Pub Date : 2021-01-01DOI: 10.4018/ijdsst.2021010103
Francis J. Baumont De Oliveira, S. Ferson, Ronald Dyer
The emerging industry of vertical farming (VF) faces three key challenges: standardisation, environmental sustainability, and profitability. High failure rates are costly and can stem from premature business decisions about location choice, pricing strategy, system design, and other critical issues. Improving knowledge transfer and developing adaptable economic analysis for VF is necessary for profitable business models to satisfy investors and policy makers. A review of current horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. Data from the literature alongside lessons learned from industry practitioners are centralised in the proposed DSS, using imprecise data techniques to accommodate for partial information. The DSS evaluates business sustainability using financial risk assessment. This is necessary for complex/new sectors such as VF with scarce data.
{"title":"A Collaborative Decision Support System Framework for Vertical Farming Business Developments","authors":"Francis J. Baumont De Oliveira, S. Ferson, Ronald Dyer","doi":"10.4018/ijdsst.2021010103","DOIUrl":"https://doi.org/10.4018/ijdsst.2021010103","url":null,"abstract":"The emerging industry of vertical farming (VF) faces three key challenges: standardisation, environmental sustainability, and profitability. High failure rates are costly and can stem from premature business decisions about location choice, pricing strategy, system design, and other critical issues. Improving knowledge transfer and developing adaptable economic analysis for VF is necessary for profitable business models to satisfy investors and policy makers. A review of current horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. Data from the literature alongside lessons learned from industry practitioners are centralised in the proposed DSS, using imprecise data techniques to accommodate for partial information. The DSS evaluates business sustainability using financial risk assessment. This is necessary for complex/new sectors such as VF with scarce data.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84999148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.4018/ijdsst.2021010102
G. Fenu, Francesca Maridina Malloci
Decision support systems (DSSs) are used in precision farming to address climate and environmental changes due to human action. However, increments in the amount of data produced continuously by the latest sensor and satellite technologies have recently incentivized the integration of artificial intelligence (AI). A review of research dedicated to the application of DSSs and AI in forecasting crop disease is proposed. In this paper, the authors describe the DSS LANDS developed for monitoring the main crop productions in Sardinia and the case study conducted to forecast potato late blight. A feed-forward neural network was implemented to investigate if weather data provided by regional stations could be used to predict a disease risk index using an AI technique. The test performed by stratified k-fold cross validation achieved an accuracy of 96%.
{"title":"Lands DSS: A Decision Support System for Forecasting Crop Disease in Southern Sardinia","authors":"G. Fenu, Francesca Maridina Malloci","doi":"10.4018/ijdsst.2021010102","DOIUrl":"https://doi.org/10.4018/ijdsst.2021010102","url":null,"abstract":"Decision support systems (DSSs) are used in precision farming to address climate and environmental changes due to human action. However, increments in the amount of data produced continuously by the latest sensor and satellite technologies have recently incentivized the integration of artificial intelligence (AI). A review of research dedicated to the application of DSSs and AI in forecasting crop disease is proposed. In this paper, the authors describe the DSS LANDS developed for monitoring the main crop productions in Sardinia and the case study conducted to forecast potato late blight. A feed-forward neural network was implemented to investigate if weather data provided by regional stations could be used to predict a disease risk index using an AI technique. The test performed by stratified k-fold cross validation achieved an accuracy of 96%.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84628862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJDSST.2020100102
R. Zafar, Shah Zaib, Muhammad Asif
In the era of smart home technology, early warning systems and emergency services are inevitable. To make smart homes safer, early fire alarm systems can play a significant role. Smart homes usually utilize communication, sensors, actuators, and other technologies to provide a safe and smart environment. This research work introduced a model for the fire alarm system and designed a fire alarm detection (FAD) simulator to produce a synthetic dataset. The designed simulator utilizes a variety of sensors (temperature, gas, and humidity) to simulate fire alarm scenarios based on real-world data. The produced data is investigated and analyzed to classify the possible fire behaviors based on key assumptions taken from real-world scenarios. Different classification models are used to determine an optimal classifier for fire detection. The proposed technique can identify the false alarms based on parameters like temperature, smoke, and gas values of different sensors embedded in a fire alarm detection simulator.
