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

2019 ◽  
Author(s):  
Nikos Kanakaris ◽  
Nikos Karacapilidis ◽  
Alexis Lazanas
Keyword(s):  
Machine Learning ◽  
Project Management ◽  
Operations Research ◽  
Flexible Integration ◽  
Research Tools

Mapping operations research in project management: a bibliometric analysis

2021 ◽  
Vol 39 (1) ◽  
pp. 52
Author(s):  
Andrés Muñoz Villamizar ◽  
Elyn L. Solano Charris ◽  
Rodrigo Romero Silva
Keyword(s):  
Project Management ◽  
Bibliometric Analysis ◽  
Operations Research

Trane/Ingersoll Rand Combines Lean and Operations Research Tools to Redesign Feeder Manufacturing Operations

2013 ◽  
Vol 43 (4) ◽  
pp. 325-340 ◽  
Author(s):  
John B. Jensen ◽  
Sanjay L. Ahire ◽  
Manoj K. Malhotra
Keyword(s):  
Operations Research ◽  
Manufacturing Operations ◽  
Research Tools

scholarly journals Combinatorial Optimization and Reasoning with Graph Neural Networks

2021 ◽  
Author(s):  
Quentin Cappart ◽  
Didier Chételat ◽  
Elias B. Khalil ◽  
Andrea Lodi ◽  
Christopher Morris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Combinatorial Optimization ◽  
Computer Science ◽  
Operations Research ◽  
Building Block ◽  
Related Data ◽  
Problem Instances ◽  
Graph Neural Networks

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have mostly focused on solving problem instances in isolation, ignoring the fact that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks, as a key building block for combinatorial tasks, either directly as solvers or by enhancing the former. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at researchers in both optimization and machine learning.

Integration of Data Mining and Operations Research

2011 ◽  
pp. 1046-1052
Author(s):  
Stephan Meisel
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Operations Research ◽  
Secondary Analysis ◽  
The Other ◽  
Optimization Models ◽  
Optimal Solutions ◽  
Number Of Publications ◽  
The One

Basically, Data Mining (DM) and Operations Research (OR) are two paradigms independent of each other. OR aims at optimal solutions of decision problems with respect to a given goal. DM is concerned with secondary analysis of large amounts of data (Hand et al., 2001). However, there are some commonalities. Both paradigms are application focused (Wu et al., 2003; White, 1991). Many Data Mining approaches are within traditional OR domains like logistics, manufacturing, health care or finance. Further, both DM and OR are multidisciplinary. Since its origins, OR has been relying on fields such as mathematics, statistics, economics and computer science. In DM, most of the current textbooks show a strong bias towards one of its founding disciplines, like database management, machine learning or statistics. Being multidisciplinary and application focused, it seems to be a natural step for both paradigms to gain synergies from integration. Thus, recently an increasing number of publications of successful approaches at the intersection of DM and OR can be observed. On the one hand, efficiency of the DM process is increased by use of advanced optimization models and methods originating from OR. On the other hand, effectiveness of decision making is increased by augmentation of traditional OR approaches with DM results. Meisel and Mattfeld (in press) provide a detailed discussion of the synergies of DM and OR.

Machine Learning and Data Science Project Management From an Agile Perspective

2022 ◽  
pp. 73-88
Author(s):  
Murat Pasa Uysal
Keyword(s):  
Machine Learning ◽  
Project Management ◽  
Data Science ◽  
Business Models ◽  
Background Information ◽  
Future Research ◽  
Initial Attempt ◽  
Agile Project Management ◽  
Experimental Implementation ◽  
Management Method

Successful implementations of machine learning (ML) and data science (DS) applications have enabled innovative business models and brought new opportunities for organizations. On the other hand, research studies report that organizations employing ML and DS solutions are at a high risk of failure and they can easily fall short of their objectives. One major factor is to adopt or tailor a project management method for the specific requirements of ML and DS applications. Therefore, agile project management (APM) may be proposed as a solution. However, there is significantly less study that explores ML and DS project management from an agile perspective. In this chapter, the authors discuss methods and challenges according to the background information and practice areas of ML, DS, and APM. This study can be viewed as an initial attempt to enhance these knowledge and practice domains in view of APM. Therefore, future research efforts will focus on the challenges as well as the experimental implementation of APM methods in real industrial case studies of ML and DS.

