Matching patient and physician preferences in designing a primary care facility network

This paper introduces an integer programming model for planning primary care facility networks, which accounts for the interests of different stakeholders while maximizing access to health care. Physician allocation to health-care facilities is explicitly modelled, which allows consideration of physician incentives in the planning phase. An illustrative case study in the Turkish primary care system is presented to show the implications of focusing on patient or physician preferences in the planning phase. A discussion of trade-offs between the different stakeholder preferences and some recommendations for modelling choices to match these preferences are provided. In the context of this case, we found that using an access measure that decays with distance, and incorporating nearest allocation constraints improves performance for all stakeholders. We also show that increasing the number of physicians may have adverse affects on access measures when physician preferences are addressed.     

Performance analysis of distributed solution approaches in simulation-based optimization

Applying computationally expensive simulations in design or process optimization results in long-running solution processes even when using a state-of-the-art distributed algorithm and hardware. Within these simulation-based optimization problems the optimizer has to treat the simulation systems as black-boxes. The distributed solution of this kind of optimization problem demands efficient utilization of resources (i.e. processors) and evaluation of the solution quality. Analyzing the parallel performance is therefore an important task in the development of adequate distributed approaches taking into account the numerical algorithm, its implementation, and the used hardware architecture. In this paper, simulation-based optimization problems are characterized and a distributed solution algorithm is presented. Different performance analysis techniques (e.g. scalability analysis, computational complexity) are discussed and a new approach integrating parallel performance and solution quality is developed. This approach combines a priori and a posteriori techniques and can be applied in early stages of the solution process. The feasibility of the approach is demonstrated by applying it to three different classes of simulation-based optimization problems from groundwater management.     

A group decision making model based on goal programming with fuzzy hierarchy: an application to regional forest planning

In this paper a multi-criteria group decision making model is presented in which there is a heterogeneity among the decision makers due to their different expertise and/or their different level of political control. The relative importance of the decision makers in the group is handled in a soft manner using fuzzy relations. We suppose that each decision maker has his/her preferred solution, obtained by applying any of the techniques of distance-based multi-objective programming [compromise, goal programming (GP), goal programming with fuzzy hierarchy, etc.]. These solutions are used as aspiration levels in a group GP model in which the differences between the unwanted deviations are interpreted in terms of the degree of achievement of the relative importance amongst the group members. In this way, a group GP model with fuzzy hierarchy (Group-GPFH) is constructed. The solution for this model is proposed as a collective decision. To show the applicability of our proposal, a regional forest planning problem is addressed. The objective is to determine tree species composition in order to improve the values achieved by Pan-European indicators for sustainable forest management. This problem involves stakeholders with competing interests and different preference schemes for the aforementioned indicators. The application of our proposal to this problem allows us to be able to comfortably address all these issues. The results obtained are consistent with the preferences of each stakeholder and their hierarchy within the group.     

Exploring the use of narrative analysis as an operational research method: A case study in voluntary sector evaluation

Operational research frequently has to deal with situations where the perceptions and views of the various stakeholders involved may be quite different. One such situation is provided by the case of the evaluation of the work of voluntary sector groups, where concepts such as quality are frequently held to be the desired objectives whose achievement should be evaluated, yet where quite different perceptions of these concepts are held by the various parties to the evaluation. Through the use of a case study, this paper illustrates how approaches to narrative analysis like actant analysis and deconstruction can be used alongside other soft OR methods to enable negotiation of common understandings of important concepts like quality.     

Using DEA for planning in UK universities—an institutional perspective

This paper is focused on the process of performance measurement undertaken by different stakeholders in the UK higher education sector, focusing on the institutional perspective. Different classes of stakeholders have different motivations to measure performance. Institutions will be affected on the one hand by the state evaluation of them, and on the other by the applicant's. The contribution of Data Envelopment Analysis (DEA) to inform management is explored and illustrated in an application to the University of Warwick, using concepts from a technique to support strategic option formulation, the Boston Consulting Group (BCG) matrix.     

