Ashlea Bennett Milburn

Operations Research Applications in Home Healthcare

The home health care industry is an important component of health care systems that have the potential to lower the system-wide costs of delivering care, and free capacity in overcrowded acute care settings such as hospitals. Demand is doubling, but resources are scarce. A nursing shortage and near-zero profit margins hinder the ability of home care agencies to meet the increasing patient demand. The effective utilization of resources is vital to the continued availability of home care services. There is tremendous opportunity for the operations research community to address the challenges faced by home care agencies to improve their ability to meet as much patient demand as possible. This chapter describes tactical and operational planning problems arising in home health care, and discusses alternative configurations of home health supply chains. Formulations for home health nurse districting, home health nurse routing and scheduling, and home health supply chain problems are presented, and the relevant literature is reviewed. Recent developments in remote monitoring technologies that could change the home health care landscape are discussed, and future research directions are proposed.

A constraint programming approach for the team orienteering problem with time windows

We propose a constraint programming (CP) model to solve the TOPTW.Our model obtains the best known solutions for 122 out of 304 benchmark instances.It also finds 49 optimal solutions out of 66 known optimal solutions.It improves the solution for 1 instances and proves 2 new optimal solutions. The team orienteering problem with time windows (TOPTW) is a NP-hard combinatorial optimization problem. It has many real-world applications, for example, routing technicians and disaster relief routing. In the TOPTW, a set of locations is given. For each, the profit, service time and time window are known. A fleet of homogenous vehicles are available for visiting locations and collecting their associated profits. Each vehicle is constrained by a maximum tour duration. The problem is to plan a set of vehicle routes that begin and end at a depot, visit each location no more than once by incorporating time window constraints. The objective is to maximize the profit collected. In this study we discuss how to use constraint programming (CP) to formulate and solve TOPTW by applying interval variables, global constraints and domain filtering algorithms. We propose a CP model and two branching strategies for the TOPTW. The approach finds 119 of the best-known solutions for 304 TOPTW benchmark instances from the literature. Moreover, the proposed method finds one new best-known solution for TOPTW benchmark instances and proves the optimality of the best-known solutions for two additional instances.

An improved multi-directional local search algorithm for the multi-objective consistent vehicle routing problem

ABSTRACT This article presents a multi-objective variant of the Consistent Vehicle Routing Problem (MoConVRP). Instead of modeling consistency considerations such as driver consistency and time consistency as constraints as in the majority of the ConVRP literature, they are included as objectives. Furthermore, instead of formulating a single weighted objective that relies on specifying relative priorities among objectives, an approach to approximate the Pareto frontier is developed. Specifically, an improved version of multi-directional local search (MDLS) is developed. The updated algorithm, IMDLS, makes use of large neighborhood search to find solutions that are improved according to at least one objective to add to the set of nondominated solutions at each iteration. The performance of IMDLS is compared with MDLS and five other multi-objective algorithms on a set of ConVRP test instances from the literature. The computational study validates the competitive performance of IMDLS. Furthermore, results of the computational study suggest that pursuing the best compromise solution among all three objectives may increase travel costs by about 5% while improving driver and time consistency by approximately 60% and over 75% on average, when compared with a compromise solution having lowest overall travel distance. Supplementary materials are available for this article. Go to the publishes online edition of IIE Transactions for datasets, additional tables, detailed proofs, etc.

The Traveling Salesman Problem with Imperfect Information with Application in Disaster Relief Tour Planning

Many in the disaster response community have begun to explore ways to use information posted on social media platforms to identify a larger set of needs in a shorter amount of time following a disaster. However, needs communicated through social media platforms have initially not been verified so many within the emergency response community remain skeptical over the usefulness of such information. Consequently, as emergency managers consider whether to incorporate social media data in disaster planning efforts, a key tradeoff must be assessed. Confidence in the accuracy of needs to which resources are allocated is increased when information discovered on social media is ignored, but there is potential to leave populations that have not yet been discovered through traditional means unassisted. This paper introduces a new problem framework that describes a formal method for quantitatively assessing the impact of including unverified information in disaster relief planning. The usefulness of the framework is demonstrated in the context of the traveling salesman problem. A decision approach that considers social media information is compared to one that does not on the basis of total response time of resulting tours. A case study that considers variations in report accuracy and quantity for uniformly distributed demand instances is presented.

Multi-objective home health nurse routing with remote monitoring devices

A multi-objective home health nurse routing and scheduling problem variant with the option of assigning some patient visits to remote monitoring devices is defined. A metaheuristic solution approach that approximates the Pareto optimal frontier for travel cost, nurse consistency and balanced workload objectives is developed. This set of objectives represents possibly conflicting interests – those of the agency, patients, and nurse workforce. A computational study is conducted to determine the tradeoffs among these objectives when creating nurse routes and schedules.

