Optimization in Military Planning

2019 ◽

pp. 111-130

Author(s):

Mehmet Gokhan Metin ◽

Serol Bulkan

Keyword(s):

Resource Allocation ◽

Theoretical Background ◽

Background Information ◽

Resource Allocation Problem ◽

Time Data ◽

Rapid Improvement ◽

Optimal Sequence ◽

Wide Range ◽

Military Planning ◽

Real Time Data

In recent years, advancing technology has introduced a wide range of resources for military forces. This rapid improvement in the number and types of military resources also led to difficulties in the management of resource allocation issues in combat missions. The general resource allocation problem can be defined as determining the optimal sequence of resource usage allocations while maximizing the kills on the enemy's forces or minimizing casualties. In this chapter, the authors investigate the resource allocation problems and solution approaches observed in planning military missions. Theoretical background information and some examples based on real-time data are provided to illustrate the chosen problem types.

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CAI method in Cloud services for user workload using Cloudsim framework

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9991.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 12622-12626

Keyword(s):

Resource Allocation ◽

Data Center ◽

Cloud Service ◽

Cloud Services ◽

Time Data ◽

Real Time Data ◽

The Impact ◽

System Designs ◽

New System

In sighting the distinct patterns of processing capability in a cloud service is pedantic to enhance the resource management and operable conditions of the servers without compromising the Quality of Service is important. Simulations and models based on practicable parameters are required to understand the impact of the load on new system designs and policies. The proposed scheme and analysis provides a requirement for designing new systems which will be lessaffected by process loads. Classifying, analysis and improving (CAI) is done using real-time data center logs and simulations are done based on user requests and data center configurations. Simulations are created using cloudsim framework. Various simulations are done to provide a comprehensive result to improve the resource allocation for the system.

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ADAPTIVE HARMONY SEARCH FOR OPTIMIZING CONSTRAINED RESOURCE ALLOCATION PROBLEM

International Journal of Computing ◽

10.47839/ijc.17.4.1148 ◽

2018 ◽

pp. 260-269

Author(s):

Amol C. Adamuthe ◽

Tushar R. Nitave

Keyword(s):

Resource Allocation ◽

Optimization Problems ◽

Harmony Search ◽

Population Diversity ◽

Allocation Problem ◽

Resource Allocation Problem ◽

Multi Objective ◽

Wide Range ◽

Allocation Problems ◽

Population Based Algorithm

Resource Allocation problem is finding the optimal assignment of finite available resources to tasks or users. Resource allocation problems refer to a wide range of applications such as production, supply chain management, transportation, ICT technologies, etc. Resource allocation problems are NP-hard in nature where the objective is to find the optimal allocations satisfying given constraints. Harmony search (HS) algorithm is a meta-heuristic population based algorithm found good for solving different optimization problems. This paper presents adaptive harmony search (AHS) for solving one-dimensional bin packing problem (BPP) and multi-objective virtual machine placement problem (VMP). The proposed real coded solution representation supports partial constraint satisfaction. Adaptive pitch adjustment rate (PAR) based on population diversity improves the performance of harmony search algorithm. Results show that proposed HS gives optimal solution for 50 BPP instances with 100 % success rate. The performance reduced for large instances of BPP. The proposed weighted AHS for multi objective VMP problem gives better results than genetic algorithm.

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Implementation of a Digital Roadmap with Best-in-Class Applications

10.2118/205418-ms ◽

2021 ◽

Author(s):

John McIntosh ◽

Renata Martin ◽

Pedro Alcala ◽

Stian Skjævesland ◽

John Rigg

Keyword(s):

