We have provided a demonstration of how virtual reality VR can benefit training processes was geared toward Marines within the aircraft armament systems and munition systems, and gave a glimpse of how VR applications can support in providing an enhanced experience to preparation of aircraft for combat missions.
Aircraft armament systems Marines are responsible for maintaining launch and release devices on aircraft. This means that when a pilot pulls the trigger, the devices successfully launch away from the aircraft toward the intended target.
“It’s a way to build the readiness and experience level by leveraging advanced technologies. In the past, we received this level of experience because the weapon systems were in need of constant repair and maintenance. Now, our systems are more advanced, and it’s hard to practice difficult repairs
“We can build our skill sets and proficiency faster by not having an aircraft break to perform the training. We could break one virtually at any time, any place. VR is a unique way to fully train while still maintaining our mission capable rate.”
In this demonstration, Marines experienced an immersive VR training scenario, put on a head-mounted display for VR application and used hand-held devices for training scenarios.
The immersive VR scenario allowed users to walk inside a hangar with a piece of munition positioned for maintenance. The user could look around the hangar, interact with the munition, pull up the technical order in a full-view mode or even watch a video of someone successfully installing that specific item on the munition. Essentially, the user could take apart and reassemble a munition from the barracks.
“In a controlled setting, VR allows for instant immersion into the field to help Marines understand the content better, faster.”
If VR is fully implemented into its training processes, Marines could have virtual hands-on experience much earlier in their careers, which could bridge the training-to-experience gap challenge the Service now faces.
The in-garrison mission may be different from the deployed mission. That gap can become noticeable if a Marine who has a home-station duty on a certain airframe or munition deploys and must work with unfamiliar equipment or in a joint environment. VR could be used as recurrent or just-in-time training to bolster the combat capabilities of users when they are deployed.
Demonstrations like these are designed to combat today’s challenges through innovation and collaboration among top subject matter experts. It’s a way to increase combat capability and solve complex security issues by partnering with experienced organisations to create platforms to house the application.
Technology will be transformative, but it is a long-term solution typically reflected in procurement processes that take too long.. Some forces have only just recently introduced or are in the process of introducing new combat capabilities. This means the opportunity to influence platform efficiency will be very limited for some time yet.
Platform efficiency refers to the application of technology to minimise the amount of logistics support required to deliver and sustain forces. This logistics strategy has the least ability to influence outside of describing logistic costs to key decision makers in the acquisition process.
Fortunately, the next strategy for reducing logistic demand – force efficiency – is an option that can be implemented now. Force efficiency refers to initiatives which require fewer force elements to achieve a desired effect. In developing system-capability, the organic intelligence, surveillance and reconnaissance available to brigade combat teams, coupled with precision fires complemented and enhanced the capability of the medium-weight nature of the platform.
In this case, force efficiency didn’t deliver operational effectiveness – at least in terms of the operations system subsequently find itself in. Even so, we are continuously reminded that the combination of modern armed, and increasingly cheap, UAV’s supported by surveillance capabilities and guided weapons offer forces firepower with little permanent presence on the ground and logistics cost as a consequence.
In terms of logistics-specific activities there are other force efficiency opportunities that are currently being undertaken. Adopting common components, ammunition and other items, and standardisation across coalition boundaries greatly simplifies supply between partners.
Collectively, and in an operational environment, there may be possibilities to share capabilities and prevent the unnecessary duplication of effort. Elsewhere, the modularisation of vehicle components, supported by information systems that better predict maintenance requirements, has been touted as offering opportunities to improve force efficiency.
Implemented effectively, this approach limits the need to forward position maintenance personnel with most deep repair occurring rearward but this approach can make a maintenance problem a distribution one. Self-offloading distribution vehicles, or more effective ways to store and package supplies, also exemplify a force efficiency strategy.
Force efficiency can also be improved through conceptual means. At the macro level, land forces – as part of joint forces – can achieve greater efficiencies by removing duplicate functions, or if demand can’t be reduced, sharing functions to create greater opportunities. This approach is a cornerstone of the multi-domain battle concept.
Approaches to logistics include where modularised logistic capabilities are surged to support particular missions and tasks for limited time periods, also offers the prospect of improving force efficiency.
Rethinking assumptions about who ‘owns’ what in the battlespace, and the logistic control methods such as ‘lines’ or ‘levels’ of logistics support must be part of future logistic transformation efforts. Development of land forces tolerates the inevitable periods where limited logistic support must be directed away from one unit to another to support combat operations.
Closely aligned to force efficiency is personnel efficiency. An example of personnel efficiency, whereby less personnel are required to do a particular job, by ‘mixing’ tasks such as armoured fighting vehicle operations and maintenance.
