Reliable Decision-Making for Sustainable Transportation. This item is unavailable.Reliable Decision-Making for Sustainable Transportation

Reliable Decision-Making for Sustainable Transportation explores decision-making methods that incorporate expert and decision-maker opinions for improved reliability. The book examines fuzzy sets that capture ambiguity and enable a more comprehensive analysis of stakeholder perspectives, focusing on transportation case studies to demonstrate techniques for weighing criteria, ranking alternatives, and selecting optimal and reliable decisions. It seeks to advance transportation planning, traffic engineering, road safety, and sustainability by integrating state-of-the-art decision support systems that leverage AI and multiple stakeholder viewpoints.

This approach will benefit researchers and professionals across transportation, decision sciences, supply chain management, and operations looking for innovative ways to model uncertainty and decision-maker reliability and providing methodologies and frameworks for more robust group decisions.

• Introduces reliability concepts in decision-making, providing insights into the robustness of strategic choices for sustainable transportation
• Presents practical case studies within the transportation sector to illustrate the application of reliability principles in decision-making, offering tangible solutions to everyday challenges
• Provides reliable strategies for existing public transportation systems, considering the unique challenges faced by investment departments and engineers in the planning process

Gholamreza Haseli is currently a Research Assistant at University College Dublin, Ireland. Concurrently, he is a Ph.D. Candidate in Industrial Engineering at Tecnologico de Monterrey, Mexico. Recently, he introduced the Base-Criterion Method (BCM) and the HECON methods for multi-attribute decision-making. Gholamreza Haseli serves as a reliable group decision-making framework designer for analysis in the VOTE-TRA Project, funded by the Science Foundation Ireland through the Digital Voting Hub for Sustainable Urban Transport System (22/NCF/DR/11309). His research interests encompass decision science, fuzzy sets, fuzzy numbers, multi-criteria decision-making, waste management, and transportation.

Prof. Mostafa Hajiaghaei-Keshteli is an Associate Professor at the Department of Industrial Engineering, Tecnológico de Monterrey, which is one of the most prestigious universities for industry and supply chain in Mexico. He is also a Co-founder and Senior Researcher at the Center of Sustainable Smart Logistics (CLIS), established in 2022, to offer sustainable logistics solutions to the public and private sector in Mexico and North America. He has developed famous metaheuristic algorithms, including algorithms such as Keshtel Alg., Red Deer Alg., Tree Growth Alg., and the Social Engineering Optimizer. He is also an editorial member in some top Q1 journals at Elsevier, such as Applied Soft Computing (ASOC), Engineering Applications of Artificial Intelligence (EAAI), and Expert Systems with Applications (ESWA). He has a solid industrial background, both in managerial and consulting sectors, in different industries such as agriculture, food, automotive, transportation, and engine industries.

Sarbast Moslem received a Ph.D. degree in transport and vehicle engineering from the Budapest University of Technology and Economics, in 2020. He is currently a Postdoctoral Research Fellow with the School of Architecture Planning and Environmental Policy, University College Dublin (UCD). He has been involved in several research projects on national and international levels. He worked on several EU projects. He is a Coordinator and a Principal Investigator of the VOTE-TRA Project, funded by the Science Foundation Ireland. He is the author or co-author of several conference papers and articles. His research interests include transport planning, traffic engineering, logistics, supply chain management, soft computing, decision policy, fuzzy set theory, sustainability, and citizen science.