A multiobjective optimization approach to smart growth in land development

In this paper we apply a multiobjective optimization model of Smart Growth to land development. The term Smart Growth is meant to describe development strategies—that do not promote urban sprawl. However, the term is somewhat open to interpretation. The multiobjective aspects arise when considering the conflicting interests of the various stakeholders involved in land development decisions: the government planner, the environmentalist, the conservationist, and the land developer. We present a formulation—employing linear and convex quadratic objective functions subject to polyhedral and binary constraints for the stakeholders. The resulting optimization problems are convex, quadratic mixed integer programs that are NP-complete. We report numerical results with this model for Montgomery County, Maryland, and present them using a geographic information system (GIS).     

Minimisation of uncertainty in decision-making processes using optimised probabilistic Fuzzy Cognitive Maps: A case study for a rural sector

Several studies have focused on methods of increasing system and uncertainty knowledge for socio-economic and environmental policies; however, the nonlinearity and dynamism of real world increase the gap between uncertainty depiction and its evaluation in policy strategies. This work attempts to implement a methodology that is able to minimise uncertainty in decision support tools related to rural planning and management. Fuzzy Cognitive Maps, the Dempster–Shafer theory and nonlinear optimisation were applied to achieve the above-mentioned goal. The method was tested to describe suitable policies and intervention strategies to address the effects of the recent economic crisis in the agricultural sector.     

A multi-criteria modeling approach to jury selection

The decision to select a particular group of individuals to serve as a jury is critically important to insure the fairness and impartiality in a trial under the United States Constitution. Yet, the amount of effort and, particularly, the time required under currently used methods of jury selection adds substantially to judicial system costs and can contribute to the inability of the courts to provide a timely hearing. This paper presents an approach to this decision process that incorporates current questionnaire data collection approaches with a multi-criteria, multi-objective modeling approach. An illustrative example from an actual trial case study is presented to demonstrate the informational efficacy of the modeling approach in selecting candidates for jury trials.     

Mapping the innovation production process from accumulative advantage to economic outcomes: A path modeling approach

The research about the innovation production process (IPP) is burgeoning. Our understanding of the interdependent interactions between functionally distinct innovation activities during it from a systemic perspective is rather unclear, yet, which is beneficial to empirical innovation management. This study, based on systems thinking, presents a novel analytical framework to empirically and quantitatively map the IPP jointly associated with a path modeling approach, which helps in untangling the interactive mechanism between stage-specific innovation activities with distinct functions within an IPP from accumulative advantage to economic outcomes. We use the attractive analytical framework to guide an empirical investigation to the China's high-tech industries' IPP at the macro-regional level. Our empirical study confirms the dominant role of previous innovation capital accumulation in the whole IPP embedded into regional innovation systems of China's high-tech industries. That is, we prove the existence of accumulative advantage phenomenon in the regional IPP. The examination results show that there is a significant Matthew effect of technological innovation accumulation on technological innovation inputs as well as the Path dependence of technological innovation outputs/outcomes on technological innovation accumulation. This indicates that the innovation-practitioners should promote innovation capital accumulation for sustainable innovations and economic profits in a long time. At the same time, our findings suggest that, in order to alleviate the cross-regional unbalance of innovation development and promote radial innovations in China's high-tech industries, both policy-makers and innovation-practitioners should try to get rid of the dependence on the previous accumulated innovation capital.     

A decision support system to improve the efficiency of resource allocation in healthcare management

Limitations in healthcare funding require hospitals to find more effective ways to utilize resources. An effective patient management system is critically dependent on the accurate analysis of individual patient outcomes and resource utilization. In the current paper, a management-oriented decision support model is thus proposed to assist health system managers in improving the efficiency of their systems. In the first stage of the model, the key variables affecting system efficiency, as well as their causal relationships, are identified through causal maps. Efficiency is measured by the total time spent in the system. In the second stage, a Bayesian Belief Network (BBN) is employed to represent both the conditional dependencies and uncertainties of the key variables. In the third stage, a sensitivity analysis is performed using a BBN to determine the most critical variable(s) in terms of impact on the system. Finally, strategies to improve system efficiency are proposed. The suggested decision support system is applied to the tomography section in the radiology department of a private hospital in Turkey.     

A coupled Markov chain approach to credit risk modeling

We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heuristic global optimization techniques.We benchmark the model against a GLMM model in the context of bond portfolio risk management. The proposed model yields stronger dependencies and higher risks than the GLMM model. As a result, the risk optimal portfolios are more conservative than the decisions resulting from the benchmark model.     