Noting the training burden and competency risk it imposes, some small units extensively cross-train limited logistic s personnel; where land terminal, movements and aerial delivery personnel come from a base trade. There is no particular reason that the skills possessed by personnel from logistics or combat arms cannot be similarly transferred between one another in such a way.
Technology can also support personnel efficiency, and is being rigorously pursued by forces as a way of enhancing the effect of each deployed soldier contributing to the active force. Examples of such include modernising ‘logistics information systems’ and ‘common operating pictures’, both of which promise to improve supply chain performance thereby enhancing the capacity to respond to emergent tactical requirements.
The final strategy is mission focus applicable to militaries who have transitioned their forces to enable consistent, rotatable and available combat elements, Mission focus refers to the specialisation of formations for particular tasks thus avoiding the costly logistics capabilities that might enable the formation to be prepared for all tasks, or those tasks which might be perceived as unlikely.
There are, however, inventive ways in which land forces can be structured appropriately to achieve mission focus without abandoning preparedness-based force design methodologies. Temporary allocations of modularised logistic capabilities based upon emerging operational requirements is perhaps the best-known method and should be rigorously applied in future attempts to transform land forces. Even so, land forces should always be prepared to abandon force design models which are based upon an assumption of being able to ‘do it all’ when the need arises, and prepare logistics capabilities accordingly.
As required and when necessary, units can be tasked to support the readiness division and be deployed to a theater of operation to provide logistics support to include an aviation maintenance slice. As aviation material is retrograded from the battlefield, critical aviation components are classified and repaired before they enter the depot pipeline.
Fixed-base, limited depot facilities units are capable of deploying to a theater of operations, given enough time for movement to the deployment location. Once mobilised and deployed, support primarily from a fixed base is provided capable of projecting forward limited, task-organised support using maintenance contact teams and classification support teams.
The purpose of this report is to create Logistics Support plan to integrate all equipment upgrade/repair work order tasks, identify dispatch responsibilities & activities, and outline approach toward accomplishing field-level mission requirements. Here we present inclusion of the following elements of information, with range/depth of information for each element tailored to the acquisition phase of critical equipment.
Dispatch structure & authorities applicable to logistics support plan can be described by detailing associations between line, service, staff & policy organisations.
Identification/assignment of each logistics support work order task and how each will be performed are subject to many applicable major tradeoffs. Schedule interactions with system engineering activities impacting estimated start and completion points for each logistics support programme activity or task must be identified.
Work Order Breakdown Structure identification of items to be acted upon will be performed and documented. Identification of logistics support candidate lists & selection criteria must include all items recommended for review, items not recommended & appropriate rationale for selection or non-selection.
Dispatch techniques for design requirements related to equipment item support must be disseminated to suppliers and controls levied under such circumstances. Efforts directed at update/validate of logistics support information must include configuration control procedure requirements for end items of support equipment provided by supplier.
New creation of applicable procedures must evaluate status/control of each work order task with identification of organisation unit with authority/responsibility for executing each task. Controls for identifying and recording design problems or deficiencies affecting supportability, corrective actions required & status of actions taken to resolve problems must be employed.
We recommend Information collection systems to be used by performing activity must document, disseminate, and control logistics support design specs alongside description of subsystem to be used and identification of validated status when independent applications are deployed.
Efforts must include description of how information from work orders tasks will interface with other logistics support system oriented factors to include consideration of equipment criticality and required reporting interactions with the following programmes, as applicable.
Advancements in technology with virtual reality are changing every day. It’s a process many people are not even aware of or know little about. There is no limit to what can be done virtually. Many of the tasks done currently will be completely different whether it’s something with designing components, quoting, or even meetings. Virtual reality is aiding in connecting troops in any location.
Work space floor layouts can be optimised to ensure success before going in and moving equipment. Rather than having to tie several people up to move equipment around only to realise that the selected location won’t work, it’s possible to have just one person using only one hand to move equipment around with virtual reality.
Scheduling in a job shop is can have a significant impact on the performance of the shop floor. The job shop scheduling problem for jobs are to be processed by machines or work stations within a given time period so performance objectives are optimised.
Plans are in the works to extend single-machine rule learning approach to more complex shop configurations. The first rule is to learn a centralised sequencing rule that governs all machines.
The second more effective approach is to allow each machine to learn its individual sequencing rule so each machine considers a number of factors in its learning process, for example its position in the production system, the amount of work that flows downstream to the machine, the amount of work the machine sends downstream and so on. This decentralised approach is required for extending the learning approach to more complex scenarios.
Dispatching rules examine all jobs awaiting processing at a given machine at each point in time that machine becomes available, computing a priority index for each job, using a function of attributes of the jobs present at the current machine and its immediate surroundings. The job with the best index value is scheduled next at the machine.
Most dispatching rules only consider local and current conditions so it is required to investigate operations such as predicting the arrival times of jobs from previous stations and limited, local optimisation of the schedule at the current machine.
