Connections between Survey Calibration Estimators and Semiparametric Models for Incomplete Data

Survey calibration (or generalized raking) estimators are a standard approach to the use of auxiliary information in survey sampling, improving on the simple Horvitz–Thompson estimator. In this paper we relate the survey calibration estimators to the semiparametric incomplete‐data estimators of Robins and coworkers, and to adjustment for baseline variables in a randomized trial. The development based on calibration estimators explains the “estimated weights” paradox and provides useful heuristics for constructing practical estimators. We present some examples of using calibration to gain precision without making additional modelling assumptions in a variety of regression models.     

An analytical approach for making management decisions concerning corporate restructuring

Internal corporate restructuring activities, such as downsizing, sale or termination of a business line, facility closure, consolidation, or relocation, often occur as part of managerial strategies intended to improve efficiency, control costs, and adapt to an ever‐changing business environment. Such actions frequently result in fundamental changes in a business's organization, its strategies, its systems, and its operations. They can unsettle a business and often significantly affect current and future earnings and cash flows. In this paper we propose a novel decision‐making model through the use of the dynamic programming technique to illustrate how management can determine the optimal timing and appropriate restructuring actions that maximize the benefits of a restructuring program. Copyright © 2006 John Wiley & Sons, Ltd.     

Holistic investment assessment: optimization,risk appraisal and decision making

On deciding for the most appropriate investment when capital restrictions exist, investors define their alternatives and analyze each one of them. Traditionally, the definition, appraisal and analysis stages are treated separately. Herein, an innovative holistic method is proposed for bridging these stages. Within this method, investment attributes definition occurs by genetic algorithm optimization, while the analysis of the investment is realized through simulation. The method also proposes the NPV Expected Shortfall and the NPV Risk Preference Index as investment evaluation criteria. An illustrative case study of two mutually exclusive renewable energy investment scenarios is also used for demonstration purposes. Copyright © 2008 John Wiley & Sons, Ltd.     

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.      

Sampling-based Randomised Designs for Causal Inference under the Potential Outcomes Framework

We establish the inferential properties of the mean-difference estimator for the average treatment effect in randomised experiments where each unit in a population is randomised to one of two treatments and then units within treatment groups are randomly sampled. The properties of this estimator are well understood in the experimental design scenario where first units are randomly sampled and then treatment is randomly assigned but not for the aforementioned scenario where the sampling and treatment assignment stages are reversed. We find that the inferential properties of the mean-difference estimator under this experimental design scenario are identical to those under the more common sample-first-randomise-second design. This finding will bring some clarifications about sampling-based randomised designs for causal inference, particularly for settings where there is a finite super-population. Finally, we explore to what extent pre-treatment measurements can be used to improve upon the mean-difference estimator for this randomise-first-sample-second design. Unfortunately, we find that pre-treatment measurements are often unhelpful in improving the precision of average treatment effect estimators under this design, unless a large number of pre-treatment measurements that are highly associative with the post-treatment measurements can be obtained. We confirm these results using a simulation study based on a real experiment in nanomaterials.     

A goal-seeking approach to capital budgeting

This paper articulates how a goal-seeking model addresses a variation of the capital-budgeting problem. Focused toward technology modernization in the public sector, this multi-criteria optimization model explicitly considers the diverse functions of the organization. In particular, the synergism amongst the functions is modeled as a multiplicative value function. The model is solved by the “constraint reduced feasible-region method”, resulting in a non-convex mathematical program that produces numerical intricacies. Linearization of the criterion (objective) functions reduces such intricacy. An Army-modernization acquisition-study was used to illustrate the proposed model, showing that its non-inferior solutions are remarkably stable. Comparison is also made with other approaches, typically formulated as goal-setting programs. The model highlights how technology acquisitions are affected as the priorities of each organizational function changes.     

A nonlinear approach to a class of combinatorial optimization problems

A special class of combinatorial optimization problems is considered. We develop a compact nonconvex quadratic model for these problems that incorporates all inequality constraints in the objective function, and discuss two approximation algorithms for solving this model. One is inspired by Karmarkar's potential reduction algorithm for solving combinatorial optimization problems; the other is a variant of the reduced gradient method. The paper concludes with computational experiences with both real-life and randomly generated instances of the frequency assignment problem. Large problems are satisfactorily solved in reasonable computation times.     

