Inland waterway ports selection and evaluation using stochastic analytical hierarchy process

ABSTRACT

Inland waterway ports are an integral part of a country’s transportation system and the overall economy it supports. Though these ports support and contribute to a country’s import-export business and transportation economy, little attention is paid, primarily on the characterisation of these inland waterway ports. In this paper, we develop a systematic, multi-criteria, multi-personnel decision making approach, referred to as stochastic Analytical Hierarchy Process (AHP), that can be used to characterise inland waterway ports and to identify the factors that contribute most towards a port’s performance and overall utilisation. This robust method can successfully handle judgment errors, biases, and inconsistencies, while reducing the uncertainty associated with decision-making. We use the state of Mississippi’s inland waterways system as a testbed to visualise and validate the modelling results. Results indicate that the Mississippi River ports demonstrate less capability over the ports located along the Tennessee-Tombigbee waterway, based on the criteria used as a part of this study. A number of sensitivity analyses were performed, including additional investments, augmenting dredging capabilities, and introducing new businesses, to reveal managerial insights about the selection of inland waterway ports in Mississippi.

KEYWORDS:

• Inland waterway ports
• stochastic Analytical hierarchy process
• port characterisation
• multi-criteria decision making process

Acknowledgements

The authors also gratefully acknowledge the following personnel for their valuable insights and generous data support that greatly assisted this research: Robby Burt and Trung Trinh from Mississippi Department of Transportation (MDOT); Robert Sheely, Sara Fuller and Jenna Johnson from CAVSE; and Randy Jones, T.C. Falls, and Larry Dalton from ISER.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors gratefully acknowledge the funding support from U.S. Army Engineer Research and Development Center (ERDC).

Notes on contributors

Farjana Nur

Farjana Nur is a Ph.D. Candidate in Industrial & Systems Engineering department at Mississippi State University. She is currently working as a graduate assistant in this department. Her research interest involves stochastic programming, supply chain optimisation with specific applications to transportation, power management, logistic networks. She researched on decision support systems combining human systems engineering and logistics and demonstrated the real-life case examples where these decision support systems can help. Her research appeared in multiple reputed journals such as, OR Spectrum, Computers & Industrial Engineering, Journal of Computational Design and Engineering, Reliability Engineering and System Safety, Complexity and Journal of Mechanical Engineering. Additionally, she has publications in the proceedings of IIE Annual Conference and Expo, ASME conference, Transportation Research Board Annual Meeting. She holds her professional membership in Institute for Operations Research and the Management Sciences (INFORMS), Women in ORMS (WORMS), Institute of Industrial and Systems Engineering (IISE), and Human Factors and Ergonomics Society (HFES) and is actively involved in different voluntary organisations.

Mohammad Marufuzzaman

Mohammad Marufuzzaman received his Ph.D. in Industrial & Systems Engineering from Mississippi State University in 2014. He received his MASc degree in Industrial Systems Engineering from University of Regina, Canada in 2010 and B.Sc. degree in Industrial & Production Engineering from Shah Jalal University of Science & Technology, Bangladesh in 2006. He joined Industrial & Systems Engineering department as a Visiting Assistant Professor in August 2014. His main areas of interest are in supply chain optimisation with applications in renewable energy, stochastic programming, decomposition methods, solving large scale supply chain network problems and supply chain risk management. Dr Maruf’s publications have appeared in journals such as Transportation Science, Computers & Operations Research, Transportation Research Part E, International Journal of Production Economics, Canadian Journal of Chemical Engineering and several conference proceedings. He is a member of the Institute for Operations Research and the Management Sciences (INFORMS) and the Institute of Industrial Engineers (IIE).

Stephen M. Puryear

Stephen M. Puryear currently works at the CAVS Extension Center, Mississippi State University. Stephen does research in Transportation Engineering, Human Development Theory and Entrepreneurial Economics. Their current project is ‘CNG usage as an illustration of innovation acceptance’. His transportation-based research is published in Computers & Industrial Engineering, Reliability Engineering and System Safety and Journal of Complexity to name a few.

Emily S. Wall

Emily S. Wall is a Research Engineer for the Center for Advanced Vehicular Systems Extension (CAVS-E). She graduated from Mississippi State University (MSU) with a masters of engineering (2018), bachelor’s in Industrial and Systems Engineering in 2015, and received her Six Sigma Black Belt from MSU’s CAVS Extension in June 2016. She is also working towards obtaining her Ph.D. in Industrial Engineering and PE licensure through the MS State Engineering Board. Her current research involves lean six sigma practices and applications, manufacturability, modelling and simulations, and virtual reality applications. She also participates in the teaching of workshops and classes including: Introduction to Engineering, Gage RnR, Minitab and Statistical Process Control.

Reuben Burch

Reuben Burch is an assistant professor in Industrial & Systems Engineering department at Mississippi State University. His primary research interests centre around human factors, ergonomics and future generations of technologies. He is particularly interested in the design of and human interaction with rugged mobile and wearable tools, robotics and autonomy and contextual awareness within the industrial workplace. Dr Burch relies upon the industrial and academic professional network he’s created in order to enhance his academic expertise both in research and in the classroom. His industry-based research is published in Applied Ergonomics, Total Quality Management & Business Excellence and the International Journal of Industrial Ergonomics to name a few. Dr Burch serves as a logistics and technology advisor for numerous universities and multiple Fortune 100 companies around the world.