scholarly journals The architecture of decision-making support systems used for the rational railway capacity management

2021 ◽  
Author(s):  
Ivica Ljubaj ◽  
Tomislav Josip Mlinarić ◽  
Slavko Vesković ◽  
Dušan Jeremić
Keyword(s):  
Decision Making ◽  
Support System ◽  
Traffic Control ◽  
Support Systems ◽  
Specific Model ◽  
Railway Transport ◽  
Railway Line ◽  
Railway Capacity ◽  
Double Track ◽  
Decision Making Support

Decision-making support systems in railway transport are systems that make it easier for traffic controllers and dispatchers involved in the regulation of train traffic to make individual decisions more easily and accurately. Without such systems, dispatchers usually make decisions based on previous experiences and feelings they have developed working in train traffic control. However, quality decision-making support systems are based on large amounts of data processed by one or several different artificial intelligence techniques. This paper will examine the architecture of such a system in railway transport, which helps the dispatcher to make decisions based on different criteria and values of individual criteria. The architecture of this decision-making support system has been developed to equal or, if necessary, use the maximum available double-track railway line capacity to resolve delays caused by lack of capacity for any given route. This system has been developed for the specific configuration of a double track, whereby each track is intended for one direction of train traffic. This paper will lay the foundation for understanding decision-making support systems and for the development of a specific model of decision-making support system in practice.

The Evaluation of Decision-Making Support Systems' Functionality

2011 ◽  
pp. 329-338 ◽  
Author(s):  
Giusseppi Forgionne ◽  
Stephen Russell
Keyword(s):  
Decision Making ◽  
Support System ◽  
Support Systems ◽  
Field Studies ◽  
Integrated System ◽  
Management Support ◽  
Individual System ◽  
Management Support Systems ◽  
Independent Effects ◽  
Decision Making Support

Contemporary decision-making support systems (DMSSs) are large systems that vary in nature, combining functionality from two or more classically defined support systems, often blurring the lines of their definitions. For example, in practical implementations, it is rare to find a decision support system (DSS) without executive information system (EIS) capabilities or an expert system (ES) without a recommender system capability. Decision-making support system has become an umbrella term spanning a broad range of systems and functional support capabilities (Alter, 2004). Various information systems have been proposed to support the decision-making process. Among others, there are DSSs, ESs, and management support systems (MSSs). Studies have been conducted to evaluate the decision effectiveness of each proposed system (Brown, 2005; Jean-Charles & Frédéric, 2003; Kanungo, Sharma, & Jain, 2001; Rajiv & Sarv, 2004). Case studies, field studies, and laboratory experiments have been the evaluation vehicles of choice (Fjermestad & Hiltz, 2001; James, Ramakrishnan, & Kustim, 2002; Kaplan, 2000). While for the most part each study has examined the decision effectiveness of an individual system, it has done so by examining the system as a whole using outcome- or user-related measures to quantify success and effectiveness (Etezadi-Amoli & Farhoomand, 1996; Holsapple & Sena, 2005; Jain, Ramamurthy, & Sundaram, 2006). When a study has included two or more systems, individual system effects typically have not been isolated. For example, Nemati, Steiger, Lyer, and Herschel (2002) presented an integrated system with both DSS and AI (artificial intelligence) functionality, but they did not explicitly test for the independent effects of the DSS and AI capabilities on the decision-making outcome and process. This article extends the previous work by examining the separate impacts of different DMSSs on decision effectiveness.

The Evaluation of Decision-Making Support Systems' Functionality

2011 ◽  
pp. 149-160
Author(s):  
Giusseppi Forgionne ◽  
Stephen Russell
Keyword(s):  
Decision Making ◽  
Support System ◽  
Support Systems ◽  
Field Studies ◽  
Integrated System ◽  
Management Support ◽  
Individual System ◽  
Management Support Systems ◽  
Independent Effects ◽  
Decision Making Support

Contemporary decision-making support systems (DMSSs) are large systems that vary in nature, combining functionality from two or more classically defined support systems, often blurring the lines of their definitions. For example, in practical implementations, it is rare to find a decision support system (DSS) without executive information system (EIS) capabilities or an expert system (ES) without a recommender system capability. Decision-making support system has become an umbrella term spanning a broad range of systems and functional support capabilities (Alter, 2004). Various information systems have been proposed to support the decision-making process. Among others, there are DSSs, ESs, and management support systems (MSSs). Studies have been conducted to evaluate the decision effectiveness of each proposed system (Brown, 2005; Jean-Charles & Frédéric, 2003; Kanungo, Sharma, & Jain, 2001; Rajiv & Sarv, 2004). Case studies, field studies, and laboratory experiments have been the evaluation vehicles of choice (Fjermestad & Hiltz, 2001; James, Ramakrishnan, & Kustim, 2002; Kaplan, 2000). While for the most part each study has examined the decision effectiveness of an individual system, it has done so by examining the system as a whole using outcome- or user-related measures to quantify success and effectiveness (Etezadi-Amoli & Farhoomand, 1996; Holsapple & Sena, 2005; Jain, Ramamurthy, & Sundaram, 2006). When a study has included two or more systems, individual system effects typically have not been isolated. For example, Nemati, Steiger, Lyer, and Herschel (2002) presented an integrated system with both DSS and AI (artificial intelligence) functionality, but they did not explicitly test for the independent effects of the DSS and AI capabilities on the decision-making outcome and process. This article extends the previous work by examining the separate impacts of different DMSSs on decision effectiveness.