Real Time ◽

Case Studies ◽

Performance Indicators ◽

Key Performance Indicators ◽

Third Party ◽

Commercial Application ◽

Time Data ◽

Operating Model ◽

Wide Range ◽

Real Time Data

Abstract The paper describes a project known internally as "InWell" to address multiple requirements in Repsol Drilling & Completions. InWell is defined by a new Operating Model comprising Governance, People, Process, Functions and Technology. This paper addresses changes to the Technology element - often referred to as "Digitalization". The paper includes a discussion about the business transformation strategy and case studies for addressing three of 18 functionalities identified in the first round of development. The InWell development strategy followed four steps; identification of performance issues, envisioning of a future operating model, identification of functionalities required/supporting this operating model and matching to digital solutions. Our case studies focus on three functionalities provided by three separate companies, Unification of Planning and Compliance, Real Time Data aggregation and Key Performance Indicators. Each functionality was addressed with an existing commercial application customized to meet specific requirements. A corporate web-based Well Construction Process (WCP) was initially piloted and then extended to include all well projects. The WCP identifies the key Tasks that must be completed per project, and these are all tracked. Data from this application is used by a third-party Business Analytics application via an API. Real time data from many sites and a wide range of sources was aggregated and standardized, Quality Controlled and stored within a private secure cloud. The data collation service is an essential building block for current third-party applications such as the operating centre and is a prerequisite for the goal of increased automation. A suite of Operator specific Key Performance Indicators (KPIs) and data analytics services were developed for drilling and completions. Homogenized KPIs for all business units provide data for objective performance management and apples-to-apples comparison. Results are presented via custom dashboards, reports, and integrations with third party applications to meet a wide range of requirements. During a four-month Pilot Phase the InWell Project delivered € 2.5 million in tangible savings through improvements in operational performance. In the first 12 months € 16 million in savings were attributed to InWell. By 2022 forecast savings are expected to exceed € 60 million (Figures 1 & 2). The value of Intangible benefits is thought to exceed these objective savings. Figure 1 The Business Case for InWell – Actual & Projected Savings and Costs. Figure 2 InWell Services addressing Value Levers and quantified potential impact. A multi-sourced digital strategy can produce quick gains, is easily adapted, and provides high value at low risk. The full benefit of digital transformation can only be realised when supported by an effective business operating model.

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Planning for LOTOS: A New LOwer Troposphere Observing System

10.5194/egusphere-egu2020-19600 ◽

2020 ◽

Author(s):

Terry Hock ◽

Tammy Weckwerth ◽

Steve Oncley ◽

William Brown ◽

Vanda Grubišić ◽

...

Keyword(s):

Boundary Layer ◽

Real Time ◽

Sensor Network ◽

Regional Scale ◽

Lower Troposphere ◽

Integrated System ◽

Lower Atmosphere ◽

Time Data ◽

Wide Range ◽

Real Time Data

The National Center for Atmospheric Research Earth Observing Laboratory (EOL) proposes to develop the LOwer Troposphere Observing System (LOTOS), a new integrated sensor network that offers the potential for transformative understanding of the lower atmosphere and its coupling to the Earth's surface.&#160;&#160;The LOTOS sensor network is designed to allow simultaneous and coordinated sampling both vertically, through the atmospheric planetary boundary layer, and horizontally, across the surrounding landscape, focusing on the land-atmosphere interface and its coupling with the overlying free troposphere. The core of LOTOS will be a portable integrated network of up to five nodes, each consisting of a profiling suite of instruments surrounded by up to fifteen flux measuring towers. LOTOS will provide an integrated set of measurements needed to address outstanding scientific challenges related to processes within the atmospheric surface layer, boundary layer, and lower troposphere. LOTOS will also enable novel quantification of exchanges of biogeochemical and climate-relevant gases from microscale up to regional scale.&#160;&#160;LOTOS&#8217; uniqueness lies in its ability to simultaneously sample both horizontally and vertically as an integrated system, but also in its flexibility to be easily relocated as a portable field-deployable system suitable for addressing a wide range of research needs. LOTOS will provide real-time data quality control, combine measurements from a variety of sensors into integrated data products, and provide real-time data displays. It is envisioned that LOTOS will become part of the deployable NSF Lower Atmosphere Observing Facilities (LAOF) and thus be available to a broad base of NSF users from both observational and modeling communities. LOTOS offers the potential for transformative understanding of the Earth and its atmosphere as a coupled system. This presentation will describe the background, motivation, plan, and timeline for the LOTOS&#8217; proposed development.

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