On the Advancement of Project Management through a Flexible Integration of Machine Learning and Operations Research Tools

Kanakaris, Nikos; Karacapilidis, Nikos; Lazanas, Alexis

doi:10.5220/0007387103620369

Cited by 5 publications

(7 citation statements)

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“…In addition, these attributes need to be continuously maintained since they are not static; workers skills and qualifications, for example, may change over time. Lastly, mathematical models can propose near-optimal solutions that are not realistically feasible (Kanakaris et al 2019).…”

Section: Nc Idenɵfiedmentioning

confidence: 99%

“…We were looking for a solution that imposes fewer restrictions on data. In 2019, Kanakaris, Karacapilidis, and Lazanas showed that task assignment, among other aspects of project management, can be significantly improved through the techniques of machine learning (Kanakaris et al 2019). These tools are programmed to work with large data sets, allowing much more freedom in the context of its characteristics.…”

Section: Nc Idenɵfiedmentioning

confidence: 99%

“…Machine learning (ML) is a rapidly growing set of automatic pattern recognition methods aiming to exploit available historical data and classify without human intervention (Kanakaris et al 2019). The extent of the impact ML has had on our lives is beyond understanding and Many day-to-day applications rely on ML methods (Kan 2017).…”

Section: Task Assignment Based On Machine Learningmentioning

confidence: 99%

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Improving nonconformity responsibility decisions: a semi-automated model based on CRISP-DM

Ziv

Parmet

2021

Int J Syst Assur Eng Manag

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Nonconformity (NC) management is a fundamental process in production, yet the literature notion of it does not always align with what is practiced in reality. In particular, the literature often excludes the NC responsibility decision, which is a difficult, costly and time-consuming task assignment, but also an integral part of the NC management process. We propose a semi-automated model we call SANC, which improves the accuracy of NC responsibility decisions and significantly cuts their costs. We base our methodology on CRISP-DM and extend it to fit the semi-automated NC responsibility decision. Unlike the original CRISP-DM, SANC utilizes existing organizational resources, and thus extends the capabilities of CRISP-DM in terms of both achieving greater overall performance and broadening its appeal to more traditional production processes. We demonstrate this solution by implementing it in a large-scale assembly plant in the printing industry, that may result in savings of over $186 K according to our assessments.

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Section: Nc Idenɵfiedmentioning

confidence: 99%

Section: Nc Idenɵfiedmentioning

confidence: 99%

Section: Task Assignment Based On Machine Learningmentioning

confidence: 99%

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Improving nonconformity responsibility decisions: a semi-automated model based on CRISP-DM

Ziv

Parmet

2021

Int J Syst Assur Eng Manag

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“…Apriori algorithms are generally used in the retail sector to determine sales patterns (Chandra & Dewi, 2019;Fitriah, Riadi, & Herman, 2023;Kurnia, Isharianto, Giap, Hermawan, & Riki, 2019;Santoso, 2021), consumer patterns (Panjaitan et al, 2019;Qisman, Rosadi, & Abdullah, 2021), and consumer behavior (Sussolaikah, 2021). Apart from retail, the Apriori algorithm is also used to analyze crime data (Mangara Wainana, Njuguna Karomo, Kyalo, & Mutai, 2020), DNA sequence pattern mining (Kanakaris, Karacapilidis, & Lazanas, 2019;Yang et al, 2020), detect malicious network traffic (Gao et al, 2020), identify behavioral phenotypes of loneliness and social isolation (Dorya et al, 2019), and detect diabetes (Mahboob Alam et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Market Basket Analysis Using Apriori Algorithm to Find Effective Fiscal Policy Mix with R Programming

Isnen Hadi Al Ghozali,

Arief Wibowo

2023

Decode

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Fiscal policy drives a country's economy and is the most effectivepolicy to restore a country's economy. When a recession occurs, the fiscal policy helps a country increase aggregate demand in the market for goods and services. This study proposes a fiscal policy mix that can be implemented based on historical data. So this research focuses on using association rules to assist decision-makers (regulators in adopting appropriate fiscal policies in the global VUCA (Volatility, Uncertainty, Complexity, Ambiguity) era. Therefore, an experimental research approach was used in this study to produce the best association rules. The research was carried out in six stages to obtain conclusions, namely problem identification in the research sample and literature review related to apriori algorithms, data collection, data pre-processing, parameter determination, research findings building a priori algorithms, and knowledge extraction formed from a priori algorithms. Based on the experimental results using the a priori algorithm, 657 rules were obtained with a minimum variation of two to six itemsets. Rule 6 produces an average value of the budget performance of 90.65. Rule 6 also says that the performance value of the budget can be increased by spreading out funding sources and ensuring that operational spending is as efficient as possible.

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“…Traditional models and algorithms can be adjusted for specific circumstances but are often more generally applied and taught. This is where artificial intelligence and machine learning show their benefit through using historical datasets to best determine patterns and algorithms, features and convolutions, that best-fit circumstance from actual data that has been realized in actual projects [20].…”

Section: Methodology For Educational Artificial Intelligence In Pmmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI) in Project Management Curriculum: Exploration and Application to Time, Cost, and Risk

Radhakrishnan,

Jaurez

2021 ASEE Virtual Annual Conference Content Access Proceedings

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He is the Academic Program Director for MS Engineering Management program. He develops and teaches Engineering and Sustainability Management graduate and undergraduate level courses. Ben has taught Sustainability workshops in Los Angeles (Army) and San Diego (SDGE). His special interests and research include promoting Leadership in Sustainability Practices, application of Blockchain technology for reliable corporate social responsibility reports, energy management of Data Centers and to establish Sustainable strategies for enterprises. He is now researching bringing in Artificial Intelligence into project management. He is an Affiliate Researcher at Lawrence Berkeley National Laboratory, Berkeley, CA, focusing on the energy efficiency of IT Equipment in a Data Centers. As a means of promoting student-centric learning, Prof. Radhakrishnan has successfully introduced games into his sustainability classes where students demonstrate the 3s of sustainability, namely, Environment, Economics and Equity, through games. He is the coauthor of books relating to gaming and energy and has published papers in international journals. He is an active participant in national and international conferences with papers and presentations. Before his teaching career, he had a very successful corporate management career working in R&D at Lucent Technologies and as the Director of Global Technology Management at Qualcomm. He had initiated and managed software development for both the companies in India. Prof. Radhakrishnan holds three graduate Degrees (M. Tech, M.S., M.B.A), and Sustainable Business Practices certification.

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On the Advancement of Project Management through a Flexible Integration of Machine Learning and Operations Research Tools

Cited by 5 publications

References 0 publications

Improving nonconformity responsibility decisions: a semi-automated model based on CRISP-DM

Improving nonconformity responsibility decisions: a semi-automated model based on CRISP-DM

Market Basket Analysis Using Apriori Algorithm to Find Effective Fiscal Policy Mix with R Programming

Explainable Artificial Intelligence (XAI) in Project Management Curriculum: Exploration and Application to Time, Cost, and Risk

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