Development of a Digital ESP Performance Monitoring System Based on Artificial Intelligence

2021 ◽  
Author(s):  
Göktug Diker ◽  
Herwig Frühbauer ◽  
Edna Michelle Bisso Bi Mba
Keyword(s):  
Machine Learning ◽  
Project Management ◽  
Data Science ◽  
Performance Monitoring ◽  
Intelligent System ◽  
Operating Conditions ◽  
Oil Field ◽  
Training Dataset ◽  
Detailed Knowledge ◽  
Concept Evaluation

Abstract Wintershall Dea is developing together with partners a digital system to monitor and optimize electrical submersible pump (ESP) performance based on the data from Mittelplate oil field. This tool is using machine learning (ML) models which are fed by historic data and will notify engineers and operators when operating conditions are trending beyond the operating envelope, which enables an operator to mitigate upcoming performance problems. In addition to traditional engineering methods, such a system will capture knowledge by continuous improvement based on ML. With this approach the engineer has a system at hand to support the day-to-day work. Manual monitoring and on demand investigations are now backed up by an intelligent system which permanently monitors the equipment. In order to create such a system, a proof of concept (PoC) study has been initiated with industry partners and data scientists to evaluate historic events, which are used to train the ML-systems. This phase aims to better understand the capabilities of machine learning and data science in the subsurface domain as well as to build up trust for the engineers with such systems. The concept evaluation has shown that the intensive collaboration between engineers and data scientist is essential. A continuous and structured exchange between engineering and data science resulted in a mutual developed product, which fits the engineer's needs based on the technical capabilities and limits set by ML-models. To organize such a development, new project management elements like agile working methods, sprints and scrum methods were utilized. During the development Wintershall Dea has partnered with two organizations. One has a pure data science background and the other one was the data science team of the ESP manufacturer. After the PoC period the following conclusions can be derived: (1) data quality and format is key to success; (2) detailed knowledge of the equipment speeds up the development and the quality of the results; (3) high model accuracy requires a high number of events in the training dataset. The overall conclusion of this PoC is that the collaboration between engineers and data scientists, fostered by the agile project management toolkit and suitable datasets, leads to a successful development. Even when the limits of the ML-algorithms are hit, the model forecast, in combination with traditional engineering methods, adds significant value to the ESP performance. The novelty of such a system is that the production engineer will be supported by trusted ML-models and digital systems. This system in combination with the traditional engineering tools improves monitoring of the equipment and taking decisions leading to increased equipment performance.

Mapping operations research in project management: A bibliometric analysis

2020 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Rodrigo Romero Silva ◽  
Elyn Solano ◽  
Andrés Muñoz Villamizar
Keyword(s):  
Project Management ◽  
Bibliometric Analysis ◽  
Operations Research

scholarly journals The Successful Delivery of Megaprojects: A Novel Research Method

2017 ◽  
Vol 48 (5) ◽  
pp. 78-94 ◽  
Author(s):  
Giorgio Locatelli ◽  
Miljan Mikic ◽  
Milos Kovacevic ◽  
Naomi Brookes ◽  
Nenad Ivanisevic
Keyword(s):  
Machine Learning ◽  
Project Management ◽  
Research Method ◽  
Machine Learning Techniques ◽  
Exact Test ◽  
Delivery Performance ◽  
Learning Techniques ◽  
Benefits Realization ◽  
And Performance ◽  
Effective Design

Megaprojects are often associated with poor delivery performance and poor benefits realization. This article provides a method of identifying, in a quantitative and rigorous manner, the characteristics related to project management success in megaprojects. It provides an investigation of how stakeholders can use this knowledge to ensure more effective design and delivery for megaprojects. The research is grounded in 44 mega-projects and a systematic, empirically based methodology that employs the Fisher's exact test and machine learning techniques to identify the correlation between megaprojects’ characteristics and performance, paving the way to an understanding of their causation.

scholarly journals THE MODEL OF IT PROJECT MANAGEMENT SYSTEM BASED ON MACHINE LEARNING

2019 ◽  
Vol 0 (1(1326)) ◽  
pp. 42-50
Author(s):  
Вадимович Максим Проскурін ◽  
Віктор Володимирович Морозов ◽  
Тетяна Миколаївна Шелест
Keyword(s):  
Machine Learning ◽  
Project Management ◽  
Management System ◽  
It Project Management ◽  
Project Management System ◽  
It Project
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