khaled Mili | Quantitative Methods | Cutting-edge Industry Advancement Award

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Mr. khaled Mili | Quantitative Methods | Cutting-edge Industry Advancement Award

King Faisal University Saudi Arabia

Author Profile

Early Academic Pursuits

Dr. Khaled Mili embarked on his academic journey with a fervent pursuit of knowledge in quantitative management and economics. His educational background includes a Master's in Quantitative Economics and Management from the Higher Institute of Management of Sousse, Tunisia, followed by a Master of Research in Modeling from the Higher Institute of Management of Tunis. This foundation culminated in a significant milestone with a Doctorate Thesis in Management from LAVAL University in Québec, Canada, and the Institute of High Commercial Studies of Carthage, Tunisia.

Professional Endeavors

His professional journey has been marked by a relentless dedication to advancing the fields of transportation, operations management, and decision support systems. His postdoctoral stage at the Interuniversity Research Centre on Enterprise Networks, Logistics, and Transportation (CIRRELT) in Québec, Canada, served as a platform for honing his expertise and contributing to cutting-edge research initiatives.

Contributions and Research Focus

With a focus on maritime transportation and logistics optimization, His research contributions have been instrumental in enhancing operational efficiency in various domains. Notable projects include his involvement in logistical projects for repositioning Canadian military bases worldwide and optimizing operational processes at the Tunis Maritime Port. His research interests span across decision support systems, multi-criteria decision-making, and supply chain management.

Accolades and Recognition

His academic and professional achievements have garnered widespread recognition and acclaim. His groundbreaking work in the field of transportation and logistics earned him the distinction of being the first Tunisian student to defend a doctoral thesis online at the Virtual University of Tunis. Furthermore, his participation in international scientific conferences and contributions to scholarly publications underscore his standing as a thought leader in his field.

Impact and Influence

Through 15 years of teaching experience across various prestigious institutions, Dr. Mili has played a pivotal role in shaping the next generation of scholars and practitioners in management, operational research, and supply chain management. His commitment to excellence in both research and teaching has left an indelible mark on the academic landscape, inspiring students and peers alike.

Legacy and Future Contributions

His legacy is defined by his relentless pursuit of excellence, innovation, and industry advancement. As he continues to explore new frontiers in quantitative methods and operational research, his vision is to drive meaningful change and foster collaboration across interdisciplinary domains. With a steadfast commitment to pushing the boundaries of knowledge and practice, His future contributions are poised to leave a lasting impact on the field of management science and beyond.

Citations

A total of  72 citations for his publications, demonstrating the impact and recognition of his research within the academic community.

  • Citations            72
  • h-index              3
  • i10-index           2

Notable Publications 

The effect of corruption on the environmental quality in African countries: a panel quantile regression analysis

Six Sigma approach for the straddle carrier routing problem

Optimizing the operational process at container terminal

Blockchain Traceability to Ensure the Veracity of Diplomas

Genetic procedure for the Single Straddle Carrier Routing Problem

Solving the straddle carrier routing problem using Six Sigma methodology

Expectation-maximization algorithms for obtaining estimations of generalized failure intensity parameters

New procedure for assigning drivers to work schedules at a container terminal

Multiple straddle carrier routing problem

Genetic procedure for the Single Straddle Carrier Routing Problem

The Link between Six Sigma and Business Performance

 

Data Analysis

Posted on by Popular Scientist

Data Analysis

Introduction of Data Analysis

 

Data analysis is the art and science of transforming raw information into meaningful insights. In an era of unprecedented data abundance, this field employs statistical methods, machine learning, and visualization techniques to unveil patterns, trends, and knowledge, empowering decision-making across diverse industries.

  1. Machine Learning Algorithms:
  2. Delving into the realm of machine learning, data analysis harnesses algorithms that enable systems to learn from data patterns. From classification to regression, these algorithms are pivotal in predicting outcomes and uncovering hidden relationships within datasets.
  3. Big Data Analytics:
  4. Navigating the vast landscape of big data, analysts grapple with massive datasets that traditional methods struggle to handle. Big data analytics involves techniques and technologies to extract valuable insights from these colossal pools of information.
  5. Data Visualization and Storytelling:
  6. Beyond numbers, effective data analysis incorporates the art of visualization. This subtopic explores techniques for creating compelling charts, graphs, and dashboards, transforming complex datasets into narratives that are easily understandable and actionable.
  7. Predictive Analytics:
  8. In the quest to anticipate future trends, predictive analytics employs statistical algorithms and machine learning models to forecast outcomes. This subfield is crucial for businesses seeking to make informed decisions based on likely future scenarios.
  9. Ethical Data Analysis:
  10. With great analytical power comes the responsibility to ensure ethical practices. This subtopic delves into the ethical considerations surrounding data analysis, including privacy, bias mitigation, and transparency, ensuring that insights are derived responsibly and equitably.

These subtopics reflect the multifaceted nature of data analysis, showcasing the diverse tools and methodologies employed to extract meaningful information from the ever-expanding universe of data.

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