When in a drug epidemic should the policy objective switch from use reduction to harm reduction?

A heated debate in drug policy concerns the relative merits of “harm reduction” (e.g., reducing drug-related HIV/AIDS transmission) vs. “use reduction” (controlling drug use per se). This paper models whether shifting emphasis between these goals over the course of a drug epidemic might reduce social costs relative to pursuing one or the other exclusively. Results suggest different answers for different drugs and/or countries. In particular, harm reduction may have always been effective for Australia’s injection drug use problem, but for US cocaine it may not have been in the past even if it could be so today. In certain circumstances harm reduction may “tip” an epidemic toward a high- rather than low-use equilibrium. The location in state space of regions where this occurs can be sensitive to parameter changes, suggesting caution may be in order when advocating harm reduction, unless there is confidence the epidemic has been modeled and parameterized accurately.     

Estimating the population utility function: A parametric Bayesian approach

In this paper we consider the health utility index mark II for quantifying and describing a population’s health related quality of life over health states composed of multiple attributes. This measure can be used for various purposes such as evaluating the severity of the effect of a disease or comparing different treatment methods. We present a Bayesian framework for population utility estimation and health policy evaluation by introducing a probabilistic interpretation of the multi-attribute utility theory (MAUT) used in health economics. In doing so, our approach combines ideas from the MAUT and Bayesian statistics and provides an alternative method of modeling preferences and utility estimation.     

Integrated fleet mix and routing decision for hazmat transportation: A developing country perspective

In developing countries, truck purchase cost is the dominant criteria for fleet acquisition-related decisions. However, we contend that other cost factors such as loss due to the number of en route truck stoppages based on a truck type and recovery cost associated with a route choice decision, should also be considered for deciding the fleet mix and minimizing the overall costs for long-haul shipments. The resulting non-linear model, with integer variables for the number and type of trucks, and the route choices, is solved via genetic algorithm. Using real data from a bulk liquid hazmat transporter, the trade-offs between the cost of travel, loss due to number of truck stoppages, and the long-term recovery cost associated with the risk of exposure due to a hazmat carrier accident are discussed. The numerical experiments show that when factors related to public safety and truck stoppages are taken into account for transportation, the lowest total cost and optimal route choice do not align with the cheapest truck type option; rather, the optimal solution corresponds to another truck type and route with total costs significantly less than the total costs associated with the cheapest truck type. Our model challenges the current truck purchasing strategy adopted in developing countries using the cheapest truck criteria.     

Linking stakeholder visions with resource allocation scenarios and multi-criteria assessment

Stakeholders and decision makers often develop visions of the ideal-type future as a response to complex societal problems and design their actions accordingly. However, these actors sometimes have a limited understanding as to whether their visions are feasible, what action is required and what the potential consequences are. This paper presents a methodology for linking visions with quantitative resource allocation scenarios which show different options in implementing the visions. The consequences are then appraised by multi-criteria assessment in order to find optimal and acceptable ways of implementation. As a result, stakeholders and decision makers learn about their visions and may even rethink them before decision making. The methodology thus couples visionary ideas with analytical information, providing a novel approach using quantitative techniques in a soft framework. The methodology is illustrated via a real-world case study concerning the future energy system in a small Swiss community.     

Solving the semi-desirable facility location problem using bi-objective particle swarm

In this paper, a new model for the semi-obnoxious facility location problem is introduced. The new model is composed of a weighted minisum function to represent the transportation costs and a distance-based piecewise function to represent the obnoxious effects of the facility. A single-objective particle swarm optimizer (PSO) and a bi-objective PSO are devised to solve the problem. Results are compared on a suite of test problems and show that the bi-objective PSO produces a diverse set of non-dominated solutions more efficiently than the single-objective PSO and is competitive with the best results from the literature. Computational complexity analysis estimates only a linear increase in effort with problem size.     

