Timothy L. Urban’s research while affiliated with University of Tulsa and other places

The asymmetric-dominance effect occurs when consumers change their preferences between products when another (a decoy) is added to the assortment. This phenomenon has been extensively studied in the consumer-behavior/marketing research literature. However, despite its direct relevance to consumer product distribution, the effect this has on a firm’s inventories has not been studied. This paper presents a model that integrates a consumer-preference model to account for the decoy’s effect on the relative market share of the existing products with a multi-item, single-period inventory model that accounts for product substitution resulting from the addition of the decoy. The ensuing inventory levels and profit contribution are investigated for a variety of situations, including competitive vs. cooperative scenarios as well as the effect of double decoys and offsetting a competitor’s decoy with a firm’s own decoy. We find that adding a decoy to a firm’s assortment can result in a substantial gain in profits—accomplished with small inventories of decoys—due to the demand drawn from the competitor’s product. The loss realized from a competitor’s decoy can be offset by a firm’s own decoy, and a firm may further improve their profits with a double decoy, albeit with diminishing returns.

Data availability enables clinics to use predictive analytics to improve appointment scheduling and overbooking decisions based on the predicted likelihood of patients missing their appointment (no-shows). Analyzing data using machine learning can uncover hidden patterns and provide valuable business insights to devise new business models to better meet consumers’ needs and seek a competitive advantage in healthcare. The innovative application of machine learning and analytics can significantly increase the operational efficiency of online scheduling. This study offers an intelligent, yet explainable, analytics framework in scheduling systems for primary-care clinics considering individual patients’ no-show rates that may vary for each appointment day and time while generating appointment and overbooking decisions. We use the predicted individual no-show rates in two ways: (1) a probability-based greedy approach to schedule patients in time slots with the lowest no-show likelihood, and (2) marginal analysis to identify the number of overbookings based on the no-show probabilities of the regularly-scheduled patients. We find that the summary measures of profit and cost are considerably improved with the proposed scheduling approach as well as an increase in the number of patients served due to a substantial decrease in the no-show rate. Sensitivity analysis confirms the effectiveness of the proposed dynamic scheduling framework even further.

As part of the initiative to prevent the spread of the novel coronavirus (COVID–19), many retailers implemented one-way aisles in their stores. Moreover, the retailing research literature has shown a significant positive relationship between the distance that shoppers travel within the store and their resulting unplanned purchases. To evaluate the effect that one-way aisles have on the amount of traffic flow in the store, we use the traveling salesperson problem to determine the increase in distance traveled as well as the increase in the area within the store that is covered by the shopper. Overall, our results indicate that shoppers may travel 50 percent further with one-way traffic and cover an additional 67 percent of the store area, a significant increase in the amount of product and in-store stimuli exposed to the customer. We also present other advantages and disadvantages of the continued use of one-way aisles after the pandemic subsides.

In this paper, we examine a vehicle routing problem with a makespan objective incorporating both stochastic and correlated travel times, which is usually not considered in routing problems. As an alternative to simulation, we develop an approach based on extreme-value theory to estimate the expected makespan (and standard deviation) and show how this approach can be embedded within an existing routing heuristic. We present results that demonstrate the impact of different correlation patterns and levels of correlation on route planning using real-world motivated instances. Depending on the particular objective, cost savings of up to 13.76% can be obtained by considering correlation.

Selective assembly is an approach in which high-precision assemblies can be produced from relatively low-precision components or subassemblies. This research investigates component ordering policies for fixed-bin selective-assembly processes that consider the stochastic nature of the binning process as well as stochastic demand. The distributional aspects of the assembly process are identified, and an approximation of the number of assemblies completed is provided utilising extreme-value theory. The order quantity can then be determined to meet demand with a given service level; an implicit-enumeration procedure is presented to illustrate this process. Computational results illustrate that this is an effective approach for controlling component inventories.