Template-Type: ReDIF-Article 1.0 Author-Name: R. Balasundaram Author-X-Name-First: R. Author-X-Name-Last: Balasundaram Author-Name: S. Sathiya Devi Author-X-Name-First: S. Sathiya Author-X-Name-Last: Devi Title: Genetic algorithm-based hybrid approach for optimal instance selection of minimising makespan in permutation flowshop scheduling Abstract: Recently, the instance selection is getting more attention for the researchers to achieve enhanced performance of algorithms. A typical flowshop dataset can be represented in the form of a number of instances. The instances that are recorded during production process may not be a good example to learn useful knowledge. Therefore, the selection of high quality instances can be considered as a search problem and be solved by evolutionary algorithms. In this work, a genetic algorithm (GA) is proposed to select a sub-set of best instances. The selected instances are represented in the form of IF-Then else rules using a decision tree (DT) algorithm. The seed solution from DT is used as input to a scatter search (SS) algorithm for a few iterations, which acts as a local search to find the best value of the selected instances. The GA is used to select best instances in order to have a smaller tree size with good solution accuracy for minimizing makespan criterion in permutation flowshop scheduling. The computational experiments are performed with standard problems and compared against various existing literatures. Journal: Int. J. of Business Intelligence and Systems Engineering Pages: 197-225 Issue: 3 Volume: 1 Year: 2019 Keywords: instance selection; genetic algorithm; decision tree algorithm; scatter search algorithm; makespan. File-URL: http://www.inderscience.com/link.php?id=98864 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijbise:v:1:y:2019:i:3:p:197-225 Template-Type: ReDIF-Article 1.0 Author-Name: James A. Rodger Author-X-Name-First: James A. Author-X-Name-Last: Rodger Author-Name: Pankaj Chaudhary Author-X-Name-First: Pankaj Author-X-Name-Last: Chaudhary Author-Name: Ganesh Bhatt Author-X-Name-First: Ganesh Author-X-Name-Last: Bhatt Title: Refining information systems competencies: the role of big data analytics resilience in organisational learning Abstract: Information systems competencies (ISC) have been an important area of inquiry for both business managers and academicians. It is now widely believed that in order to achieve sustainable business advantages, a firm must be able to renew IS competencies. Although the literature has discussed the importance of organisational learning (OL), there is not much known about how organisational learning renews IS competencies. Less is known about big data analytics resilience in information system competencies (BDARISC) and the organisation's ability to recover from lost information. In this research, we take a step in this direction and analyse the relationship of organisational learning on IS competencies for business advantages and investigate IS infrastructure flexibility, expertise, trust relationships, business performance and big data analytics resilience. Journal: Int. J. of Business Intelligence and Systems Engineering Pages: 226-250 Issue: 3 Volume: 1 Year: 2019 Keywords: IS competencies; organisational knowledge; business advantages; big data analytics; resilience; flexibility; expertise; trust; business performance. File-URL: http://www.inderscience.com/link.php?id=98867 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijbise:v:1:y:2019:i:3:p:226-250 Template-Type: ReDIF-Article 1.0 Author-Name: Sherief Abdallah Author-X-Name-First: Sherief Author-X-Name-Last: Abdallah Author-Name: Wesam Alnusairat Author-X-Name-First: Wesam Author-X-Name-Last: Alnusairat Title: Understanding user demographics using public transportation data Abstract: As smart cards become dominant in public transportation, more data is collected that capture passenger behaviours: their trip times, which stations do they hop in/out, etc. Understanding passenger demographics can have important applications for health and marketing, as well as public transportation. For example, if we know that a certain metro station has high concentration of young children (students), then the transport authority may increase the security in the station to ensure the children's safety. In this work, we collect real smart card data from a cosmopolitan city in the Middle East and analyse how it relates to the underlying passenger demographics. Our analysis illustrates how association rule-mining can expose rules that are indicative of age, gender and nationality based on usage patterns of public transportation. Journal: Int. J. of Business Intelligence and Systems Engineering Pages: 251-260 Issue: 3 Volume: 1 Year: 2019 Keywords: public transport; data science; association rule mining; passenger demographics; data mining. File-URL: http://www.inderscience.com/link.php?id=98868 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijbise:v:1:y:2019:i:3:p:251-260 Template-Type: ReDIF-Article 1.0 Author-Name: João Luiz Chela Author-X-Name-First: João Luiz Author-X-Name-Last: Chela Author-Name: Luiz Leduíno De Salles Neto Author-X-Name-First: Luiz Leduíno De Salles Author-X-Name-Last: Neto Author-Name: Renan Brito Butkeraites Author-X-Name-First: Renan Brito Author-X-Name-Last: Butkeraites Title: Efficient frontier of credit risk using Monte Carlo simulation Abstract: This paper presents a new methodology for the construction of an efficient frontier for private securities in the Brazilian market, aiming to obtain the maximum return with minimum risk. For this purpose, the modelling risk was considered to be the risk of default of the securities coupled with a measure of credit risk that also considers emerging markets without liquidity in the secondary credit market. We used a multi-objective approach to the problem, considering the existence of both objectives, the maximum return with minimum risk. The generation of the loss distribution was performed via Monte Carlo simulation. Computational tests demonstrate the use of this innovative methodology is an example built for the Brazilian market. Journal: Int. J. of Business Intelligence and Systems Engineering Pages: 261-270 Issue: 3 Volume: 1 Year: 2019 Keywords: credit risk; efficient frontier; optimisation. File-URL: http://www.inderscience.com/link.php?id=98924 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijbise:v:1:y:2019:i:3:p:261-270