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SIMLOX DATASHEET

Introduction

SIMLOX is a next discreet event simulation model developed as an extension to OPUS10. It is important that analysis of the time dimension is available including the stochastic elements as there is no such thing as an average operation.  Although a separate product to OPUS10, SIMLOX will interface/integrate with OPUS10. SIMLOX is fully OPUS10v5 compatible and will be developed to maintain this compatibility. 

The next discreet event simulation uses a Monte-Carlo technique that enables many replications per ‘run’ of the model. It simulates the operation and maintenance logistics, and their interactions, for any technical system enabling the capability assessment of that system. 

SIMLOX supplements the OPUS10 model and uses the same data and definitions with a simple and easy to use data import utility. SIMLOX analyses the OPUS10 scenario with major extension to cover:

  • Different mission profiles with variable utilisation

  • Mission prioritisation

  • Mission controlled generation of failures/damages

  • Maintenance resource and constraints

  • Preventative maintenance

  • Mission damage

  • Cannibalisation/robbing

  • Non critical and critical failures

  • Deferred maintenance and repair

  • Lateral or flexible support

  • System variants

  • Batched transport

  • Transfer of systems, items and resources 

Use of SIMLOX and OPUS10 

It is normal to use OPUS10 first to: 

  • Compare different alternatives

  • Generate optimum spares allocations/assortments

  • Generate optimal repair location

  • Select cost-effective solution

Then to use SIMLOX to: 

  • Apply detailed mission data and study the impact of a dynamic mission profile

  • Apply data for the model extensions such as robbing and study the effects on system availability

  • Run selected stock allocation point

  • Analyse the impact of the time dimension

  • Compare system Measure of Effectiveness (MoE) with requirements

This approach enables the advantages of both models to be felt. 

The OPUS10 advantages are: 

  • OPUS10 optimises, SIMLOX analyses

  • A complete C/E curve

  • Life support cost (LSC) calculations

  • Can compare different strategies in a simple cost-effective way

  • Very fast

  • Mature software

The SIMLOX advantages are:

  • More flexible than OPUS10

  • Detailed mission profiles

    • Flexible and time dependent

      • Explicit missions that are performed at certain points in time

      • Variable utilisation profile

      • Cancel missions in minimum assets not available

    • Many mission types

  • Time dependencies/aspects can be modelled

  • More accurate system MoE

  • Ability to track resources and spares through the simulation

  • Percentiles on results

  • Mean values and standard deviations can be presented

The SIMLOX results are similar to OPUS10 but there are a number of differences:

  • Lower values due to aspects such as maintenance resource availability, batched transports, damages and utilisation varying over time

  • Higher values due to aspect such as cannibalisation, robbing, loans between bases and repair prioritisation

  •  Non contiguous demand generation

  • Failures/damages only effects systems when operating

  • Ability to limit the number of failed items per system

  • Non steady state

  • Parallel repair

  • Delayed sub item demand 

SIMLOX is thus a tool that can enable capability assessment and to analyse if requirements cam be met or to formalise those requirements, for instance: 

  • The fraction of successful mission must exceed X%

  • The number of aircraft on ground must not exceed Y in any 24 hour period with a confidence of 90% 

Scope of the simulation

System 

At the core of the simulation and subsequent analysis is the ‘macro system’ under scrutiny. In practice, this comprises many self-sufficient systems (such as vehicles) operating with semi autonomous constraints. These systems are described be reference to their hierarchical structure and constituents. Failures within SIMLOX contribute to events in both the operational and support network insofar as both critically and the associated support resources are modelled. 

Scenario 

The overall operational profile in SIMLOX is modelled by describing the Operational Support Organisation in the same terms successfully used by the OPUS10 model. SIMLOX models replacement tasks, repair tasks and the transportation of stock as separate activities each dependent on the availability and capability of key resources. Prior to each run, users may tailor the details modelled. For instance, precise stock allocations may be included and resources such as manpower, repair facilities, and specialised support equipment accounted for. Limited resources, which restrict the repair of specific failures, may also be targeted and monitored to determine whether they impact on system availability. Finally batch transportation, ‘robbing’, lateral resupply, scheduled maintenance and operationally induced damage may also be modelled.

Operational Simulation 

A model run is performed against a profile, normally requiring a number of tasks (referred to as missions) to be performed by systems of the same or different types. Systems belong to units and several of these units based at a single operating site, of which may be modelled simultaneously. Units and systems are allocated a number of tasks to fulfil within the profile given imposed constraints (i.e. number of systems per task, task earliest/latest start times). Missions cannot start until all critical systems are serviceable and a mission success is dependent on the system reaching a predetermined point without incurring critical failure. 

The system states within the simulation are preparation, ready, ready prepared, on mission, turnround and maintenance. 

The simulation includes detailed mission definitions including: 

  • A mission can comprise several systems (vehicles/prime equipments)

  • Minimum launch quantity

  • Minimum delivery quantity

  • Structured system failures initiate logistics activities

  • Mission critical/non critical items

  • Missions comprise a variety of ‘sortie types’

  • Adaptable success points

  • Time on station facility

  • Battle and maintenance induced failures

In the simulation a mission is defined as:

  • An event that is scheduled to occur at a particular time and last for a defined period

  • Ability to comprise a number of sorties i.e. several systems are required to undertake the same tasks at the same time

  • In certain cases be permitted to proceed with less than the number os systems

  • In certain cases be delayed by a specified time, called deferment time to allow systems that are still being repaired to become available

A mission can be split into 3 phases; outbound, on task and return. The outbound/return times can zero or the on task time can be zero but not both. 

The simulation also has the capability for the modelling of regenerative missions. This capability will: 

  • Automatically programs missions to maintain on  task time
  • Ensures that new missions arrive on task when existing mission is about to return

This has the advantage of limiting gaps in on task time when system failure is critical.

Outputs

The output data files enable a wide range of Effectiveness Measures to be extracted. These include, but are not limited to, Operational Availability, Task Generation Rate, Critical Resource Shortages, Systems Redundancy and Probability of Operational Success. Multiple replications are performed to determine the cost-effectiveness of the support organisation in sustaining the operation. The model provides additional results including summary statistics giving confidence limits on output results. The user may select to automatically perform as many replications as required to achieve satisfactory target confidence levels. 

 

 

Technical information

  • Commercially available off the shelf  

  • Microsoft Windows compatibility  

  • Simulation is written in C++ and VB for the GUI  

  • ODBC interfaces

  • Results object based with commonality with OPUS10

  • Graphical results capability

  • Commonality of form based data input with OPUS10

  • Extensive data checking

  • Reuse of OPUS10 data

  • Use of  OPUS10 results – allocation table

  • Comprehensive help facility

  • Comprehensive documentation

  • Fully supported 

  •  Long term development program in consultation with our customers

The flow of data and results through the models can be summarized as:

  • OPUS10 input and output data are imported into the SIMLOX input database

  • SIMLOX unique data such as mission related information is added either via the editor or imported from an external database

  • Data is then checked for validity and consistency

  • Simulation runs

  • Output to a results database for viewing via the editor or graphics utility

  • Output to a graphical replay database for running a replay program