Supporting Operational and Real-time Planning Tasks of Road Freight Transport with Machine Learning. Guiding the Implementation of Machine Learning Algorithms
Sandra Lechtenberg
Cite this publication as
Sandra Lechtenberg, Supporting Operational and Real-time Planning Tasks of Road Freight Transport with Machine Learning. Guiding the Implementation of Machine Learning Algorithms (2023), Logos Verlag, Berlin, ISBN: 9783832583156
Descripción / Abstract
World-wide trends such as globalization, demographic shifts, increased customer demands, and shorter product lifecycles present a significant challenge to the road freight transport industry: meeting the growing road freight transport demand economically while striving for sustainability.
Artificial intelligence, particularly machine learning, is expected to empower transport planners to incorporate more information and react quicker to the fast-changing decision environment. Hence, using machine learning can lead to more efficient and effective transport planning. However, despite the promising prospects of machine learning in road freight transport planning, both academia and industry struggle to identify and implement suitable use cases to gain a competitive edge.
In her dissertation, Sandra Lechtenberg explores how machine learning can enhance decision-making in operational and real-time road freight transport planning. She outlines an implementation guideline, which involves identifying decision tasks in planning processes, assessing their suitability for machine learning, and proposing steps to follow when implementing respective algorithms.
Artificial intelligence, particularly machine learning, is expected to empower transport planners to incorporate more information and react quicker to the fast-changing decision environment. Hence, using machine learning can lead to more efficient and effective transport planning. However, despite the promising prospects of machine learning in road freight transport planning, both academia and industry struggle to identify and implement suitable use cases to gain a competitive edge.
In her dissertation, Sandra Lechtenberg explores how machine learning can enhance decision-making in operational and real-time road freight transport planning. She outlines an implementation guideline, which involves identifying decision tasks in planning processes, assessing their suitability for machine learning, and proposing steps to follow when implementing respective algorithms.
Descripción
Sandra Lechtenberg works as a research assistant at the European Research Center for Information Systems (ERCIS), University of Münster. She earned both her bachelorâs and masterâs degrees in information systems at the same institution, with a specialization in data analytics and logistics. She also integrated these fields in her doctoral research conducted at the Chair for Information Systems and Supply Chain Management, successfully completing her PhD in January 2023.
Índice
- BEGINN
- Preface
- Acknowledgement
- Table of Contents
- List of Figures
- List of Tables
- List of Abbreviations
- 4 Development of a Process Reference Model for Operational and Real-Time Planning 53
- 1 Introduction
- 1.1 Motivation and Problem Statement
- 1.2 Research Objective
- 1.3 Research Design
- 1.4 Thesis Structure
- 2 Introduction to Road Freight Transport
- 2.1 Road Freight Transport
- 2.2 Scope of the Thesis
- 3 Research Approach
- 3.1 Structured Literature Review
- 3.2 Multi-vocal Literature Review
- 3.3 Semi-structured Interviews
- 3.4 Integration of Methods in the Design Science Framework
- 4.1 Terminology and Goals of Process Reference Models
- 4.2 Requirements for the developed Process Reference Model
- 4.3 State of the Art - Depiction of operational and real-time transport planning in existing models
- 4.4 Method for Developing the Process Reference Model
- 4.5 A Process Reference Model for Operational and Real-Time Transport Planning in Road Freight Transport
- 4.6 Identification of Decision Tasks
- 4.7 Evaluation
- 4.8 Limitations and Outlook
- 5 Machine Learning for Road Freight Transport
- 5.1 Introduction to Machine Learning
- 5.2 State of the Art: Machine Learning for Road Freight Transport
- 6 Identification of Decision Tasks suitable for Machine Learning
- 6.1 Goal and Requirements of the Suitability Assessment Approach
- 6.2 Characteristics of ML-addressable Problems
- 6.3 Question-based Machine Learning Suitability Assessment
- 6.4 Evaluation
- 6.5 Limitations and Outlook
- 7 Design of an Implementation Guideline
- 7.1 Goal and Requirements of the Implementation Guideline
- 7.2 Method for Developing the ML Implementation Guideline
- 7.3 State of the Art - Implementation Guidelines for using Machine Learning
- 7.4 A Three-Cycle Machine Learning Implementation Guideline for Road Freight Transport Planning
- 7.5 Evaluation
- 7.6 Limitations and Outlook
- 8 Case-based Evaluation
- 8.1 Overview of Evaluation Approach and Use Cases
- 8.2 Case A: Price Estimation
- 8.3 Case B: Freight Prediction
- 8.4 Case C: Pattern Recognition
- 8.5 Discussion of Insights and Limitations
- 9 Conclusion
- 9.1 Summary
- 9.2 Limitations
- 9.3 Outlook
- References
- Appendix
- A Process Reference Model for Operational and Real-Time Transport Planning
- A.1 Glossary
- A.2 Complete Process Reference Model
- A.3 Overview of used BPMN elements
- A.4 Development Interview Guideline
- A.5 Evaluation Interview Guideline
- A.6 Evaluation Interviews Summary
- B Suitability Assessment
- B.1 Assessment Questions and Explanatory Statements
- B.2 Assessment of Strong Characteristics
- B.3 Evaluation Interview Guideline
- B.4 Evaluation Interview Summary
- C Implementation Guideline
- C.1 Sources for Experiment Cycle
- C.2 Strengths and Weaknesses of ML Algorithm Classes
- C.3 Evaluation Interview Guideline
- C.4 Evaluation Interview Summary
- D Case-based Evaluation
- D.1 Assessment of Strong Characteristics
- D.2 Assessment of Soft Characteristics