{"title":"False Fire Alarm Detection Using Data Mining Techniques","authors":"R. Zafar, Shah Zaib, Muhammad Asif","doi":"10.4018/IJDSST.2020100102","DOIUrl":"https://doi.org/10.4018/IJDSST.2020100102","url":null,"abstract":"In the era of smart home technology, early warning systems and emergency services are inevitable. To make smart homes safer, early fire alarm systems can play a significant role. Smart homes usually utilize communication, sensors, actuators, and other technologies to provide a safe and smart environment. This research work introduced a model for the fire alarm system and designed a fire alarm detection (FAD) simulator to produce a synthetic dataset. The designed simulator utilizes a variety of sensors (temperature, gas, and humidity) to simulate fire alarm scenarios based on real-world data. The produced data is investigated and analyzed to classify the possible fire behaviors based on key assumptions taken from real-world scenarios. Different classification models are used to determine an optimal classifier for fire detection. The proposed technique can identify the false alarms based on parameters like temperature, smoke, and gas values of different sensors embedded in a fire alarm detection simulator.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75859766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Integrated decisions on merchandise image display and inventory planning are closely related to operational performance of online stores. A visual-attention-dependent demand (VADD) model has been developed to support online stores make the decisions. In the face of evolving products, customer needs, and competitors in an e-commerce environment, the benefits of using VADD model depend on how fast the model runs on the computer. As a result, a discrete particle swarm optimization (DPSO) method is employed to solve the VADD model. To verify the usability and effectiveness of DPSO method, it was compared with the existing methods for large-scale, medium-scale, and small-scale problems. The comparison results show that both GA and DPSO method perform well in terms of the approximation rate, but the DPSO method takes less time than the GA method. A sensitivity is conducted to determine the model parameters that influence the above comparison result.
{"title":"Integrated Decisions on Online Product Image Configuration and Inventory Planning Using DPSO","authors":"Kuan-Chung Shih, Yan-Kwang Chen, Yi-Ming Li, Chih-Teng Chen","doi":"10.4018/IJDSST.2020100101","DOIUrl":"https://doi.org/10.4018/IJDSST.2020100101","url":null,"abstract":"Integrated decisions on merchandise image display and inventory planning are closely related to operational performance of online stores. A visual-attention-dependent demand (VADD) model has been developed to support online stores make the decisions. In the face of evolving products, customer needs, and competitors in an e-commerce environment, the benefits of using VADD model depend on how fast the model runs on the computer. As a result, a discrete particle swarm optimization (DPSO) method is employed to solve the VADD model. To verify the usability and effectiveness of DPSO method, it was compared with the existing methods for large-scale, medium-scale, and small-scale problems. The comparison results show that both GA and DPSO method perform well in terms of the approximation rate, but the DPSO method takes less time than the GA method. A sensitivity is conducted to determine the model parameters that influence the above comparison result.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4018/IJDSST.2020100101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72398240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJDSST.2020100105
O. Ghanem, L. Xuemei
An efficiency evaluation is one of the most significant tools of transportation performance assessment and is of particular importance to decision making units to consider efficiency issues. The experience of Turkey can be used to compare and improve the efficiency of rail performance. The study employs both of radial and non-radial of data envelopment analysis method, where efficiency scores and technical efficiency of rail performance were ranked and compared over period 1977–2017. The study was fulfilled that Turkey rail is more capable in terms of exploiting its transport indicators into useful outputs. The outcomes indicated that the rail performance was operating most effectively, and the most efficient years were 1977, 1978, 1979, 1984, 1985, 1988, 1989, 1990, 1993, 2008, 2010, 2011, 2014, 2015, 2016, and 2017, whereas it exhibited relative inefficiency throughout 2001–2002, in which the efficiency scores decreased in relation to other years.