Decision-Making Support Systems and Representation Levels

2005 ◽  
pp. 766-771
Author(s):  
Jean-Charles Pomerol ◽  
Frédéric Adam
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Support Systems ◽  
Decision Making Processes ◽  
Decision Making Support

The concept of Decision Support System (DSS), which was first coined by Gorry and Scott Morton (1971), was proposed in an attempt to focus the attention of IS researchers and practitioners more closely on the decision-making processes of managers. It sought to acknowledge the importance of decision-making as the key activity that managers must perform in organizations (Huber, 1982).

IMPLEMENTASI METODE VIKOR DALAM PENERIMAAN JURNALIS PADA KORAN WASPADA MEDAN

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Soraya Rahma Hayati ◽  
Mesran Mesran ◽  
Taronisokhi Zebua ◽  
Heri Nurdiyanto ◽  
Khasanah Khasanah
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Support Systems ◽  
Knowledge Based ◽  
Computer Based ◽  
Support Decision Making ◽  
Made In

The reception of journalists at the Waspada Daily Medan always went through several rigorous selections before being determined to be accepted as journalists at the Waspada Medan Daily. There are several criteria that must be possessed by each participant as a condition for becoming a journalist in the Daily Alert Medan. To get the best participants, the Waspada Medan Daily needed a decision support system. Decision Support Systems (SPK) are part of computer-based information systems (including knowledge-based systems (knowledge management)) that are used to support decision making within an organization or company. Decision support systems provide a semitructured decision, where no one knows exactly how the decision should be made. In this study the authors applied the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) as the method to be applied in the decision support system application. The VIKOR method is part of the Multi-Attibut Decision Making (MADM) Concept, which requires normalization in its calculations. The expected results in this study can obtain maximum decisions.Keywords: Journalist Acceptance, Decision Support System, VIKOR

Intelligent Technologies in Decision-Making Support Systems

2020 ◽  
Author(s):  
F. F. Pashchenko ◽  
Bui Truong An ◽  
Tran Duc Hieu ◽  
A. F. Pashchenko ◽  
Nguyen Van Trong
Keyword(s):  
Decision Making ◽  
Support Systems ◽  
Decision Making Support

scholarly journals Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources

2021 ◽  
Vol 11 (13) ◽  
pp. 6237
Author(s):  
Azharul Islam ◽  
KyungHi Chang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Random Forest ◽  
Support System ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Unstructured Data ◽  
Stochastic Gradient Descent ◽  
Decision Making Support

Unstructured data from the internet constitute large sources of information, which need to be formatted in a user-friendly way. This research develops a model that classifies unstructured data from data mining into labeled data, and builds an informational and decision-making support system (DMSS). We often have assortments of information collected by mining data from various sources, where the key challenge is to extract valuable information. We observe substantial classification accuracy enhancement for our datasets with both machine learning and deep learning algorithms. The highest classification accuracy (99% in training, 96% in testing) was achieved from a Covid corpus which is processed by using a long short-term memory (LSTM). Furthermore, we conducted tests on large datasets relevant to the Disaster corpus, with an LSTM classification accuracy of 98%. In addition, random forest (RF), a machine learning algorithm, provides a reasonable 84% accuracy. This research’s main objective is to increase the application’s robustness by integrating intelligence into the developed DMSS, which provides insight into the user’s intent, despite dealing with a noisy dataset. Our designed model selects the random forest and stochastic gradient descent (SGD) algorithms’ F1 score, where the RF method outperforms by improving accuracy by 2% (to 83% from 81%) compared with a conventional method.

The role of decision-making support systems in IT service management processes

2014 ◽  
Vol 8 (2) ◽  
pp. 147-163 ◽  
Author(s):  
M. Mora ◽  
Gloria Phillips-Wren ◽  
Jorge Marx-Gomez ◽  
F. Wang ◽  
O. Gelman
Keyword(s):  
Decision Making ◽  
Service Management ◽  
Support Systems ◽  
It Service Management ◽  
It Service ◽  
Management Processes ◽  
Decision Making Support
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