scholarly journals Assessing uncertainty and risk in an expeditionary military logistics network

2021 ◽  
Author(s):  
Brandon M McConnell ◽  
Thom J Hodgson ◽  
Michael G Kay ◽  
Russell E King ◽  
Yunan Liu ◽  
...  
Keyword(s):  
Decision Support ◽  
Enterprise Resource Planning ◽  
Sample Path ◽  
Resource Planning ◽  
Analytical Models ◽  
Planning System ◽  
Decision Support Tools ◽  
Military Logistics ◽  
Logistics Network ◽  
Support Tools

Uncertainty is rampant in military expeditionary operations spanning high-intensity combat to humanitarian operations. These missions require rapid planning and decision-support tools to address the logistical challenges involved in providing support in often austere environments. The US Army’s adoption of an enterprise resource planning system provides an opportunity to develop automated decision-support tools and other analytical models designed to take advantage of newly available logistical data. This research presents a tool that runs in near-real time to assess risk while conducting capacity planning and performance analysis designed for inclusion in a suite of applications dubbed the Military Logistics Network Planning System, which previously only evaluated the mean sample path. Logistical data from combat operations during Operation Iraqi Freedom drive supply requisition forecasts for a contingency scenario in a similar geographic environment. A nonstationary queueing network model is linked with a heuristic logistics scheduling methodology to provide a stochastic framework to account for uncertainty and assess risk.

scholarly journals Assessing uncertainty and risk in an expeditionary military logistics network

2019 ◽  
pp. 154851291986059
Author(s):  
Brandon M McConnell ◽  
Thom J Hodgson ◽  
Michael G Kay ◽  
Russell E King ◽  
Yunan Liu ◽  
...  
Keyword(s):  
Decision Support ◽  
Enterprise Resource Planning ◽  
Sample Path ◽  
Resource Planning ◽  
Analytical Models ◽  
Planning System ◽  
Decision Support Tools ◽  
Military Logistics ◽  
Logistics Network ◽  
Support Tools

Uncertainty is rampant in military expeditionary operations spanning high-intensity combat to humanitarian operations. These missions require rapid planning and decision-support tools to address the logistical challenges involved in providing support in often austere environments. The US Army’s adoption of an enterprise resource planning system provides an opportunity to develop automated decision-support tools and other analytical models designed to take advantage of newly available logistical data. This research presents a tool that runs in near-real time to assess risk while conducting capacity planning and performance analysis designed for inclusion in a suite of applications dubbed the Military Logistics Network Planning System, which previously only evaluated the mean sample path. Logistical data from combat operations during Operation Iraqi Freedom drive supply requisition forecasts for a contingency scenario in a similar geographic environment. A nonstationary queueing network model is linked with a heuristic logistics scheduling methodology to provide a stochastic framework to account for uncertainty and assess risk.

scholarly journals A Military Logistics Network Planning System

2021 ◽  
Author(s):  
Matthew B Rogers ◽  
Brandon M McConnell ◽  
Thom J Hodgson ◽  
Michael G Kay ◽  
Russell E King ◽  
...  
Keyword(s):  
Enterprise Resource Planning ◽  
Discrete Event ◽  
Network Planning ◽  
Resource Planning ◽  
Planning System ◽  
Military Logistics ◽  
Logistics Network ◽  
Erp System ◽  
Event Simulation ◽  
Operational Impacts

This paper presents a proof of concept for a Military Logistics Network Planning System (MLNPS) to be used during mission planning to quickly identify a robust logistical footprint that can adequately sustain units deployed in an expeditionary environment. The logistical network is modeled using an efficient form of goal-seeking deterministic discrete event simulation to process supply requisitions through the logistical network. The queuing information obtained from the simulation informs capacity adjustments to the network to maximize efficiency. This process of simulation and network tuning continues interactively until an adequate and robust logistical footprint is found. During the planning stages, the MLNPS can be used to identify and mitigate logistical problems instead of waiting to react to backlogs when the military's operations would have already been affected. Designed to run as an app on the Army's enterprise resource planning (ERP) system (Global Combat Support System-Army), the MLNPS can also be used during operations to inform commanders of expected operational impacts on logistics. Contingency operation scenarios are used to demonstrate the MLNPS' capabilities.

Decision Support Tools For Assessing Petroleum Upstream Investments

2020 ◽  
pp. 323
Author(s):  
Nour Elislam Djedaa ◽  
Abderrezak Moulay Lakhdar
Keyword(s):  
Decision Support ◽  
Decision Support Tools ◽  
Support Tools

Elicitation of expert knowledge to develop decision support tools for use in water demand management planning

2007 ◽  
Vol 7 (5-6) ◽  
pp. 53-60
Author(s):  
D. Inman ◽  
D. Simidchiev ◽  
P. Jeffrey
Keyword(s):  
Decision Support ◽  
Water Demand ◽  
Expert Knowledge ◽  
Demand Management ◽  
Decision Processes ◽  
Conservation Strategy ◽  
Influence Diagrams ◽  
Decision Support Tools ◽  
Water Demand Management ◽  
Support Tools

This paper examines the use of influence diagrams (IDs) in water demand management (WDM) strategy planning with the specific objective of exploring how IDs can be used in developing computer-based decision support tools (DSTs) to complement and support existing WDM decision processes. We report the results of an expert consultation carried out in collaboration with water industry specialists in Sofia, Bulgaria. The elicited information is presented as influence diagrams and the discussion looks at their usefulness in WDM strategy design and the specification of suitable modelling techniques. The paper concludes that IDs themselves are useful in developing model structures for use in evidence-based reasoning models such as Bayesian Networks, and this is in keeping with the objectives set out in the introduction of integrating DSTs into existing decision processes. The paper will be of interest to modellers, decision-makers and scientists involved in designing tools to support resource conservation strategy implementation.

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