The value of remote monitoring systems for treatment of chronic disease

Caring for patients with chronic illnesses is costly—75% of U.S. healthcare spending can be attributed to treating chronic conditions (CDC, 2009a,b). Several components contribute to the cost of treating chronic disease. There are the direct costs associated with treating the disease, and those associated with complications that arise as a result of the disease. There are also indirect costs associated with loss of productivity and quality of life. Technological advances in remote monitoring systems (RMS) may provide a more cost-effective and less labor-intensive way to manage chronic disease by focusing on preventive measures and continuous monitoring instead of emergency care and hospital admissions. In this paper, we develop a model that estimates the total potential savings associated with broad introduction of RMS, and considers how capacity constraints and fairness concerns should impact RMS allocation to target populations. To illustrate the value and insight the model may provide, we conduct a small computational study that focuses on direct costs that would be real costs to a healthcare provider or payer for a subset of the most common chronic diseases: diabetes, heart failure, and hypertension. The computational study shows that, under reasonable assumptions, broad introduction of RMS will lead to substantial cost savings for target populations. The study provides proof of concept that the model could serve as a useful tool for policy makers, as it allows a decision maker to modify cost, risk, and capacity parameters to determine reasonable policies for the allocation of and reimbursement for RMS.

A general framework for assessing the value of social data for disaster response logistics planning

Abstract Social media may play a critical role in disaster response by identifying needs in a shorter amount of time and thereby improving situational awareness. However, needs identified through social media initially have not been verified and some may be inaccurate. This can create a barrier to its use during disaster response decision making. Consequently, a key tradeoff between the timeliness and accuracy of social data for disaster response logistics planning must be assessed. This study aims to stimulate interest in a research agenda aimed at evaluating this tradeoff. A general framework for investigating whether it is worthwhile to act on user-generated data prior to its absolute verification in the context of disaster response logistics planning is presented. While the framework is applicable to a variety of logistics planning problems, this paper demonstrates its use via an application in mobile delivery of disaster response commodities. A case study motivated by the 2010 Haiti earthquake is developed using real social data, and is presented for use in conjunction with the framework. The types of insights that are possible via the framework are revealed through the small computational study included in the paper.

Case Article—Growing Pains: A Case Study for Large-Scale Vehicle Routing

This is a case study focused on the quantitative modeling of transportation services for use in undergraduate and graduate level transportation and logistics or optimization courses. The problem considered in the case is a capacitated vehicle routing problem CVRP with additional side constraints corresponding to delivery windows and United States Department of Transportation drive and duty time regulations. The case is presented in the context of a fictional company, Northeastern Home Goods NHG, but is based on a problem encountered by a real organization. NHG is considering outsourcing its transportation services to a firm having logistics as a core competency. Specifically, NHG wishes to evaluate the transportation costs that will result if a single distribution center is used to serve all of NHGs current stores according to a preexisting store delivery schedule. The primary objective of the case is to provide students with hands-on experience developing and applying solution techniques for a large unstructured vehicle routing problem. Secondary objectives include requiring students to think logically through the process of creating good solutions, promoting continuous improvement, and encouraging students to consider the business implications of their recommendations.

A study of home telehealth diffusion among US home healthcare agencies using system dynamics

ABSTRACT Home telehealth is a type of telemedicine technology that enables the collection and remote transmission of health data from a patient to a healthcare provider. It enables healthcare professionals to remotely monitor the health progress of patients, especially those with chronic illnesses, on a daily basis. By utilizing home telehealth, home healthcare agencies can provide better chronic care while reducing costs. Furthermore, the use of home telehealth has been shown to decrease the utilization of more acute care services, such as hospitalizations and emergency department visits. Hence, a widespread adoption of HT technologies holds great potential for the current US healthcare system. In this study, a Bass diffusion model is used to understand the diffusion of home telehealth among home healthcare agencies in the US over time. The diffusion model is embedded within a system dynamics model to study how home telehealth will impact the long-term utilization of services in the US healthcare system. The potential benefits of home telehealth over a ten-year horizon are projected in a computational study. Results indicate significant cost savings in even the most conservative test instances studied.

Characterizing the Home Health Care Supply Chain

Many home health agencies report low direct supply costs, but the total cost of the home health care supply chain may be much higher when indirect costs associated with ordering, storing, handling, and delivering supplies are considered. Results of a 2010 survey of home health agencies aimed at characterizing the home health care supply chain are presented, and statistical analysis is used to identify patterns based on agency affiliation, for profit status, and patient volume. Findings of interest to home health executives indicate the supply chain is performing well with respect to supply lead time and availability in patient homes. However, frequent practices reported may be leading to inefficiencies and high levels of nurse involvement in nonclinical duties such as ordering, sorting, and picking supplies.