A time-varying volatility approach to modeling the phillips curve: A cross-country analysis

This research examines the Phillips curve price adjustment mechanism allowing for the conditional variance of inflation to be time varying. Specifically, we estimate ARCH and GARCH models of inflation for Canada, Japan, and the U.K. The results suggest that an increase in the conditional variability of inflation leads to higher levels of inflation. In addition, inclusion of inflation variability in the Phillips curve model results in a higher weight being attributed to the output gap than in traditional models. (JEF E24)     

A scenario-based integrated approach for modeling carbon price risk

Carbon prices are highly dependent on government emission policies and local industrial compositions. When historical data does not exist or limited price data can only be sourced from another country, scenario analysis becomes the only tool for the modelling of future carbon prices. However, various plausible but equally possible scenarios can produce large variations in forecast carbon prices. In a traditional approach of scenario analysis, investment decisions or risk management strategies are proposed and analysed for each given scenario, optimal solutions are determined. However, when the number of scenarios becomes large, it often becomes too complex and intractable to have a clear view on the selection of investment decisions or risk-management strategies because these decisions and strategies are closely linked with each of the many scenarios. In this paper, it is proposed to use a stochastic mean-reversion model to represent future carbon price movements, but this model is calibrated to the forecast carbon prices of all the scenarios. In this approach, a single model is used to capture the underlying uncertainty and expectation of the stochastic carbon prices as projected by all the scenarios, carbon price risk can thus be modeled and analysed without the need for direct references to any specific scenarios. The modelling and management of long-term carbon-price risk are therefore purely dependent on future carbon price levels and volatilities of these scenarios, instead of on the scenarios themselves. Through such an approach, the optimization of investment decisions and risk management solutions can be much simpler because the forecasted carbon prices are the only input data.      

A new methodology for multidimensional poverty measurement based on the capability approach

We propose a methodology based on the use of clustering techniques derived from data analysis and multi-attribute decision analysis methods aiming at purposeful multidimensional poverty measurement. Our contribution to methodological knowledge insists on the necessity to build “meaningful measurements” for policy making and policy implementation. Our standpoint underlines the necessity to consider the problem of poverty measurement as a decision problem and to tackle its measurement issue with that in mind. We also show that such an exercise can be useful to develop a better operational definition of poverty and to solve the aggregation issues.     

A Bayesian approach to optimal monetary policy with parameter and model uncertainty

This paper undertakes a Bayesian analysis of optimal monetary policy for the U.K. We estimate a suite of monetary-policy models that include both forward- and backward-looking representations as well as large- and small-scale models. We find an optimal simple Taylor-type rule that accounts for both model and parameter uncertainty. For the most part, backward-looking models are highly fault tolerant with respect to policies optimized for forward-looking representations, while forward-looking models have low fault tolerance with respect to policies optimized for backward-looking representations. In addition, backward-looking models often have lower posterior probabilities than forward-looking models. Bayesian policies therefore have characteristics suitable for inflation and output stabilization in forward-looking models.     

Performance effects of using an ERP system for manufacturing planning and control under dynamic market requirements

Enterprise resource planning (ERP) systems have a controversial reputation. Critics say that even if ERP systems may be beneficial for organizations operating in stable conditions, they are surely detrimental to organizations that face dynamic market requirements. This is because ERP systems are said to impose such procedures and constraints on organizations that make business processes inflexible to change. In contrast, proponents argue that the information-processing capabilities of ERP systems are crucial for organizations that face dynamic market requirements and also that the criticized procedures and constraints actually support process reengineering. These two contradictory arguments are often found in practitioner literature, but both of them can also be supported by management theory. The central tenets of the Organic Theory of organization design imply that ERP systems should be detrimental when market requirements change frequently, whereas the principles of Rigid Flexibility Theory suggest that they should be advantageous. In this study, we use cross-sectional data from 151 manufacturing plants to determine which argument is more applicable in the context of manufacturing planning and control. The results strongly favor the use of ERP systems under dynamic market requirements. To facilitate the reconciliation of the two contradictory arguments, we discuss how the results may have been influenced by two contextual factors: the predominantly technical nature of the studied organizational system and the tight interdependence of the studied activities.     

A decision model for selecting protected habitat areas within migratory flyways

A decision model is developed for identifying alternative configurations of new protected habitat areas to augment and enhance major global flyways of migratory birds. New habitat areas are selected to fill in the spatial gaps between existing protected stopovers such as wildlife refuges. The methodology is applied to the US portion of the Atlantic flyway in order to develop alternative networks of habitat stepping stones extending from Maine to Florida. The model is formulated as a zero-one program and solutions are derived using a greedy adding heuristic procedure. Two ecological-economic objectives drive the model: maximizing the geographic coverage of the protected stopovers, and maximizing average stopover quality, as represented by an index based on wetland prevalence and land cost. Thirty-five alternative solutions were generated, indicating the tradeoffs between the objectives. These results are expected to be useful to natural resource agencies and conservation organizations in the development of habitat conservation, preservation, and restoration policies and programs.     

装备保障配送中心选址的灰色评价模型

通过对影响装备保障配送中心选址的影响因素分析,从服务水平、经济性、可扩展性、可靠性和安全性等几个方面构建装备保障配送中心评价的指标体系,在此基础上,运用多层次灰色评价方法建立了配送中心选址多层次灰色综合评价模型。最后通过实例分析表明该模型能很好地处理装备保障配送中心选址问题,为决策者提供一种有效的优化工具。