Honora Smith

Locational analysis: highlights of growth to maturity

Locational analysis has grown to maturity over the last decades, from its earliest roots, to fruitfulness in a wide-ranging number of strands that join with other disciplines and applications such as environmental planning and supply chain management. This paper charts the progress of location theory in three stages: a period of early contributions, when a number of seminal geometrical and geographical problems were studied; a ‘coming of age’ with the development of defining or classical problems that have proved fundamental to much later research and a third period of new models and new applications.

OR in developing countries: A review

The relevance of Operational Research (OR) in developing countries has increasingly engaged the attention of operational researchers in both the industrialised and less developed countries over the last 50 years. With this, there has been a considerable amount of interest in the potential for using OR in developing countries. One sign of this is the emergence of a number of initiatives to promote OR in developing countries and the number of new societies for OR that have emerged from the developing world. This paper is an attempt at providing an overall picture of the state of OR in the developing countries. In particular, it will look at the coverage in terms of countries and methods. It will also highlight the contribution OR is making towards the theme of poverty, the reduction of which is regarded as the key focus of development policy interventions as reflected in the Millennium Development Goals (MDGs).

Planning sustainable community health schemes in rural areas of developing countries

In this research, we consider the planning of community health schemes by non-governmental or faith-based organisations in rural areas of developing countries, from both top-down and ground level viewpoints. We conclude that both types of planning approach are valid and necessary for sustainability of such developments. With top-down planning in mind, we describe our hierarchical models especially designed for location of community health facilities, with objectives pertaining to both efficiency and equity of provision. As an additional case study, we present modelling of the location of a maximal number of self-sustainable primary healthcare workers in a rural region of India.

A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making

This paper presents a structured review of the literature on quantitative modelling for the blood product supply chain. This is a widely researched topic, dating back to the 1960s, and several other reviews have been published over the years. However, this paper presents new relevant information for researchers, not only by including more recent models but chiefly because of the structured way in which the models are presented. The models are broken down into five categories. The first four categories represent the four stages (echelons) in the supply chain: collection, production, inventory and delivery. The final category contains ‘integrated’ models which cover more than one stage. Each section (other than integrated models, which are treated slightly differently) contains two distinct elements. The first element is a diagrammatic representation of decisions and relationships, broken down by hierarchy level (strategic – tactical – operational). The second element is a text description of the main features, contributions and gaps found in the analysed models. An additional element for each section is available online, namely a searchable table describing specific features of each echelon, together with a taxonomic key to assist the reader.

Bicriteria Efficiency/Equity Hierarchical Location Models for Public Service Application

As is often the case in healthcare provision, public services may offer facilities at a hierarchy of levels in different locations, ranging from basic to specialised levels of care. In addition to efficiency objectives, with public services there is the concern of equity of provision when locating new facilities. We present, as a tool-kit for decision makers, a range of discrete hierarchical location models with bicriteria efficiency/equity objectives. These models are for use in location of facilities within hierarchical systems where a fair but efficient hierarchical service is sought. The hierarchical models have as efficiency criteria both p-median and maximal-covering types. These components are combined in a novel manner with appropriate equity objectives to give decision makers a range of choices of scenarios. We illustrate use of the models in a healthcare setting.

Dynamic pricing of airline tickets with competition

Competition has a huge influence on customer buying behaviour and will impact on the optimal price that companies should charge for goods or services. To date, many dynamic pricing models have not modelled competition explicitly. In this paper, we introduce pricing strategies that maximize revenue when selling an inventory of identical items by a fixed time and where there is a competing seller. The model used incorporates a probabilistic formulation of customer demand, which is influenced by the prices offered by the company and the competitor, and the time remaining until the end of the selling period. Calculus of variations is used to solve the problem and simple conditions are given that ensure the uniqueness of a solution. Illustrative examples are included. A practical implementation that uses dynamic updating is proposed and tested using simulated data, showing the effectiveness of the method.

Simulation-optimization model for production planning in the blood supply chain

Production planning in the blood supply chain is a challenging task. Many complex factors such as uncertain supply and demand, blood group proportions, shelf life constraints and different collection and production methods have to be taken into account, and thus advanced methodologies are required for decision making. This paper presents an integrated simulation-optimization model to support both strategic and operational decisions in production planning. Discrete-event simulation is used to represent the flows through the supply chain, incorporating collection, production, storing and distribution. On the other hand, an integer linear optimization model running over a rolling planning horizon is used to support daily decisions, such as the required number of donors, collection methods and production planning. This approach is evaluated using real data from a blood center in Colombia. The results show that, using the proposed model, key indicators such as shortages, outdated units, donors required and cost are improved.

Can you model growth of trust? A study of the sustainability of a rural community health centre in North India

Trust in the service provided by any health facility is of vital importance to its sustainability, whether it is a community clinic in a rural area of a developing country or an international telemedicine service. Community health centres can be used as a means of delivering highly accessible, low-cost health service in the developing world. A major strategic issue for planners of such centres is the expected level of uptake of services throughout a region and its effect on sustainability of any facility. In this study of a clinic in rural north India, Monte Carlo simulation is used in modelling the spatio-temporal spread of usage of the service. Trust in the provider is built both through word-of-mouth contacts and previous development activities: our study contributes to the literature with a practical application of trust modelling.

Whole blood or apheresis donations? A multi-objective stochastic optimization approach

Abstract In the blood supply chain, several alternative technologies are available for collection and processing. These technologies differ in cost and efficiency: for example, collection by apheresis requires very expensive machines but the yield of blood products is considerably greater than whole blood collection. Blood centre managers are faced with the difficult strategic problem of choosing the best combination of technologies, as well as the equally difficult operational problem of assigning donors to collection methods. These decisions are complex since so many factors have to be taken into account, including stochastic demand, blood group compatibilities, donor availability, the proportions of blood types in both donor and recipient populations, fixed and variable costs, and process efficiencies. The use of deterministic demand forecasts is rarely adequate and a robust decision must consider uncertainty and variability in demand as well as trade-offs between several potentially conflicting objectives. This paper presents a multi-objective stochastic integer linear programming model to support such decisions. The model treats demand as stochastic and seeks to optimize two objectives: the total cost and the number of donors required. To solve this problem, we apply a novel combination of Sample Average Approximation and the Augmented Epsilon-Constraint algorithm. This approach is illustrated using real data from Bogota, Colombia.

A tutorial on selecting and interpreting predictive models for ordinal health-related outcomes

Ordinal variables are very often objects of study in health sciences. However, due to the lack of dissemination of models suited for ordinal variables, users often adopt other practices that result in the loss of statistical power. In this tutorial, different models from the family of logistic regression models are introduced as alternatives to handle and interpret ordinal outcomes. The models that were considered include: ordinal regression model (ORM), continuation ratio model (CRM), adjacent category model (ACM), generalised ordered logit model, sequential model, multinomial logit model, partial proportional odds model, partial continuation ratio model and stereotype ordered regression model. By using the relationship of hospital length of stay in a public hospital in Mexico with patient characteristics as an example, the models were used to describe the nature of such relationship and to predict the length of stay category to which a patient is most likely to belong. After an initial analysis, the ORM, CRM and ACM proved to be unsuitable for our data due to the transgression of the parallel regression assumption. The rest of the models were estimated in STATA. The results suggested analogous directionality of the parameter estimates between models, although the interpretation of the odds ratios varied from one model to another. Performance measurements indicated that the models had similar prediction performance. Therefore, when there is an interest in exploiting the ordinal nature of an outcome, there is no reason to maintain practices that ignore such nature since the models discussed here proved to be computationally inexpensive and easy to estimate, analyse and interpret.