Recent optimization models and trends in location,relocation, and dispatching of emergency medical vehicles

Over the past 10 years, a considerable amount of research has been devoted to the development of models to support decision making in the particular yet important context of Emergency Medical Services (EMS). More specifically, the need for advanced strategies to take into account the uncertainty and dynamism inherent to EMS, as well as the pertinence of socially oriented objectives, such as equity, and patient medical outcomes, have brought new and exciting challenges to the field. In this context, this paper summarizes and discusses modern modeling approaches to address problems related to ambulance fleet management, particularly those related to vehicle location and relocation, as well as dispatching decisions. Although it reviews early works on static ambulance location problems, this review concentrates on recent approaches to address tactical and operational decisions, and the interaction between these two types of decisions. Finally, it concludes on the current state of the art and identifies promising research avenues in the field.     

A multicriteria approach to sustainable energy supply for the rural poor

Despite significant progress in energy technology, about two billion people worldwide, particularly the poor in rural areas of developing countries, have no access to electricity. Decision-making concerning the most appropriate energy technology for supplying these areas has been difficult; existing energy decision-support tools have been useful but are mostly incomplete. Trade-offs, as well as impacts that can be positive or negative, may emerge as a result of implementing modern forms of energy. These can affect both community’s livelihoods as well as the confidence of decision-makers in relation to alternative technologies. The paper discusses a newly designed multicriteria approach and its novel robustness analysis for selecting energy generation systems for the improvement of livelihoods in rural areas. The proposed methodology builds upon a sustainable rural livelihoods framework to address multiple interactions and calculate trade-offs aimed at boosting decision-makers’ confidence in the selected technologies. The methodology is tested via a case study in Colombia.     

Stochastic models for strategic resource allocation in nonprofit foreclosed housing acquisitions

Increased rates of mortgage foreclosures in the U.S. have had devastating social and economic impacts during and after the 2008 financial crisis. As part of the response to this problem, nonprofit organizations such as community development corporations (CDCs) have been trying to mitigate the negative impacts of mortgage foreclosures by acquiring and redeveloping foreclosed properties. We consider the strategic resource allocation decisions for these organizations which involve budget allocations to different neighborhoods under cost and return uncertainty. Based on interactions with a CDC, we develop stochastic integer programming based frameworks for this decision problem, and assess the practical value of the models by using real-world data. Both policy-related and computational analyses are performed, and several insights such as the trade-offs between different objectives, and the efficiency of different solution approaches are presented.     

Actor analysis methods and their use for public policy analysts

Public policy analysts use methods rooted in OR and systems analysis to support policy makers in their judgement. In doing so, most policy analysts recognize the value of a certain understanding of the role of actors in policy making processes. Different methods are available to aid such understanding and, although they all focus on actors, there are important differences between them. Insight into the range of available methods and their characteristics will thus help policy analysts to learn more about the potential and limitations involved in analyzing multi-actor processes. This article provides such an overview, based on the main requirements these methods should meet. This overview is used to discuss some of the implications for policy analysts who are interested in analyzing multi-actor processes, focusing specifically on trade-offs between analytic quality and practical usability.     

Quantitative models for sustainable supply chain management: Developments and directions

Sustainability, the consideration of environmental factors and social aspects, in supply chain management (SCM) has become a highly relevant topic for researchers and practitioners. The application of operations research methods and related models, i.e. formal modeling, for closed-loop SCM and reverse logistics has been effectively reviewed in previously published research. This situation is in contrast to the understanding and review of mathematical models that focus on environmental or social factors in forward supply chains (SC), which has seen less investigation. To evaluate developments and directions of this research area, this paper provides a content analysis of 134 carefully identified papers on quantitative, formal models that address sustainability aspects in the forward SC. It was found that a preponderance of the publications and models appeared in a limited set of six journals, and most were analytically based with a focus on multiple criteria decision making. The tools most often used comprise the analytical hierarchy process or its close relative, the analytical network process, as well as life cycle analysis. Conclusions are drawn showing that numerous possibilities and insights can be gained from expanding the types of tools and factors considered in formal modeling efforts.     

