Edward A. Wasil’s research while affiliated with American Military University and other places

The Traveling Salesman Problem with a Drone (TSP-D) is a hybrid truck and drone model of delivery, in which the drone rides on the truck and launches from the truck to deliver packages. Our approach to the TSP-D uses branch and bound, whereby each node of the branch-and-bound tree corresponds with a potential order to deliver a subset of packages. An approximate lower bound at each node is given by solving a dynamic program. We provide additional variants of our heuristic approach and compare solution quality and computation times. Consideration is given to various input parameters and distance metrics. The online supplement is available at https://doi.org/10.1287/ijoc.2018.0826 .

The effect of patient movement between hospitals and long-term care facilities (LTCFs) on methicillin-resistant Staphylococcus aureus (MRSA) prevalence levels is unknown. We investigated these effects to identify scenarios that may lead to increased prevalence in either facility type. We used a hybrid simulation model to simulate MRSA transmission among hospitals and LTCFs. Transmission within each facility was determined by mathematical model equations. The model predicted the long-term prevalence of each facility and was used to assess the effects of facility size, patient turnover, and decolonization. Analyses of various healthcare networks suggest that the effect of patients moving from a LTCF to a hospital is negligible unless the patients are consistently admitted to the same unit. In such cases, MRSA prevalence can increase significantly regardless of the endemic level. Hospitals can cause sustained increases in prevalence when transferring patients to LTCFs, where the population size is smaller and patient turnover is less frequent. For 1 particular scenario, the steady-state prevalence of a LTCF increased from 6.9% to 9.4% to 13.8% when the transmission rate of the hospital increased from a low to a high transmission rate. These results suggest that the relative facility size and the patient discharge rate are 2 key factors that can lead to sustained increases in MRSA prevalence. Consequently, small facilities or those with low turnover rates are especially susceptible to sustaining increased prevalence levels, and they become more so when receiving patients from larger, high-prevalence facilities. Decolonization is an infection-control strategy that can mitigate these effects.

The multi-depot split delivery vehicle routing problem combines the split delivery vehicle routing problem and the multiple depot vehicle routing problem. We define this new problem and develop an integer programming-based heuristic for it. We apply our heuristic to 30 instances to determine the reduction in distance traveled that can be achieved by allowing split deliveries among vehicles based at the same depot and vehicles based at different depots. We generate new test instances with high-quality, visually estimated solutions and report results on these instances.

The vehicle routing problem (VRP) is a difficult and well-studied combinatorial optimization problem. We develop a parallel algorithm for the VRP that combines a heuristic local search improvement procedure with integer programming. We run our parallel algorithm with as many as 129 processors and are able to quickly find high-quality solutions to standard benchmark problems. We assess the impact of parallelism by analyzing our procedure's performance under a number of different scenarios.

Methicillin-resistant Staphylococcus aureus (MRSA) is a significant ongoing problem in health care, posing a substantial threat to hospitals and communities as well. Its spread among patients causes many downstream effects, such as a longer length of stay for patients, higher costs for hospitals and insurance companies, and fatalities. An agent-based simulation model is developed to investigate the dynamics of MRSA transmission within a hospital. The simulation model is used to examine the effectiveness of various infection control procedures and explore more specific questions relevant to hospital administrators and policy makers. Simulation experiments are performed to examine the effects of hand-hygiene compliance and efficacy, patient screening, decolonization, patient isolation, and health-care worker-to-patient ratios on the incidence of MRSA transmission and other relevant metrics. Experiments are conducted to investigate the dynamic between the number of colonizations directly attributable to nurses and physicians, including rogue health-care workers who practice poor hygiene. We begin to explore the most likely threats to trigger an outbreak in hospitals that practice high hand-hygiene compliance and additional preventive measures.

Utility companies typically send their meter readers out each day of the billing cycle in order to determine each customer's usage for the period. Customer churn requires the utility company to periodically remove some customer locations from its meter-reading routes. On the other hand, the addition of new customers and locations requires the utility company to add new stops to the existing routes. A utility that does not adjust its meter-reading routes over time can find itself with inefficient routes and, subsequently, higher meter-reading costs. Furthermore, the utility can end up with certain billing days that require substantially larger meter-reading resources than others. However, remedying this problem is not as simple as it may initially seem. Certain regulatory and customer service considerations can prevent the utility from shifting a customer's billing day by more than a few days in either direction. Thus, the problem of reducing the meter-reading costs and balancing the workload can become quite difficult. We describe this Balanced Billing Cycle Vehicle Routing Problem in more detail and develop an algorithm for providing solutions to a slightly simplified version of the problem. Our algorithm uses a combination of heuristics and integer programming via a three-stage algorithm. We discuss the performance of our procedure on a real-world data set. © 2009 Wiley Periodicals, Inc. NETWORKS, 2009

In the small package shipping industry (as in other industries), companies try to differentiate themselves by providing high levels of customer service. This can be accomplished in several ways, including online tracking of packages, ensuring on-time delivery, and offering residential pickups. Some companies want their drivers to develop relationships with customers on a route and have the same drivers visit the same customers at roughly the same time on each day that the customers need service. These service requirements, together with traditional constraints on vehicle capacity and route length, define a variant of the classical capacitated vehicle routing problem, which we call the consistent VRP (ConVRP). In this paper, we formulate the problem as a mixed-integer program and develop an algorithm to solve the ConVRP that is based on the record-to-record travel algorithm. We compare the performance of our algorithm to the optimal mixed-integer program solutions for a set of small problems and then apply our algorithm to five simulated data sets with 1,000 customers and a real-world data set with more than 3,700 customers. We provide a technique for generating ConVRP benchmark problems from vehicle routing problem instances given in the literature and provide our solutions to these instances. The solutions produced by our algorithm on all problems do a very good job of meeting customer service objectives with routes that have a low total travel time. In the paper “The Consistent Vehicle Routing Problem,” published in Manufacturing & Service Operations Management, ePub ahead of print December 4, 2008, http://dx.doi.org/10.1287/msom.1080.0243, the authors have amended the original text published online to correct an oversight in conveying the real-world problem studied in this article.

Over the last decade or so, the health-care industry in the United States, Canada, and around the world has started to solve important problems using traditional management science and operations research (MS/OR) models and methods, such as mathematical programming, simulation, queueing theory, and decision analysis. In recent years, the results of modeling efforts have been significant and have begun to positively affect the delivery of health care. This special issue documents eight successful applications of MS/OR methods to solving actual problems in health care.

In this chapter, we use genetic algorithms (GAs) to solve the generalized orienteering problem (GOP). In the orienteering problem (OP), we are given a transportation network in which a start point and an end point are specified, and other points have associated scores. Given a fixed amount of time, the goal is to determine a path from start to end through a subset of the other locations in order to maximize the total path score. In the GOP, each point has a score with respect to a number of attributes (e.g., natural beauty, historical significance, cultural and educational attractions, and business opportunities) and the overall objective function is nonlinear. The GOP is more difficult than the OP, which is itself NP-hard. An effective heuristic using artificial neural networks (ANNs), however, has been designed to solve the GOP. In this chapter, we show that a straightforward GA can yield comparable results.