{"title":"Decision-Making Support in Evaluating Gaps and Efficiencies of the Railway Industry Performance: Using Non-Radial of Data Envelopment Analysis","authors":"O. Ghanem, L. Xuemei","doi":"10.4018/IJDSST.2020100105","DOIUrl":"https://doi.org/10.4018/IJDSST.2020100105","url":null,"abstract":"An efficiency evaluation is one of the most significant tools of transportation performance assessment and is of particular importance to decision making units to consider efficiency issues. The experience of Turkey can be used to compare and improve the efficiency of rail performance. The study employs both of radial and non-radial of data envelopment analysis method, where efficiency scores and technical efficiency of rail performance were ranked and compared over period 1977–2017. The study was fulfilled that Turkey rail is more capable in terms of exploiting its transport indicators into useful outputs. The outcomes indicated that the rail performance was operating most effectively, and the most efficient years were 1977, 1978, 1979, 1984, 1985, 1988, 1989, 1990, 1993, 2008, 2010, 2011, 2014, 2015, 2016, and 2017, whereas it exhibited relative inefficiency throughout 2001–2002, in which the efficiency scores decreased in relation to other years.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80517889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJDSST.2020100103
M. Aloud
The authors present a simple data-driven decision support system for stock market trading using multiple technical indicators, decision trees, and genetic algorithms (GAs). It assembles technical indicators set into a decision tree based on stock trading rules and generates buy, hold, and sell classes that represent trading decisions. The main contribution of this study is the use of GAs based on a two-step classification method. This allows for selecting the relevant inputs and adapting them to the market dynamic. The GAs are used at the data input selection step and the weight selection step. Classifiers of different technical indicators are trained in the first step and combined into the trading rules in the second step. Random sampling and data input selection techniques were used to ensure the required variety of technical indicators in the first step. An evaluation shows that the proposed algorithm improved forecasting accuracy from 73.6% to 81.78%.
{"title":"An Intelligent Stock Trading Decision Support System Using the Genetic Algorithm","authors":"M. Aloud","doi":"10.4018/IJDSST.2020100103","DOIUrl":"https://doi.org/10.4018/IJDSST.2020100103","url":null,"abstract":"The authors present a simple data-driven decision support system for stock market trading using multiple technical indicators, decision trees, and genetic algorithms (GAs). It assembles technical indicators set into a decision tree based on stock trading rules and generates buy, hold, and sell classes that represent trading decisions. The main contribution of this study is the use of GAs based on a two-step classification method. This allows for selecting the relevant inputs and adapting them to the market dynamic. The GAs are used at the data input selection step and the weight selection step. Classifiers of different technical indicators are trained in the first step and combined into the trading rules in the second step. Random sampling and data input selection techniques were used to ensure the required variety of technical indicators in the first step. An evaluation shows that the proposed algorithm improved forecasting accuracy from 73.6% to 81.78%.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75989723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJDSST.2020100104
M. Ebrahimi
This study aims to design and implement a new collaborative and integrated multi-agent system (MAS) to support chief executive officers (CEOs) of small-medium enterprises (SMEs) in formulating and implementing the right technology strategy (TS) in the renewable energy (RE) sector. In this regard, the TS requirements and methods are examined, and TSs are presented in two types of general TSs and specific TSs, including research strategies, investment strategies, and technology sourcing strategies, and also, system architecture is explained. Developing a novel comprehensive TS model for SMEs in the RE industry to provide a clear explanation of the systematic TS planning process and designing and implementing a distributed support system for facilitating collaboration between decision makers and TS negotiation are the most important contributions of this paper. In addition to developing existing knowledge about the importance of MAS for TS planning, the present study makes CEOs able to improve the process.
