A Spatial Decision Support System Approach to Evaluate the Efficiency of a Meals-on-Wheels Program∗

Wong, David W. S.; Meyer, Judith W.

doi:10.1111/j.0033-0124.1993.00332.x

Cited by 23 publications

(14 citation statements)

References 10 publications

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“…Most early health care applications worked with relatively simple objective functions, such as minimizing average distance or maximizing population coverage, and these considerations remain important, especially in developing countries where use of health services varies strongly with distance (33,46,64). More recently, optimization models have been used for vehicle routing problems-to identify shortest-path routes for Meals on Wheels programs-and in vehicle-based ambulance routing systems (69). These systems integrate the latest geographic information technologies.…”

Section: Locating Health Servicesmentioning

confidence: 99%

“…It is also straightforward to incorporate differences in mobility and transportation access among population groups, but few published location-allocation studies appear to have done this. Using tools readily available in GIS, analysts can better represent geographical context in identifying optimal health care locations, and they can visualize and explore model results (57,69).…”

Section: Locating Health Servicesmentioning

confidence: 99%

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GIS and Health Care

McLafferty

2003

Annu. Rev. Public Health

404

278

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GIS and related spatial analysis methods provide a set of tools for describing and understanding the changing spatial organization of health care, for examining its relationship to health outcomes and access, and for exploring how the delivery of health care can be improved. This review discusses recent literature on GIS and health care. It considers the use of GIS in analyzing health care need, access, and utilization; in planning and evaluating service locations; and in spatial decision support for health care delivery. The adoption of GIS by health care researchers and policymakers will depend on access to integrated spatial data on health services utilization and outcomes and data that cut across human service systems. We also need to understand better the spatial behaviors of health care providers and consumers in the rapidly changing health care landscape and how geographic information affects these dynamic relationships.

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Section: Locating Health Servicesmentioning

confidence: 99%

Section: Locating Health Servicesmentioning

confidence: 99%

GIS and Health Care

McLafferty

2003

Annu. Rev. Public Health

404

278

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show abstract

“…Some research on nonprofit applications have similar additive objective functions that maximize utility/service or minimize costs, see Chou et al (2008), Wong and Meyer (1993) and Johnson et al (2005). However, the majority of operations research literature in nonprofit settings incorporate equity as an objective.…”

Section: Literature Reviewmentioning

confidence: 99%

Sequential Resource Allocation for Nonprofit Operations

Lien

Iravani

Smilowitz

2014

Operations Research

117

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This paper studies a sequential resource allocation problem motivated by distribution operations of a nonprofit organization. The alternate objectives that arise in nonprofit, as opposed to commercial, operations lead to new variations on traditional problems in operations research and inventory management. Specifically, we consider the problem of distributing a scarce resource to meet sequentially observed customer demand. In a commercial setting, the amount distributed to each customer is determined to maximize profit; however, this objective may lead to inequitable distributions among customers. Our work in a nonprofit setting solves the sequential resource allocation problem with an objective function aimed at equitable and sustainable service. We define service in terms of fill rate (the ratio of the allocated amount to observed demand) and develop an objective function to maximize the expected minimum fill rate among customers. Through a dynamic programming framework, we characterize the structure of the optimal allocation policy for a given sequence of customers. In addition, we address customer visitation sequencing by identifying properties to consider in sequencing decisions to optimize the objective. For both inventory allocation and customer sequencing decisions, we develop heuristic methods which yield near-optimal solutions.

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“…In fact, there is a need for quantitative analysis on a variety of levels, for a variety of purposes (Johnson 2000b). Spatial decision support systems can consolidate a large volume of spatial, demographic, housing and labor market data and provide PHA planners with the ability to perform con®rmatory and exploratory data analysis, operations research/management science-based decision modeling and visualization of optimization model outputs (see Wong and Meyer 1993 for an overview of SDSS capabilities for location modeling). HLP is seen as a ®rst step in ful®ll-ing the potential of SDSSs to improve PHA planning and decisionmaking.…”

Section: Introductionmentioning

confidence: 99%

A spatial decision support system prototype for housing mobility program planning

Johnson¹

2001

J Geograph Syst

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This paper presents a GIS-based decision support system prototype intended for use by public housing authority (PHA) administrators and planners designing policy for housing mobility programs. Housing mobility programs enable low-income families, many of whom live in governmentoperated public housing, to move to more desirable private-market rentals via rent subsidies. Unfortunately, housing authority planners have limited ability to visualize alternative relocation schemes of cohorts of low-income families or the impacts associated with these relocation policies. Thus, they are often not able to give highest-quality advice to clients regarding places to search for private-market rental housing. Housing Location Planner assists PHA planners in three ways: it analyzes spatial, demographic and housing market characteristics of the study area, selects certain portions of the study area for input to an optimization model which generates alternative family allocations, and displays optimization model results in a way that links decision variable values and objective function values. Housing Location Planner is seen as a ®rst step in the development of even more sophisticated multi-stakeholder spatial decision support systems for subsidized housing planning in which one or more alternative allocations of families across a study area is chosen as a basis for policy initiatives.

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A Spatial Decision Support System Approach to Evaluate the Efficiency of a Meals-on-Wheels Program∗

Cited by 23 publications

References 10 publications

GIS and Health Care

GIS and Health Care

Sequential Resource Allocation for Nonprofit Operations

A spatial decision support system prototype for housing mobility program planning

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