scholarly journals A decision support system for cost determination in grain storage facility operations

2011 ◽  
Vol 31 (4) ◽  
pp. 735-744 ◽  
Author(s):  
Domingos S. M. Valente ◽  
Daniel M. de Queiroz ◽  
Paulo C. Corrêa ◽  
Luis C. da Silva ◽  
Sônia M. L. R. do Vale
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Turnover Rate ◽  
Grain Storage ◽  
Storage Facility ◽  
Factors Associated ◽  
Processing And Storage ◽  
Cost Determination ◽  
And Storage

Many research works have being carried out on analyzing grain storage facility costs; however a few of them had taken into account the analysis of factors associated to all pre-processing and storage steps. The objective of this work was to develop a decision support system for determining the grain storage facility costs and utilization fees in grain storage facilities. The data of a CONAB storage facility located in Ponta Grossa - PR, Brazil, was used as input of the system developed to analyze its specific characteristics, such as amount of product received and stored throughout the year, hourly capacity of drying, cleaning, and receiving, and dispatch. By applying the decision support system, it was observed that the reception and expedition costs were exponentially reduced as the turnover rate of the storage increased. The cleaning and drying costs increased linearly with grain initial moisture. The storage cost increased exponentially as the occupancy rate of the storage facility decreased.

Kontinuierliche Planung von Logistikflächen/Continuous planning of logistic areas within factories

2020 ◽  
Vol 110 (04) ◽  
pp. 195-200
Author(s):  
Michael Teucke ◽  
Marius Veigt ◽  
Hendrik Engbers ◽  
Malte Klose ◽  
Michael Freitag
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Continuous Monitoring ◽  
Software Tools ◽  
System P ◽  
And Storage

Da Logistikflächen für innerstädtische Fabriken nur begrenzt verfügbar sind, ist deren bestmögliche Nutzung bedeutsam. Der Einsatz von Softwarewerkzeugen ist in der Neuplanung von Logistikflächen gängige Praxis. Die effiziente Nutzung bestehender Flächen in Anbetracht geänderter Anforderungen wird aber selten kontinuierlich überprüft. Der Beitrag zeigt, wie eine kontinuierliche planerische Restrukturierung von Logistikflächen durch ein digitales Assistenzsystem unterstützt werden kann.   Due to limited available space in urban production plants, the best possible use of logistic and storage areas is very important. The use of software tools is common practice for the planning of new logistics areas. However, continuous monitoring of the efficient use of existing areas due to changing requirements is only rarely implemented. This article describes how a continuous restructuring planning of logistics areas can be supported by a decision support system.

Modeling Web-Based Data in a Data Warehouse

2011 ◽  
pp. 826-831
Author(s):  
Hadrian Peter ◽  
Charles Greenidge
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Data Warehouse ◽  
Data Structures ◽  
Support System ◽  
Database Design ◽  
Storage Facility ◽  
Web Based ◽  
Good Data

Good database design generates effective operational databases through which we can track customers, sales, inventories, and other variables of interest. The main reason for generating, storing, and managing good data is to enhance the decision-making process. The tool used during this process is the decision support system (DSS). The information requirements of the DSS have become so complex, that it is difficult for it to extract all the necessary information from the data structures typically found in operational databases. For this reason, a new storage facility called a data warehouse has been developed. Data in the data warehouse have characteristics that are quite distinct from those in the operational database (Rob & Coronel, 2002).

scholarly journals Experience with decision support system and comfort with topic predict clinicians’ responses to alerts and reminders

2015 ◽  
Vol 23 (e1) ◽  
pp. e125-e130 ◽  
Author(s):  
Nerissa S Bauer ◽  
Aaron E Carroll ◽  
Chandan Saha ◽  
Stephen M Downs
Keyword(s):  
Information Technology ◽  
Quality Improvement ◽  
Decision Support ◽  
Decision Support System ◽  
Health Information Technology ◽  
Health Information ◽  
Support System ◽  
Response Rate ◽  
Factors Associated ◽  
Prompt Response