{"title":"A Distributed Fuzzy Multi-Agent-Based System in Collaborative Technology Strategy Making","authors":"M. Ebrahimi","doi":"10.4018/IJDSST.2020100104","DOIUrl":"https://doi.org/10.4018/IJDSST.2020100104","url":null,"abstract":"This study aims to design and implement a new collaborative and integrated multi-agent system (MAS) to support chief executive officers (CEOs) of small-medium enterprises (SMEs) in formulating and implementing the right technology strategy (TS) in the renewable energy (RE) sector. In this regard, the TS requirements and methods are examined, and TSs are presented in two types of general TSs and specific TSs, including research strategies, investment strategies, and technology sourcing strategies, and also, system architecture is explained. Developing a novel comprehensive TS model for SMEs in the RE industry to provide a clear explanation of the systematic TS planning process and designing and implementing a distributed support system for facilitating collaboration between decision makers and TS negotiation are the most important contributions of this paper. In addition to developing existing knowledge about the importance of MAS for TS planning, the present study makes CEOs able to improve the process.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77987145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.4018/ijdsst.2020070104
Fu-Shiung Hsieh
Although combinatorial auctions have been extensively studied, the factor of transportation cost has not been considered in most studies. Without considering transportation cost, the profits of the seller cannot be determined accurately. The goals of this article are to propose models, develop a solution methodology for the winner determination problem (WDP) in combinatorial auctions and study the effects of transportation cost on the seller's profits. Two models are proposed: one model considers transportation cost in WDP whereas the other one does not take transportation cost into account in WDP but calculates the transportation cost based on the solution obtained. The author formulates the WDPs for these two models and proposes a solution method. The author then analyzes and compares the two models to illustrate the advantage of taking transportation cost into account in combinatorial auctions. Finally, the author studies the influence of transportation cost on combinatorial auctions by examples and demonstrate effectiveness of our approach.
{"title":"A Comparative Study of Two Models for Handling Transportation Cost in Combinatorial Auctions","authors":"Fu-Shiung Hsieh","doi":"10.4018/ijdsst.2020070104","DOIUrl":"https://doi.org/10.4018/ijdsst.2020070104","url":null,"abstract":"Although combinatorial auctions have been extensively studied, the factor of transportation cost has not been considered in most studies. Without considering transportation cost, the profits of the seller cannot be determined accurately. The goals of this article are to propose models, develop a solution methodology for the winner determination problem (WDP) in combinatorial auctions and study the effects of transportation cost on the seller's profits. Two models are proposed: one model considers transportation cost in WDP whereas the other one does not take transportation cost into account in WDP but calculates the transportation cost based on the solution obtained. The author formulates the WDPs for these two models and proposes a solution method. The author then analyzes and compares the two models to illustrate the advantage of taking transportation cost into account in combinatorial auctions. Finally, the author studies the influence of transportation cost on combinatorial auctions by examples and demonstrate effectiveness of our approach.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73511686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.4018/ijdsst.2020070105
Misha Kakkar, Sarika Jain, Abhay Bansal, P. Grover
Humans use the software in every walk of life thus it is essential to have the best quality software. Software defect prediction models assist in identifying defect prone modules with the help of historical data, which in turn improves software quality. Historical data consists of data related to modules /files/classes which are labeled as buggy or clean. As the number of buggy artifacts as less as compared to clean artifacts, the nature of historical data becomes imbalance. Due to this uneven distribution of the data, it difficult for classification algorithms to build highly effective SDP models. The objective of this study is to propose a new nonlinear geometric framework based on SMOTE and ensemble learning to improve the performance of SDP models. The study combines the traditional SMOTE algorithm and the novel ensemble Support Vector Machine (SVM) is used to develop the proposed framework called SMEnsemble. SMOTE algorithm handles the class imbalance problem by generating synthetic instances of the minority class. Ensemble learning generates multiple classification models to select the best performing SDP model. For experimentation, datasets from three different software repositories that contain both open source as well as proprietary projects are used in the study. The results show that SMEnsemble performs better than traditional methods for identifying the minority class i.e. buggy artifacts. Also, the proposed model performance is better than the latest state of Art SDP model- SMOTUNED. The proposed model is capable of handling imbalance classes when compared with traditional methods. Also, by carefully selecting the number of ensembles high performance can be achieved in less time.