Soft OR in China: A critical report

As China’s reform steps into the ‘deep water zone’ where value complexity becomes paramount, general-purpose decision-making aids such as Operational Research (OR) are increasingly confronted with the challenge of dealing with interest conflicts. However, due to historical events and institutional circumstances, OR in China to date is largely constrained by a technocratic approach which is not fit for purpose. Encouragingly, recent OR innovations inside China signify a conscious move to embrace value plurality and tackle social conflicts. OR is not merely a neutral tool for solving technical problems, but a world-building discourse that shapes society. The future of OR, particularly Soft OR, in China will be determined by whether OR workers are willing and capable to act as institutional entrepreneurs promoting scientific and democratic decision-making that deepens the reform toward an open, just and prosperous society. The implications go beyond the OR community and China’s borders.     

Practical aspects associated with location planning for maternal and perinatal assistance in Brazil

A study has been carried out aimed at reducing perinatal mortality in the municipality of Rio de Janeiro through a better distribution of health care facilities. The algorithmic aspects are detailed elsewhere and here the emphasis is on practical issues and difficulties encountered. A 3-level hierarchical model was developed. Both uncapacitated and capacitated versions are briefly described together with some results based on actual data. The project brought to light many contradictions between OR theory and practice in developing countries and, unfortunately, the models developed were not implemented by the municipality health authorities. Possible reasons for this outcome are analysed.     

Introducing competition in healthcare services: The role of private care and increased patient mobility

We study the operational implications from competition in the provision of healthcare services, in the context of national public healthcare systems in Europe. Specifically, we study the potential impact of two alternative ways through which policy makers have introduced such competition: (i) via the introduction of private hospitals to operate alongside public hospitals and (ii) via the introduction of increased patient choice to grant European patients the freedom to choose the country they receive treatment at. We use a game-theoretic framework with a queueing component to capture the interactions among the patients, the hospitals and the healthcare funders. Specifically, we analyze two different sequential games and obtain closed form expressions for the patients’ waiting time and the funders’ reimbursement cost in equilibrium. We show that the presence of a private provider can be beneficial to the public system: the patients’ waiting time will decrease and the funders’ cost can decrease under certain conditions. Also, we show that the cross-border healthcare policy, which increases patient mobility, can also be beneficial to the public systems: when welfare requirements across countries are sufficiently close, all funders can reduce their costs without increasing the patients’ waiting time. Our analysis implies that in border regions, where the cost of crossing the border is low, “outsourcing” the high-cost country’s elective care services to the low-cost country is a viable strategy from which both countries’ systems can benefit.     

An empirical method for assessing the research relevance gap

There has been much debate on the relevance to firms of the academic research produced by business schools. However, what has not received as much attention is how the relevance of the research to businesses should be measured in a systematic and empirical way. We develop a systematic method to test for the relevance of academic research to businesses. Our method models as a vector autoregressive process the interests of the academic and practitioner communities in some new topic, as expressed by the number of articles published in the academic and the practitioner literature on that topic per calendar quarter, and then studies Granger causality between the academic and practitioner interest processes. This method can be used by academics to empirically demonstrate the impact of their intellectual contributions on practitioners and thence on the business world. We employ our approach to two relatively new and important topics, Real Options and Economic Value Added.     

Optimizing the marriage market: An application of the linear assignment model

Research shows that the success of marriages and other intimate partnerships depends on objective attributes such as differences in age, cultural background, and educational level. This article proposes a mathematical approach to optimizing marriage by allocating spouses in such a way as to reduce the likelihood of divorce or separation. To produce our optimization model, we use the assumption of a central “agency” that would coordinate the matching of couples. Based on a representative and longitudinal sample of 1074 cohabiting and married couples living in Switzerland, we estimate various objective functions corresponding to age, education, ethnicity, and prior divorce concerning every possible combination of men and women. Our results show that the current state of marriages or partnerships is well below the social optimum. We reallocate approximately 68% of individuals (7 out of 10) to a new couple that we posit has a higher likelihood of survival. From this selection of new partners, we obtain our final “optimal” solutions, with a 21% reduction in the objective function.     

