Donald C. Sweeney

Simulation of alternative approaches to relieving congestion at locks in a river transportion system

We construct a discrete-event simulation model to investigate the impact of alternative decision rules and infrastructural improvements to relieve traffic congestion in a section of the Upper Mississippi River navigation system. The model covers a series of five locks that serve commercial tows with widely different barge configurations, as well as private recreational vessels. Mixes and intensities of vessel activity are highly dependent on the time of year, day of week and time of day. The model reveals that some improvement in performance (especially in peak periods) can be achieved by scheduling lock activity with priority given to vessels with shortest average processing and lock set-up times (tempered by the time that vessels have spent in queue). Greater improvement occurs with the use of helper boats and greatest improvement occurs with enlarged locks. The alternative remedies must be evaluated with consideration of their dramatically different capital costs.

Teaching supply chain and logistics management through commercial software

Purpose – The purpose of this paper is to describe the development and teaching of graduate courses providing in‐depth experiential learning employing commercial supply chain management software. The benefits of teaching such courses are described, the challenges in offering such courses are identified, and some solutions to overcome the challenges are offered.Design/methodology/approach – The experiences of the authors in developing and teaching supply chain management courses utilizing commercial software provided a basis for discussing the benefits and challenges associated with teaching students the management of modern supply chains using commercial decision‐support software.Findings – Incorporating commercial software in university programs presents a myriad of challenges and therefore is rarely done. However, providing students with in‐depth knowledge of commercial logistics and supply chain management software improves their understanding of supply chain issues and provides a key differentiator in...

A Robust Strategy for Managing Congestion at Locks on the Upper Mississippi River

The Mississippi River is an important waterway in national and global supply chains, especially for agricultural commodities. The tools available for managing traffic congestion on the Upper Mississippi River (UMR) include operational policies such as sequencing vessels at locks, tactical decisions such as using helper boats to speed lockages, and strategic decisions such as constructing new larger locks. We describe the use of our detailed multi-lock discrete event simulation model to evaluate operational, tactical and strategic congestion mitigation measures for the UMR. With a focus on policy issues surrounding lock expansion, we evaluate the national net economic benefits of different congestion mitigation measures and identify a robust strategy for implementing measures that maximizes the national net economic benefit. We also highlight the importance of traffic forecasts for future inland waterway infrastructure investments.

Triangulation of Modeling Methodologies for Strategic Decisions in an Inland Waterway Transportation System

We show how optimizing models, computer simulation, and expert opinion were integrated to investigate the potential performance of a waterway navigation system under different scheduling regimes and infrastructure changes. Analytical models are used to derive scheduling rules that balance efficiency against equity. Discrete-event simulation is used to assess system performance under various infrastructural changes (helper boats, expanded locks) and traffic management schemes (such as load leveling, priority scheduling, or self-regulation by the barge industry). Expert opinion is sought to address concerns of stakeholders and shed light on industry reactions to different policy alternatives. We show the importance of triangulation involving different modeling and philosophical approaches in generating relevant information for development of solution strategies.

Measuring the impacts of administrative policies on student performance in higher education

Abstract In this study, we investigate the joint impacts of class size, modality of content delivery and instructor workloads on 1078 student performances from 2010 through 2014 in a required College of Business Administration Information Systems (IS) course at a public Midwestern university, controlling for 11 demographic and other factors that might impact student performance. Class size, modality of delivery and instructor assignments are strategic college decisions affected by and subject to administrative policies that can impact the probabilities of student performance. Using the Akaike Information Criteria (AIC) and Chi-square significance tests, we construct a parsimonious binary logit model of individual student performance in the IS course and examine the effects of the administratively controllable factors on student performance. We find that the class size and the assignment of instructors to teach the course significantly impact student performance while modality of delivery does not. We offer and evaluate administrative policy recommendations likely to improve student performances.