A social procedurial approach to the Pareto optimization problematique

This article applies a variant of game theory to the Pareto multi-value problematique, that is situations where members of a group, community or society are faced with alternative allocations, institutional arrangements, or states of the world and may collectively choose an allocation, institutional arrangement or state of the world if they can agree on it. This type of multiple value decision situation is increasingly prevalent not only on the level of societal and political issues but on the level of many enterprises, particularly those advocating corporate social responsibility. Because actors hold and apply values from different perspectives, there are potential contradictory value judgments and incompatible equilibria. In a world of contradiction, incommensurability, and disequilibrium, to what extent can conflicts be resolved and social equilibrium accomplished? Force works but it is inherently unstable. Drawing on an extension of classical game theory, generalized game theory (GGT), this article addresses the multi-value problematique in terms of collective “resolution procedures.” These regulative procedures—or social algorithms—are applied to problems of conflict and suboptimality in a multiple value world such as Pareto envisioned. This paper (the first of two) outlines key elements of GGT, defines the Pareto multi-value problematique, pointing out several of the critical weaknesses, theoretical as well as empirical, of the Pareto approach. GGT is then applied in defining and analyzing several major procedures to realize improvements in a multi-value world characterized by conflict and sub-optimality. A second article conceptualizes a complex of societal games making up a social system with 2-phase multi-level game processes; it applies the conceptualization to the different societal procedures for multi-value choice under conditions of conflict. Procedures such as democratic voting, adjudication and administrative decision-making, and multi-lateral negotiation are capable of producing outcomes that in many cases are widely accepted as legitimate and become social equilibria (at least within some range of conditions). These procedures and the conditions for their activation and implementation are modelled and explicated through a generalized game approach.     

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.     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study

In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.     

A robust optimization approach for decontamination planning of emergency planning zone: Facility location and assignment plan

Establishing a robust facility location and assignment plan to improve the efficiency of the decontamination process is critical to alleviating the physical impact of the radiation leakage that occurs in a nuclear accident. This study develops an approach for optimizing the locations of decontamination facilities and assignments of affected villages. The approach is a robust optimization model that optimizes the worst-case performance. The system dynamic model is integrated into the robust optimization model to simulate the decontamination process and compute the decontamination time. A case study is conducted of the Plume Emergency Planning Zone in China. The results indicate that (1) a decontamination site location plan can be obtained in which each site is located in a different direction, (2) no evacuee will be allowed to travel across the downwind area in an assignment plan, and (3) a larger financial investment does not imply an increased decontamination efficiency. An appropriate budget exists that can balance the decontamination time and cost. The proposed model can assist decision makers in (i) better understanding the effects of decontamination site location and village assignment and (ii) deciding which location and assignment plans should be applied to cope with disruptive nuclear accidents.     

Reliable design of a congested disaster relief network: A two-stage stochastic-robust optimization approach

The preparedness of humanitarian relief networks can be enhanced by pre-positioning resources in strategic locations and using them when disasters strike, a strategy that gives rise to a two-stage planning problem. This paper presents a novel two-stage stochastic-robust optimization approach for integrated planning of pre- and post-disaster positioning and allocation of relief resources, while taking into consideration the uncertainty about demand for relief services and disruptions in the relief facilities and the transportation network. The proposed approach enables planners to effectively use limited historical data and imperfect experts’ opinions to obtain robust solutions while avoiding the over-conservatism of classical robust optimization methods. The objective sought is to minimize the expected total time victims need to receive assistance, including both access time to facilities and waiting/service time in them. Congestion in relief facilities is accounted for by modeling them as queuing systems and penalizing waiting time. A decomposition method based on column-and-constraint generation is implemented to solve the problem, whereas the nonlinear terms corresponding to queuing in the second-stage problem are handled using a direct search procedure. Applicability of the proposed approach is demonstrated through a real case study and the numerical results are analyzed to draw managerial insights.     