Implementation of STEM Education Policy: Challenges,Progress, and Lessons Learned

This is a case study of the implementation of state STEM (science, technology, engineering, and mathematics) policy over the period of the first 18 months of building a regional STEM partnership. Fullan's change theory is the framework used to determine progress and associated challenges with building a regional STEM educational partnership and establishment of STEM schools through a sustained education reform effort. Key stakeholders who were involved in leading this effort participated in focus groups, as well as individual interviews. Archival documentation was also used. Findings indicate implementation of STEM policy in this state experienced some barriers because of the nature of funding and timeline, as well as the competing agendas and interests of partners who did not have the opportunity to develop common vision and strategic plans prior to implementation. Implications for STEM policy decisions and implementation of other efforts through Race to the Top and other federal funding sources are discussed.     

An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring

Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented.     

Reduction of overestimation in interval arithmetic simulation of biological wastewater treatment processes

A novel interval arithmetic simulation approach is introduced in order to evaluate the performance of biological wastewater treatment processes. Such processes are typically modeled as dynamical systems where the reaction kinetics appears as additive nonlinearity in state. In the calculation of guaranteed bounds of state variables uncertain parameters and uncertain initial conditions are considered. The recursive evaluation of such systems of nonlinear state equations yields overestimation of the state variables that is accumulating over the simulation time. To cope with this wrapping effect, innovative splitting and merging criteria based on a recursive uncertain linear transformation of the state variables are discussed. Additionally, re-approximation strategies for regions in the state space calculated by interval arithmetic techniques using disjoint subintervals improve the simulation quality significantly if these regions are described by several overlapping subintervals. This simulation approach is used to find a practical compromise between computational effort and simulation quality. It is pointed out how these splitting and merging algorithms can be combined with other methods that aim at the reduction of overestimation by applying consistency techniques. Simulation results are presented for a simplified reduced-order model of the reduction of organic matter in the activated sludge process of biological wastewater treatment.     

区间直觉模糊信息下的监理工程师信用评价

由于外界环境的复杂多变和决策者的主观偏好,若运用传统信用评价方法单从业主或承包商的视角对监理工程师进行信用评价,会导致评价结果出现偏差。针对此本文从利益相关者的层面,运用区间直觉模糊集构建模糊综合评价模型,对监理工程师的信用行为进行评价,此模型通过相似性度量值、精确度函数分别得到利益相关者的权重和信用评价指标的权重,并在此基础上运用IIFHG等算子对区间直觉模糊信息进行集结,可以充分考虑不同利益相关者在评价过程中的话语权,有效规避评价主体因主观偏好所引起的偏差。最后通过算例分析表明该方法的有效性和合理性。     

Efficient Software-Implementation of Finite Fields with Applications to Cryptography

In this work, we present a survey of efficient techniques for software implementation of finite field arithmetic especially suitable for cryptographic applications. We discuss different algorithms for three types of finite fields and their special versions popularly used in cryptography: Binary fields, prime fields and extension fields. Implementation details of the algorithms for field addition/subtraction, field multiplication, field reduction and field inversion for each of these fields are discussed in detail. The efficiency of these different algorithms depends largely on the underlying micro-processor architecture. Therefore, a careful choice of the appropriate set of algorithms has to be made for a software implementation depending on the performance requirements and available resources.     

Comparison of iterative improvement techniques for schedule optimization

Due to complexity reasons of realistic scheduling applications, often iterative improvement techniques that perform a kind of local search to improve a given schedule are proposed instead of enumeration techniques that guarantee optimal solutions. In this paper we describe an experimental comparison of four iterative improvement techniques for schedule optimization that differ in the local search methodology. These techniques are iterative deepening, random search, tabu search and genetic algorithms. To compare the performance of these techniques, we use the same evaluation function, knowledge representation and data from one application. The evaluation function is defined on the gradual satisfaction of explicitly represented domain constraints and optimization functions. The satisfactions of individual constraints are weighted and aggregated for the whole schedule. We have applied these techniques on data of a steel making plant in Linz (Austria). In contrast to other applications of iterative improvement techniques reported in the literature, our application is constrained by a greater variety of antagonistic criteria that are partly contradictory.     

