Axiomatic approach to approximate solutions in multiobjective optimization

An axiomatic approach is introduced in order to study the approximate solution map of a vector optimization problem in the image space. We investigate the possibility to formulate an appropriate notion of approximate solutions that is compatible with von Neumann–Morgenstern utility theory. An impossibility result is proved in the sense that, whenever all of the axioms are satisfied, either the set of the approximate solutions is a subset of the exact solutions of the problem, or it coincides with the whole admissible set. Moreover, the geometry of the approximate solution map is studied in some special cases.      

Resolving operational paradox of sustainable supply chain: A decision framework approach

This study helps to reconcile the trade-offs between sustainable supply chain management (SSCM) and operational performance. Inspired by the paradox theory, the study advocates avoiding the contradictory elements and accepting the complementary elements between SSCM and Operational Performance. Accordingly, the study identifies various SSCM and operational performance elements followed by their evaluation to understand their complementary and contradictory nature. The study is conducted in two stages. First, the study constructs a list of SSCM features through exploratory factor analysis. Second, a unique decision framework of MACBETH (Measuring Attractiveness by Categorical-Based Evaluation Technique) and TODIM (Tomada de Decisión Inerativa Multicritero) methods is used to evaluate the SSCM features based on their impact on operational performance criteria. The proposed framework is validated in the context of the Indian Automobile Industry. The study results provide an empirically validated and prioritized list of SSCM features. In the list, the top-ranked features complement the operational performance criteria. In contrast, the lower-ranked features compromise the operational criteria, at least in the short term. Thus, this study reduces the skepticism around the adoption of SSCM by focusing on the top-ranked features of the list and avoiding the lower-ranked features during the early phases of SSCM adoption. Additionally, the study provides guidance to supply chain managers on achieving sustainability in a supply chain without compromising on its traditional goals. The results are of practical importance as supply chain managers may now choose to implement specific SSCM features that exhibit minimum negative impact on operational performance. Consequently, the study encourages an assertive adoption of SSCM even in developing countries.     

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.     

An agent-based modeling optimization approach for understanding behavior of engineered complex adaptive systems

The objective of this study is to present a formal agent-based modeling (ABM) platform that enables managers to predict and partially control patterns of behaviors in certain engineered complex adaptive systems (ECASs). The approach integrates social networks, social science, complex systems, and diffusion theory into a consumer-based optimization and agent-based modeling (ABM) platform. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). Furthermore, the modeling and solution methodology address shortcomings in previous ABM and Transactive Energy (TE) approaches and advances our ability to model and understand ECAS behaviors through computational intelligence. The mathematical approach is a non-convex consumer-based optimization model that is integrated with an ABM in a game environment.     

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 Hybrid model for locating new emergency facilities to improve the coverage of the road crashes

We propose an emergency facility-locating model aimed at increasing the coverage of emergency demand throughout the city. The proposed model takes into account the status and location of the emergency facilities in the network and identifies locations suitable for the construction of new facilities. Here, Data Envelopment Analysis (DEA) and Maximum Coverage Location Problem (MCLP) have been combined in a single model. To do so, design problem and evaluation problem are considered concurrently to maximize the efficiency of services provided by emergency facilities across the city in response to the demand. Moreover, the total emergency demand in each district was considered in relation to the population density, the fatal, injurious, and property damage only (PDO) crashes. The coverage area of each emergency facility was assumed to be proportional to the average ambulance speed in the surrounding road network during rush hours. The available budget was included in the model to let the model function under various fiscal conditions. Model input variables consisted of average number of mortalities, injuries and PDO crashes as well as the population density of each urban district. The output variables of the model included the coverage share of proposed emergency centers and hospitals equipped with ambulances. The model was tested on the network of Tehran (Iran). It is recommended to add the location of some emergency centers and hospitals to the network. Moreover, the results showed that ten urban districts had efficiency problem in provision of emergency services.     

