.

c.       Similarly, team can be selected and the duration for which the report must be generated can be specified.

This pattern is repeatable for cost management routines associated with various workloads and resources.

 Migrating remote desktops 

Most migrations discuss workloads and software applications. When it comes to users, identity federation is taken as the panacea to bring all users to the cloud. But migrating remote desktops for those users is just as important for those users when they need it. Fortunately, this comes with a well-known pattern for migration. 

Autoscaling of virtual desktop infrastructure (VDI) is done by using NICE EnginFrame and NICE DCV Session Manager. NICE DCV is a high performance remote display protocol that helps us stream remote desktops and applications from any cloud or data center to any device, over varying network conditions. When used with EC2 instances, NICE DCV enables us to run graphics-intensive applications remotely on EC2 instances and stream their user interfaces to commodity remote client machines. This eliminates the need for expensive dedicated workstations and the need to transfer large amounts of data between the cloud and client machines. 

The desktop is accessible through a web-based user interface. The VDI solution provides research and development users with an accessible and performant user interface for submitting graphics-intensive analysis requests and reviewing results remotely 

The components of this VDI solution include: VPC, public subnet, private subnet, an EngineFrame Portal, a Session Manager Broker, and a VDI Cluster that can be either Linux or Windows. Both types of VDI Clusters can also be attached side by side via an Application Load Balancer. The user connects to the AWS Cloud via another Application Load Balancer that is hosted in a public subnet while all the mentioned components are hosted in a private subnet. Both the public and the private subnets are part of a VPC. The users request flows through the Application Load Balancer to the NICE EngineFrame and then to the DCV Session Manager. 

There is an automation available that creates a custom VPC, public and private subnets, an internet gateway, NAT Gateway, Application Load Balancer, security groups, and IAM policies. CloudFormation is used to create the fleet of Linux and Windows NICE DCV servers. This automation is available from the elastic-vdi-infrastructure GitHub repository. 

The steps to take to realize this pattern are listed below: 

1. The mentioned code repository is cloned. 

2. The AWS CDK libraries are installed. 

3. The parameters to the automation script are updated. These include the region, account, key pair, and optionally the ec2_type_enginframe and ec2_type_broker and their sizes 

4. The solution is then deployed using the CDK commands 

5. When the deployment is complete, there are two outputs: Elastic-vdi-infrastructure and Elastic-Vdi-InfrastruSecretEFadminPassword 

6. The fleet of servers is deployed with this information 

7. The EnginFrame Administrator password is retrieved and the portal is accessed. 

8. This is then used to start a session. 

This completes the pattern for migrating the remote desktops for users.