Technology adoption with limited foresight and uncertain technological learning

Most previous optimization models on technology adoption assume perfect foresight over the long term. In reality, decision-makers do not have perfect foresight, and the endogenous driving force of technology adoption is uncertain. With a stylized optimization model, this paper explores the adoption of a new technology, its associated cost dynamics, and technological bifurcations with limited foresight and uncertain technological learning. The study shows that when modeling with limited foresight and technological learning, (1) the longer the length of the decision period, the earlier the adoption of a new technology, and the value of a foresight can be amplified with a high learning rate. However, when the decision period is beyond a certain length, further extending its length has little influence on adopting the new technology; (2) with limited foresight, decisions aiming at minimizing the total cost of each decision period will commonly result in a non-optimal solution from the perspective of the entire decision horizon; and (3) the range of technological bifurcation is much larger than that with perfect foresight, but uncertainty in technological learning tends to reduce the range by removing the early adoption paths of a new technology.     

Analysis of the third European survey on working conditions with composite indicators

A composite indicator Working conditions for comparing European countries is constructed from data of the Third European Survey on Working Conditions. The main findings are as follows: (a) European countries differ with respect to working conditions statistically more significantly than with respect to earnings; it implies a quite accurate discrimination threshold in ranking countries with respect to working conditions, (b) working conditions and earnings positively correlate over the whole of Europe but correlate little within single countries; it indicates at the prevailing role of national determinants over professional or social specificities as contributing to the average working conditions, and (c) earnings play no essential role in subjective estimations, including job satisfaction, which mainly depends on working conditions; consequently, more attention should be paid to improving the latter.     

Donation management for menu planning at soup kitchens

The food industry is confronted with a pressure to reduce waste and to make agreements on donating surplus food to charitable organizations. Charitable organizations such as food banks and soup kitchens can use these donations in preparing food parcels or meals for their clients. For soup kitchens, donation management is strongly influencing menu planning, and conversely, menu planning considerations have a strong impact on donation management decisions. To make the best use of (mostly highly perishable) food donations, we develop an MILP model for integrated donation management and menu planning that proposes a menu plan and suggests which (part of the) donations to accept. The combination of menu planning and donation management is essential for soup kitchens, but has not been studied before.The model is used to assess the impact of contracts on a strategic or tactical level, and captures operational decision making due to the integration of donation management and menu planning. To deal with meal variety considerations and to resemble planning practices, the developed model is solved in a rolling horizon. The results show that (i) the use of donations reduces overall costs for the soup kitchen; (ii) despite the short shelf life of donations, most donations can be used efficiently; and (iii) meal variety can be easily ensured and food donations increase this variety. In addition to the benefits for soup kitchens, the approach has implications for waste reduction in food supply chains, by structural/contractual donations of surplus food by retailers.     

How do we know who to include in collaborative research? Toward a method for the identification of experts

Collaborative research, defined as research involving actors participating in the problem situation under study, has an important role in operational research, strategic management and systems thinking. In a recent study, we found that a strong organizational focus incorporated into many soft operational research (OR) approaches is inadequate for studying societal problem situations, which are fragmented and have no clear boundary. Specifically, we failed to find a process of identifying individuals that is capable of representing the perspectives of actors and sufficient for research into societal problem situations. We found no clear terminology accounting for ontological differences between actors, individuals representing them and conceptual representations of acting entities. In response to this gap in the literature, we propose terminology that differentiates among actors (individuals or collective entities in the real world), experts (individuals capable of representing the perspective of an actor) and agents (ideal-typical representations of actors). Based on this terminology, we propose an iterative method to guide the assembly of an expert group to undertake collaborative research into societal problem situations. To demonstrate the application of our method, we present selected insights from our study in an electronic supplement.