Efficient generation of alternative perspectives in public environmental policy formulation: applying co-evolutionary simulation–optimization to municipal solid waste management

In public policy formulation, it is generally preferable to create several quantifiably good alternatives that provide very different approaches to the particular situation. This is because public sector decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult—if not impossible—to quantify and capture at the time supporting decision models are constructed. There are invariably unmodelled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Furthermore, public environmental policy formulation problems often contain considerable stochastic uncertainty and there are frequently numerous stakeholders with irreconcilable perspectives involved. Consequently, it is preferable to generate several alternatives that provide multiple, disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to the known modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables. By generating a set of very different solutions, it is hoped that some of these dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This study provides a co-evolutionary simulation–optimization modelling-to-generate-alternatives approach that can be used to efficiently create multiple solution alternatives that satisfy required system performance criteria in highly uncertain environments and yet are maximally different in their decision space. This new stochastic approach is very computationally efficient, since it permits the simultaneous generation of good solution alternatives in a single computational run of the SO algorithm. The efficacy and efficiency of this technique is specifically demonstrated using an earlier waste management case to enable direct comparisons to previous methods. Waste management systems provide an ideal setting for illustrating the modelling techniques used for such public environmental policy formulation, since they possess all of the prevalent incongruencies and system uncertainties inherent in complex planning processes.     

A robust approach for scheduling in hospitals using multiple objectives

Scheduling in hospitals is a challenging task and stochastic influences have a major impact on the final schedule. Therefore, uncertainties of treatment durations and of emergency arrivals have to be taken into account explicitly. In order to avoid re-scheduling we integrate information on stochastic parameters into a scenario-based mixed-integer optimization model. Besides, we focus on different stakeholders’ objectives that are simultaneously considered within a multi-criteria optimization model. Individually optimal solutions are likely to differ and the overall aim is to identify a good and acceptable compromise solution. The presented approach is based on fuzzy sets and merges the interests of several stakeholders. Different schedules are calculated and later on evaluated with randomly generated scenarios for surgery times and emergencies. The resulting objective function values are close to the individually optimal solutions. Finally, the schedules lead to a high rate of utilization and a low amount of overtime.     

A multiple criteria information system for pedagogical evaluation and professional development of teachers

Information systems for the evaluation of pedagogical practices aim to improve teaching activity and encourage the pedagogical development of teachers. The implementation of these systems has been interpreted in different ways, depending on each school’s administration board and/or the person responsible for the evaluation process. This ambiguity (and inherent subjectivity) is a barrier to the principles of transparency, equity and fairness in the pedagogical performance evaluation of teachers. From this premise, the main objective of this paper consists in proposing a multiple criteria information system to support the evaluation of pedagogical practices. By combining cognitive mapping and the measuring attractiveness by a categorical based evaluation technique (MACBETH), our framework allows for the conception of a more transparent evaluation system, bringing new insights to the pedagogical appraisal and professional development of teachers. The strengths and weaknesses of our framework are also discussed.     

On an inverse problem: Recovery of non-smooth solutions to backward heat equation

We have recently developed two quasi-reversibility techniques in combination with Euler and Crank–Nicolson schemes and applied successfully to solve for smooth solutions of backward heat equation. In this paper, we test the viability of using these techniques to recover non-smooth solutions of backward heat equation. In particular, we numerically integrate the backward heat equation with smooth initial data up to a time of singularity (corners and discontinuities) formation. Using three examples, it is shown that the numerical solutions are very good smooth approximations to these singular exact solutions. The errors are shown using pseudo-L- and U-curves and compared where available with existing works. The limitations of these methods in terms of time of simulation and accuracy with emphasis on the precise set of numerical parameters suitable for producing smooth approximations are discussed. This paper also provides an opportunity to gain some insight into developing more sophisticated filtering techniques that can produce the fine-scale features (singularities) of the final solutions. Techniques are general and can be applied to many problems of scientific and technological interests.     