Here we present dispatching rules for scheduling in a job shop. These rules combine process-time and work-content in the queue for the next operation on a job. Rules make use of information about the process-time, work-content of jobs in the queue for the next operation on a job and due date to minimise as many measures of performance at the same time as possible. When performance of known dispatching rules is evaluated it is clear no single rule is effective in minimising all measures of performance.
Each job consists of a specific set of operations which have to be processed
according to technical precedence order logistics supply routes. Scheduled jobs can either be available at the beginning of the scheduling process or set of jobs processed is continuously changing over time.
When problem produces the same output from starting condition or initial state all parameters are known with certainty. If at least one parameter is likely to be the case, release times of the jobs the problem has a random distribution/pattern cannot be predicted precisely.
To determine when the system reaches the steady-state, shop parameters, such as utilisation level of machines, mean flowtime of jobs, etc. must be observed continuously. The shop reaches a steady-state when job orders are completed.
The aim of the planning process is to find a schedule for processing all jobs optimising one or more goals for instance, minimising mean flowtime or minimising the effect of not being on time. It appears possible to determine optimal schedules when problem parameters are known, but in practice the computation of optimal solutions is impossible.
But it is possible to generate optimal schedules using design tools to solve combinatorial problems even when time requirements for calculating optimal processing orders for a job shop scheduling problem occurring in practice is beyond any scope of time.
When jobs arrive continuously in time the release times, logistics supply routings and processing times of the jobs have problem parameters not known in advance because random distribution/pattern is likely to occur. When scheduling problems are randomly changing over time it is not possible to compute optimal schedules in advance.
Sometimes jobs currently in the shop processing sequences on the various machines can be determined. The decision as to which job is to be loaded on a machine, when the machine becomes free, is normally made with the help of dispatching rules.
No dispatch rule is found to perform well for all important criteria, e.g. mean flowtime and not being on time. The choice of a dispatching rule depends on which criterion is intended to be improved upon. In general, it has been observed that process-time based rules fare better under tight load conditions, while due-date based rules perform better under light load conditions.
A job shop could be classified as an open shop or a closed shop, depending on the way jobs are routed in the shop. In a closed shop, the number of routings available to a job is fixed and an arriving job can follow one of the available routings. In an open shop, there is no limitation on the routing of a job and each job could have a different routing. Dispatching rules for open shops make use of process-time and work-content of jobs in the queue for next operation.
Here we consider distributed versions of a modified shifting bottleneck solution for complex job shop scenarios characterised by parallel batching machines, machines with sequence-dependent setup times and reentrant process flows.
The used performance measure is total weighted late arrival. We recommend a “Digital Twin Layer” approach to decompose the overall scheduling problem. The top layer works on an aggregated model. Based on appropriately aggregated routes it determines start dates and planned due dates for the jobs within each single work area, where a work area is defined as a set of parallel machine groups.
The base layer uses the start dates and planned due dates in order to apply shifting bottleneck type solution approaches for the jobs in each single work area. We conduct simulation experiments in a dynamic job shop scenario in order to assess the performance of the solution.
Results indicate the suggested approach outperforms a pure First In First Out Dispatching scheme and provides a similar solution quality as the original modified shifting bottleneck solution.
Better operational strategies are the main key in order to reduce costs and improve overall efficiency. New planning, scheduling and dispatching methods are required in order to reach the goal of better operational performance.
Improved tool capabilities have to be taken into account during the development of more complex rules. It is now possible to solve large scale scheduling problems via decomposition methods. The power of distributed computation can be applied to solve the resulting subproblems of the decomposition process in a simultaneous manner.
The shifting bottleneck solution may serve as the prominent example for job shop decomposition approaches. However, centralised implementations of the shifting bottleneck solution are still very time consuming from a runtime point of view even in the case of moderate scheduling horizons.
In the situation of a larger scheduling horizon, the number of nodes of the scheduling graph grows tremendously, so the solution of the scheduling problem requires large computational efforts in terms of memory and computation time. On the other hand, considering a small scheduling horizon leads to the problem of proper internal due date setting that is very often difficult.
Based on a proper physical decomposition of the manufacturing system into work area subsystems, we use a simple job planning approach in order to assign internal ready times and internal due dates to each job with respect to the decomposition of the job shop into work areas.
1. Make priority Job Site planning composed of facility layout/process outlines
2. Determine production along with build capacity based on facility layout
3. Utilise Work space to form plan for Job Site specification assign based on process plan
4. Generate work order description by input of “block” design metrics
5. Select production techniques process planning
6. Design product work sequence parameters
7. Estimate lead time of each production process by control of available resources/capacity.
8. Assign schedule plan of production strategy and materiel procurement of Job Site
9. Create short-term and mid-term schedule to consider available resources
10. Assess Production volume of each Product and estimated