Model-Driven Decision Support System for Broadband Penetration in Nigeria: Smart City Challenge

2021 ◽  
pp. 349-356
Author(s):  
Cosmas Ifeanyi Nwakanma ◽  
Achimba Chibueze Ogbonna ◽  
Udoka Felista Eze ◽  
Esther Chiadikaobi Ugwueke ◽  
Christiana Chidimma Nwauzor ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Smart City ◽  
Model Driven

Implementasi Metode Entropy dan Topsis Dalam Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik

2018 ◽  
Vol 10 (1) ◽  
pp. 13-18
Author(s):  
Mitha Anggreani Rupang ◽  
Adhi Kusnadi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Smart City ◽  
Entropy Method ◽  
Satisfaction Level ◽  
Topsis Method ◽  
Alphabetical Order ◽  
Index Terms ◽  
Growth Ability

Employee is a part of the company's most important asset in its efforts to maintain survival, growth, ability to compete and profit. At this time the process of assessment of employees in Jakarta Smart City is still in the form of manual and the decision only from one party only, so the process is still not accurate. So it takes the methods that must be able to replace that system. For that reason, a Decision Support System (SPK) was created to determine the best employees in Jakarta Smart City. In the system implemented the method of Entropy and TOPSIS. Entropy method can be trusted in determining the weight of the criteria to be used. And TOPSIS method can quickly perform the ranking process. Criteria to be used are quality and quantity of work, obedience, cooperation, morale, and work discipline. The index of satisfaction level of respondents to the decision support system ranges from 70% -80%, meaning that the assessment of the system created gives results at a fairly good level. Index Terms—employee, nter key words or phrases in alphabetical order, separated by commas

A Holistic, Interdisciplinary Decision Support System for Sustainable Smart City Design

2016 ◽  
pp. 1-10 ◽  
Author(s):  
Johannes M. Schleicher ◽  
Michael Vögler ◽  
Christian Inzinger ◽  
Sara Fritz ◽  
Manuel Ziegler ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Smart City ◽  
City Design

scholarly journals A MODEL-DRIVEN DECISION SUPPORT SYSTEM FOR REALLOCATION OF SUPPLY TO ORDERS UNDER UNCERTAINTY IN CERAMIC COMPANIES

2015 ◽  
Vol 21 (4) ◽  
pp. 596-625 ◽  
Author(s):  
M. M. E. ALEMANY ◽  
A. A. ◽  
Andrés BOZA ◽  
Vicente S. FUERTES-MIQUEL
Keyword(s):  
Decision Support ◽  
Mathematical Programming ◽  
Decision Support System ◽  
Decision Maker ◽  
Support System ◽  
Programming Model ◽  
Decision Makers ◽  
Mathematical Programming Model ◽  
Finished Good ◽  
Model Driven

In ceramic companies, uncertainty in the tone and gage obtained in first quality units of the same finished good (FG) entails frequent discrepancies between planned homogeneous quantities and real ones. This fact can lead to a shortage situation in which certain previously committed customer orders cannot be served because there are not enough homogeneous units of a specific FG (i.e., with the same tone and gage). In this paper, a Model-Driven Decision Support System (DSS) is proposed to reassign the actual homogeneous stock and the planned homogeneous sublots to already committed orders under uncertainty by means of a mathematical programming model (SP-Model). The DSS functionalities enable ceramic decision makers to generate different solutions by changing model options. Uncertainty in the planned homogeneous quantities, and any other type of uncertainty, is managed via scenarios. The robustness of each solution is tested in planned and real situations with another DSS functionality based on another mathematical programming model (ASP-Model). With these DSS features, the ceramic decision maker can choose in a friendly fashion the orders to be served with the current homogeneous stock and the future uncertainty homogeneous supply to better achieve a balance between the maximisation of multiple objectives and robustness.

scholarly journals Building Model-Driven Decision Support System in Product Redesign Plan

Designs ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 18
Author(s):  
Swee Kuik ◽  
Li Diong
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Environmental Implications ◽  
Model Driven ◽  
Building Model ◽  
Product Use ◽  
Manufacturing Assembly ◽  
Operational Processes ◽  
Future Work

Product recovery strategy requires a thoughtful consideration of environmental implications of operational processes, undergone by a manufactured product in its entire product lifecycle, from stages of material processing, manufacturing, assembly, transportation, product use, product post-use and end-of-life. At the returns stream from product use stage, those parts and/or component assemblies from a used product have several disposition alternatives for recovery, such as direct reuse, remanufacture, recycle or disposal. Due to such complexity of the manufacturing processes in recovery, current decision methodologies focus on the performance measures of cost, time, waste and quality separately. In this article, an integrated decision model for used product returns stream is developed to measure the recovery of utilisation value in the aspects of cost, waste, time, and quality collectively. In addition, we proposed a model-driven decision support system (DSS) that may be useful for manufacturers in making recovery disposition alternatives. A case application was demonstrated with the use of model-driven DSS to measure recovery utilisation value for the used product disposition alternatives. Finally, the future work and contributions of this study are discussed.

Specifications of a queuing model-driven decision support system for predicting the healthcare performance indicators pertaining to the patients flow

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Performance Indicators ◽  
Performance Indicator ◽  
Dynamic Change ◽  
Public Hospitals ◽  
Queuing Model ◽  
Model Driven ◽  
Effective System

This article has developed specifications for a new model-driven decision support system (DSS) that aids the key stakeholders of public hospitals in estimating and tracking a set of crucial performance indicators pertaining to the patients flow. The developed specifications have considered several requirements for ensuring an effective system, including tracking the performance indicator on the level of the entire patients flow system, paying attention to the dynamic change of the values of the indicator’s parameters, and considering the heterogeneity of the patients. According to these requirements, the major components of the proposed system, which include a comprehensive object-based queuing model and an object-oriented database, have been specified. In addition to these components, the system comprises the equations that produce the required predictions. From the system output perspective, these predictions act as a foundation for evaluating the performance indicators as well as developing policies for managing the patients flow in the public hospitals.

Sign in / Sign up

Export Citation Format

Share Document