Decision Support System On Computer Maintenance Management SystemUsing Association Rule and Fisher Exact Test One Side P-Value

PurposeThis study aims to propose a decision support system (DSS) for maintenance management of a service system, namely, a street cleaning service vehicle. Referring to the information flow management, the blockchain technology is integrated in the proposed DSS to assure data transparency and security.Design/methodology/approachThe DSS is designed to efficiently handle the data acquired by the network of sensors installed on selected system components and to support the maintenance management. The DSS supports the decision makers to select a subset of indicators (KPIs) by means of the DEcision-MAaking Trial and Evaluation Laboratory method and to monitor the efficiency of performed preventive maintenance actions by using the mathematical model.FindingsThe proposed maintenance model allows real-time decisions on interventions on each component based on the number of alerts given by sensors and taking into account the annual cost budget constraint.Research limitations/implicationsThe present paper aims to highlight the implications of the blockchain technology in the maintenance field, in particular to manage maintenance actions’ data related to service systems.Practical implicationsThe proposed approach represents a support in planning, executing and monitoring interventions by assuring the security of the managed data through a blockchain database. The implications regard the monitoring of the efficiency of preventive maintenance actions on the analysed components.Originality/valueA combined approach based on a multi-criteria decision method and a novel mathematical programming model is herein proposed to provide a DSS supporting the management of predictive maintenance policy.

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Hierarchical Clustering and Association Rule Discovery Process for Efficient Decision Support System

Communication and Networking - Communications in Computer and Information Science ◽

10.1007/978-3-642-27201-1_27 ◽

2011 ◽

pp. 239-247

Author(s):

Bobby D. Gerardo ◽

Yung-Cheol Byun ◽

Bartolome Tanguilig

Keyword(s):

Decision Support ◽

Decision Support System ◽

Hierarchical Clustering ◽

Support System ◽

Association Rule ◽

Rule Discovery ◽

Discovery Process ◽

Association Rule Discovery

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Maintenance management with application of computational intelligence generating a decision support system for the load dispatch in power plants

Decision Making Applications in Modern Power Systems ◽

10.1016/b978-0-12-816445-7.00013-x ◽

2020 ◽

pp. 337-364

Author(s):

Milton Fonseca Júnior ◽

Jandecy Cabral Leite ◽

Tirso Lorenzo Reyes Carvajal ◽

Manoel Henrique Reis Nascimento ◽

Jorge de Almeida Brito Júnior ◽

...

Keyword(s):

Decision Support ◽

Decision Support System ◽

Support System ◽

Computational Intelligence ◽

Power Plants ◽

Maintenance Management

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Design of the Sports Training Decision Support System Based on Improved Association Rule, the Apriori Algorithm

Intelligent Automation & Soft Computing ◽

10.32604/iasc.2020.010110 ◽

2020 ◽

Vol 26 (4) ◽

pp. 755-763

Author(s):

Xinbao Wang ◽

Dawu Huang ◽

Xuemin Zhao

Keyword(s):

Decision Support ◽

Decision Support System ◽

Support System ◽

Association Rule ◽

Apriori Algorithm ◽

Sports Training

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Pengembangan Sistem Pendukung Keputusan Untuk Mendukung Penegakkan Diagnosa TB Dots Di Rumah Sakit Aisyiyah Muntilan

Jurnal Manajemen Kesehatan Indonesia ◽

10.14710/jmki.4.2.2016.123-128 ◽

2016 ◽

Vol 4 (2) ◽

pp. 123-128

Author(s):

Susanti Susanti ◽

Mateus Sakundarno Adi ◽

Atik Mawarni

Keyword(s):

Information System ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

T Test ◽

P Value ◽

Research Subjects ◽

Quality Of Information ◽

Before And After

Tuberculosis (TB) disease intervention is a national program and being a target of MDGs. Therefore, a government determined minimum service standards of a hospital that had to be implemented in all health service units and hospitals in Indonesia. A strategy of DOTS at Aisyiyah Hospital in Muntilan had been available particularly in terms of case finding. Notwithstanding, a process of patient diagnosis had not been implemented in accordance with a standard of human resource. Number of medical officers at a TB DOTS unit was not sufficient. In addition, quality of information like completeness, accurateness in diagnosis, and timeliness in reporting TB cases had not achieved a target. The aim of this study was to develop decision support system of TB as an effort to systematically diagnose and manage data of TB based on the standard. This was qualitative-quantitative research. A qualitative method was used to identify each step of information system development. Meanwhile, a quantitative method was used to assess quality of information between before and after developing information system using pre-experimental design (the one group pre and posttest design). Qualitative data were collected by conducting indepth interview, whereas quantitative data were collected using checklist. Data were analysed using T test. Research object was decision support system before and after applying at TB unit at Aisyiyah Hospital in Muntilan. Research subjects consisted of four TB officers as main informants. Informants for triangulation purpose consisted of head of SIMRS and head of medical services. The results of T test showed that variables of completeness, accurateness, and preciseness after developing the decision support system were better than that of before developing the system (p-value = 0.000). As suggestions, head of SIMRS needs to conduct further development of decision support system of TB in another unit particularly in inpatient unit. The decision support system needs to be evaluated and adjusted with the standard.

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