Abstract Objective Clinicians at our institution typically respond to about half of the prompts they are given by the clinic’s computer decision support system (CDSS). We sought to examine factors associated with clinician response to CDSS prompts as part of a larger, ongoing quality improvement effort to optimize CDSS use. Methods We examined patient, prompt, and clinician characteristics associated with clinician response to decision support prompts from the Child Health Improvement through Computer Automation (CHICA) system. We asked pediatricians who were nonusers of CHICA to rate decision support topics as “easy” or “not easy” to discuss with patients and their guardians. We analyzed these ratings and data, from July 1, 2009 to January 29, 2013, utilizing a hierarchical regression model, to determine whether factors such as comfort with the prompt topic and the length of the user’s experience with CHICA contribute to user response rates. Results We examined 414 653 prompts from 22 260 patients. The length of time a clinician had been using CHICA was associated with an increase in their prompt response rate. Clinicians were more likely to respond to topics rated as “easy” to discuss. The position of the prompt on the page, clinician gender, and the patient’s age, race/ethnicity, and preferred language were also predictive of prompt response rate. Conclusion This study highlights several factors associated with clinician prompt response rates that could be generalized to other health information technology applications, including the clinician’s length of exposure to the CDSS, the prompt’s position on the page, and the clinician’s comfort with the prompt topic. Incorporating continuous quality improvement efforts when designing and implementing health information technology may ensure that its use is optimized.

scholarly journals Decision Support System in a Memristor-Based Mobile CIM Architecture Applied on Big Data Computation and Storage

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wei Li ◽  
Zhao Deng
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Computer Architecture ◽  
Support System ◽  
Integrated Circuit ◽  
Data Communication ◽  
Oxide Semiconductor ◽  
And Storage

Data computation and storage are essential parts of developing big data applications. The memristor device technology could remove the speed and energy efficiency bottleneck in the existing data processing. The present experimental work investigates the decision support system in a new architecture, computation-in-memory (CIM) architecture, which can be utilized to store and process big data in the same physical location at a faster rate. The decision support system is used for data computation and storage, with the aims of helping memory units read, write, and erase data and supporting their decisions under big data communication ambiguities. Data communication is realized within the crossbar by the support of peripheral controller blocks. The feasibility of the CIM architecture, adaptive read, write, and erase methods, and memory accuracy were investigated. The integrated circuit emphasis (SPICE) simulation results show that the proposed CIM architecture has the potential of improving the computing efficiency, energy consumption, and performance area by at least two orders of magnitude. CIM architecture may be used to mitigate big data processing limits caused by the conventional computer architecture and complementary metal-oxide-semiconductor (CMOS) transistor process technologies.

Decision Support System Development Based On Android Using Analytical Hierarchy Process (AHP) Method For Determining The Middle Small Business Development Priority In Sorong City

2019 ◽  
Vol 5 (2) ◽  
pp. 25-39
Author(s):  
Luluk Suryani ◽  
Raditya Faisal Waliulu ◽  
Ery Murniyasih
Keyword(s):  
Decision Support ◽  
Small Business ◽  
Decision Support System ◽  
Support System ◽  
Analytical Hierarchy Process ◽  
System Development ◽  
Business Development ◽  
Ahp Method ◽  
Small Business Development ◽  
Hierarchy Process

Usaha Kecil Menengah (UKM) adalah salah satu penggerak perekonomian suatu daerah, termasuk Kota Sorong. UKM di Kota Sorong belum berkembang secara optimal. Ada beberapa penyebab diantaranya adalah mengenai finansial, lokasi, bahan baku dan lain-lain. Untuk menyelesaikan permasalah tersebut peneliti terdorong untuk melakukan pengembangan Aplikasi yang dapat membantu menentukan prioritas UKM yang sesuai dengan kondisi pelaku usaha. Pada penelitian ini akan digunakan metode Analitycal Hierarchy Process (AHP), untuk pengambilan keputusannya. Metode AHP dipilih karena mampu menyeleksi dan menentukan alternatif terbaik dari sejumlah alternatif yang tersedia. Dalam hal ini alternatif yang dimaksudkan yaitu UKM terbaik yang dapat dipilih oleh pelaku usaha sesuai dengan kriteria yang telah ditentukan. Penelitian dilakukan dengan mencari nilai bobot untuk setiap atribut, kemudian dilakukan proses perankingan yang akan menentukan alternatif yang optimal, yaitu UKM. Aplikasi Sistem Pendukung Keputusan yang dikembangkan berbasis Android, dimana pengguna akan mudah menggunakannya sewaktu-waktu jika terjadi perubahan bobot pada kriteria atau intensitas.  Hasil akhir menunjukkan bahwa metode AHP berhasil diterapkan pada Aplikasi Penentuan Prioritas Pengembangan UKM.

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