{"title":"Nonlinear Geometric Framework for Software Defect Prediction","authors":"Misha Kakkar, Sarika Jain, Abhay Bansal, P. Grover","doi":"10.4018/ijdsst.2020070105","DOIUrl":"https://doi.org/10.4018/ijdsst.2020070105","url":null,"abstract":"Humans use the software in every walk of life thus it is essential to have the best quality software. Software defect prediction models assist in identifying defect prone modules with the help of historical data, which in turn improves software quality. Historical data consists of data related to modules /files/classes which are labeled as buggy or clean. As the number of buggy artifacts as less as compared to clean artifacts, the nature of historical data becomes imbalance. Due to this uneven distribution of the data, it difficult for classification algorithms to build highly effective SDP models. The objective of this study is to propose a new nonlinear geometric framework based on SMOTE and ensemble learning to improve the performance of SDP models. The study combines the traditional SMOTE algorithm and the novel ensemble Support Vector Machine (SVM) is used to develop the proposed framework called SMEnsemble. SMOTE algorithm handles the class imbalance problem by generating synthetic instances of the minority class. Ensemble learning generates multiple classification models to select the best performing SDP model. For experimentation, datasets from three different software repositories that contain both open source as well as proprietary projects are used in the study. The results show that SMEnsemble performs better than traditional methods for identifying the minority class i.e. buggy artifacts. Also, the proposed model performance is better than the latest state of Art SDP model- SMOTUNED. The proposed model is capable of handling imbalance classes when compared with traditional methods. Also, by carefully selecting the number of ensembles high performance can be achieved in less time.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79213804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.4018/ijdsst.2020070102
Abdelghani Mohammed Bouchaala, R. Noureddine
The prioritization of equipment is among the decisions of great interest in maintenance management, given the effects it reflects on numerous sub-functions and the dependence it implies on various factors. The mastery of the techniques in this context is gaining an increasing importance, especially in heavy industries operating multiple production lines. According to the literature, the Analytic hierarchy process (AHP) method is among the most common techniques to resolve this problem, despite the concerns it involves. Knowing that, this technique supports two synthesis modes: distributive and ideal, and a confusing conflict is noticed; although the second mode seems theoretically more adapted to this problem, the first dominates in the practical aspect. In response to this conflict, the objective of this work is to demonstrate that the ideal synthesis mode is more suitable, through a comparative approach within this context. An improved AHP-approach is implicitly proposed within the study.
{"title":"Using AHP to Identify the Priority Equipment for Maintenance Actions","authors":"Abdelghani Mohammed Bouchaala, R. Noureddine","doi":"10.4018/ijdsst.2020070102","DOIUrl":"https://doi.org/10.4018/ijdsst.2020070102","url":null,"abstract":"The prioritization of equipment is among the decisions of great interest in maintenance management, given the effects it reflects on numerous sub-functions and the dependence it implies on various factors. The mastery of the techniques in this context is gaining an increasing importance, especially in heavy industries operating multiple production lines. According to the literature, the Analytic hierarchy process (AHP) method is among the most common techniques to resolve this problem, despite the concerns it involves. Knowing that, this technique supports two synthesis modes: distributive and ideal, and a confusing conflict is noticed; although the second mode seems theoretically more adapted to this problem, the first dominates in the practical aspect. In response to this conflict, the objective of this work is to demonstrate that the ideal synthesis mode is more suitable, through a comparative approach within this context. An improved AHP-approach is implicitly proposed within the study.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88924786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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