Analytically comparing disaster resilience across multiple dimensions

It is important to compare the resilience of complex human systems to different types of disasters, in order to assess their inherent vulnerabilities and take appropriate actions to strengthen them. Resilient behavior can be complicated and multi-dimensional, however, and one must be able to characterize the different ways in which that resilience actually exhibits itself in practice. With this in mind, this paper discusses creating a multi-dimensional indicator for the resilience of a complex human system, and it explores an approach for visualizing and analyzing the relationships between each of the individual resilience dimensions. Because decision makers may differ on the relative contribution of the different dimensions to overall resilience, the paper further discusses the issue of weighting the different dimension values and the impacts that such a weighting scheme can have on the relative ranking of different scenarios. We illustrate the ability to characterize the complexity of multi-dimensional resilience by analyzing an empirical data set that measures the relative resilience of the New York metropolitan area to seven different natural disasters between 2010 and 2012.     

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.     

Optimal combination of Arctic sea ice extent measures: A dynamic factor modeling approach

The diminishing extent of Arctic sea ice is a key indicator of climate change as well as being an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way and accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.     

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 fuzzy bi-level method for modeling age-specific migration

Modeling and forecasting international migration are significant research areas since migration forecasts are vital in decision making and policy design regarding economy, security, society, and resource allocation. The methods for modeling and forecasting migration rely on strict subjective or statistical assumptions which may not always be met. In addition, lack of a universally accepted definition of the term “migrant” and the ambiguities in data due to recording and collection systems result in inconsistencies and vagueness in migration modeling. Considering these, in this paper, a fuzzy bi-level age-specific migration modeling method is proposed. The bi-level structure embedded in the model makes use of the well-known Lee-Carter method as well as fuzzy regression, singular value decomposition technique, and hierarchical clustering to reflect the general characteristics of the country of concern together with the distinct emigration and immigration behaviors of the age groups. Bayesian time series models are fitted to the time-variant fuzzy parameters obtained through the proposed method to forecast future migration values. The proposed method is applied on female and male age-specific emigration and immigration counts of Finland for 1990–2010 period and Germany for 1995–2012 period, and the future values are forecasted for 2011–2025 and 2013–2025 respectively. The method is compared with an existing Bayesian approach and the numerical findings display that the proposed fuzzy method is superior to the existing one in modeling and forecasting age-specific migration values within significantly narrower prediction intervals.     

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)     

Budgeting for research and development: a dynamic financial simulation approach

The research and development (R&D) budgeting decision is crucial for at least two reasons: if too much is spent, short-term financial stability is at risk, while, if the budget is too small, long-term competitiveness is threatened. Nevertheless, many enterprises simply extrapolate the past without further reflection.This paper presents a computer-based dynamic stochastic simulation model that allows one to assess the impact of alternative R&D budgeting policies on corporate development. The core decisions to be evaluated concern timing and funding of investments in R&D. Our approach substantially expands earlier work by Brockhoff (R&D Manage. 19 (1989) 265). In particular, it distinguishes between product and process innovation, considers market dynamics related to technical progress via a modifiable S-curve, integrates marketing, and takes into account essential financial aspects. As a result, our model is closer to reality than previous ones. A sample application with real company data illustrates its potential usage.     

Structural VAR and financial networks: A minimum distance approach to spatial modeling

In this paper, I interpret a time series spatial model (T-SAR) as a constrained structural vector autoregressive (SVAR) model. Based on these restrictions, I propose a minimum distance approach to estimate the (row-standardized) network matrix and the overall network influence parameter of the T-SAR from the SVAR estimates. I also develop a Wald-type test to assess the distance between these two models. To implement the methodology, I discuss machine learning methods as one possible identification strategy of SVAR models. Finally, I illustrate the methodology through an application to volatility spillovers across major stock markets using daily realized volatility data for 2004–2018.     

Threshold cointegration in international exchange rates:A Bayesian approach

This paper considers Bayesian estimation of the threshold vector error correction (TVECM) model in moderate to large dimensions. Using the lagged cointegrating error as a threshold variable gives rise to additional difficulties that typically are solved by utilizing large sample approximations. By relying on Markov chain Monte Carlo methods, we are enabled to circumvent these issues and avoid computationally-prohibitive estimation strategies like the grid search. Due to the proliferation of parameters, we use novel global-local shrinkage priors in the spirit of Griffin and Brown (2010). We illustrate the merits of our approach in an application to five exchange rates vis-á-vis the US dollar by means of a forecasting comparison. Our findings indicate that adopting a non-linear modeling approach improves the predictive accuracy for most currencies relative to a set of simpler benchmark models and the random walk.