A new class of Laplace inverses and their applications

In this work, a new class of inverse Laplace transforms of exponential functions involving nested square roots are determined. Using these new inverses and other techniques from Laplace transform theory, a new class of three-parameter definite integrals, that yield to exact evaluation, is generated. It is shown that these integrals evaluate to simple closed-form expressions. These results are then verified using independent analytical techniques. Special and limiting cases of the parameters are investigated, some of which yield well-known expressions from classical analysis. Asymptotic results for these integrals and inverses are also given. In addition, a representation of the complementary error function as a limit is presented. Last, some aspects concerning the numerical implementation of these inverses are discussed and several applications in continuum mechanics are noted.     

Multiple objective data envelopment analysis as applied to the Indian Pharmaceutical Industry

The change of Intellectual Property Protection (IPP) from a softer process patenting to a stronger product patenting in Indian Pharmaceutical Industry (IPI) is attracting many global drug majors to source their production from India, which is the fourth largest producer of pharmaceuticals in the world. In this paper, the interests of different stake holders like the buyers (multinational enterprises), who are searching for efficient partners and the vendors (Indian drug producers) that are competing for the contracts, are analysed for a suitable efficiency evaluation criterion. The primary objective of this paper is to study how various firms in the IPI with different business strategies, competing for the same opportunities can find suitable benchmarking peer groups to meet the challenges of a dynamic business environment using data envelopment analysis (DEA). A multiple objective DEA model that determines suitable peer groups for inefficient companies is discussed along with more traditional DEA models. The proposed model has the flexibility to include inputs like R&D expenditure and outputs like Exports that are not homogeneously distributed across the firms and address the interests of various stake holders like buyers and vendors simultaneously.     

Agricultural countermeasures in nuclear emergency management: a stakeholders' survey for multi-criteria model development

In order to accommodate the use of multi-criteria decision-aid tools for nuclear crisis management in the Belgian context, a stakeholder process was carried out, aiming to add in stakeholders' values and practical knowledge in the models developed for decision support. The main steps are described in this paper, following a case study concerning the management of contaminated milk. We argue in favour of a systematic interaction with relevant stakeholders in all phases of the decision analysis. Some difficulties arising from the complexity of the problem itself, as well as from the different points of view of relevant stakeholders are illustrated and discussed. Suggestions are given for resolving this in a decision-aid framework and an example is provided throughout the paper for a better understanding of the issues involved.     

投资组合模型的改进研究:基于企业社会责任视角的实证分析

在尊重和借鉴前人对企业社会责任研究,尤其是在企业社会责任评价研究基础之上,本文从投资者的角度在投资组合过程中研究企业社会责任。在Markowitz(均值—方差)理论模型上添加企业社会责任的三个一级指标期望作为目标函数,由此将传统的投资组合模型扩展为五个目标函数的投资组合选择模型,而且我们根据经济学中经典的效用函数理论证明了此模型的正确性。本文引入主流的企业社会责任评价标准,并对一些典型公司进行打分量化。在此基础之上建立了以期望回报率、回报率的方差、核心利益相关者期望、蛰伏利益相关者期望和边缘利益相关者期望为目标函数的投资组合选择模型,在最小方差曲面上选取10个点构造投资组合,并以样本外的数据验证了模型的有效性。研究发现:根据此模型计算出来的部分投资组合回报率显著高于同期的市场指数。研究结果表明,这种关注企业社会责任的多目标投资组合选择模型,不仅让投资者可以直接控制企业社会责任,而且实际数据证明了此模型的优势之处,从而为关注企业社会责任的投资者提供一种投资的方法和思路。