An alternative quasi likelihood approach,Bayesian analysis and data-based inference for model specification

This paper studies an alternative quasi likelihood approach under possible model misspecification. We derive a filtered likelihood from a given quasi likelihood (QL), called a limited information quasi likelihood (LI-QL), that contains relevant but limited information on the data generation process. Our LI-QL approach, in one hand, extends robustness of the QL approach to inference problems for which the existing approach does not apply. Our study in this paper, on the other hand, builds a bridge between the classical and Bayesian approaches for statistical inference under possible model misspecification. We can establish a large sample correspondence between the classical QL approach and our LI-QL based Bayesian approach. An interesting finding is that the asymptotic distribution of an LI-QL based posterior and that of the corresponding quasi maximum likelihood estimator share the same “sandwich”-type second moment. Based on the LI-QL we can develop inference methods that are useful for practical applications under possible model misspecification. In particular, we can develop the Bayesian counterparts of classical QL methods that carry all the nice features of the latter studied in  White (1982). In addition, we can develop a Bayesian method for analyzing model specification based on an LI-QL.     

优化班级奖助学金评选机制的实践和探索——云南农业大学经济管理学院个案分析

近年来,随着各类奖助学金的增加,学生对获得奖助学金尤其是高额奖助学金的期望值大为提高,如何在学生中公正、公平、公开地解决奖助学金评选问题,已成为学生关注的热点,也是摆在学生工作者面前的难题。本文首先介绍了经济管理学院班级奖助学金评选工作的现状,在结合问卷调查分析结果以及总结具体的改进实践工作经验的基础上,提出了优化班级奖助学金评选机制的改进措施。     

Logistics optimization through a social approach for food distribution

The management of supply chains is becoming more important in economic and social environments. Currently, the social sustainability is a factor that must be considered to design governmental strategies and policies. The objective of this research paper was to show, with a case study an approach to optimize distribution and delivery logistics of food in a social assistance program of school breakfast using mathematical models that include transportation distance, optimal locations, and vehicle routing through different clusters. By using qualitative variables like poverty levels, food insecurity and social exclusion, different clustering methods are proposed with the purpose of identifying the common characteristics in the studied population; and at the same time, reducing the distribution complexity. The results show an efficient approach to design a supply chain that includes economic and social factors. The new model developed in this paper can be used to plan social assistance governmental programs, to identify the specific needs and characteristics of the beneficiaries, minimizing the total cost of the distribution network logistics when delivering food for school meals.     

Long-term wealth growth portfolio allocation under parameter uncertainty: A non-conservative robust approach

The Kelly portfolio, which is documented to have the highest wealth growth rate of any other portfolio in the long run, has highly risky and unstable performance in the short term. This paper offers a hybrid approach to address this problem by integrating the concept of ridge regression and shrinkage estimation into a robustly modified Kelly portfolio. The proposed approach is a two-stage optimization process that not only takes into account the effect of estimation error but also solves the notoriously conservative problem introduced by the robust optimization method. By extending the worst-case scenarios considered by the robust Kelly portfolio, our approach significantly improves its out-of-sample performance without compromising risk reduction. In an extensive out-of-sample analysis with simulated and empirical data sets, we also characterize the impacts of the robustness level and the length of the rolling window on the final result. Moreover, we conduct a comparative study to confirm the validity of the proposed approach, and our model allows the investor to have a better risk-return trade-off than other traditional models.     

Portfolio management with robustness in both prediction and decision: A mixture model based learning approach

We develop in this paper a novel portfolio selection framework with a feature of double robustness in both return distribution modeling and portfolio optimization. While predicting the future return distributions always represents the most compelling challenge in investment, any underlying distribution can be always well approximated by utilizing a mixture distribution, if we are able to ensure that the component list of a mixture distribution includes all possible distributions corresponding to the scenario analysis of potential market modes. Adopting a mixture distribution enables us to (1) reduce the problem of distribution prediction to a parameter estimation problem in which the mixture weights of a mixture distribution are estimated under a Bayesian learning scheme and the corresponding credible regions of the mixture weights are obtained as well and (2) harmonize information from different channels, such as historical data, market implied information and investors׳ subjective views. We further formulate a robust mean-CVaR portfolio selection problem to deal with the inherent uncertainty in predicting the future return distributions. By employing the duality theory, we show that the robust portfolio selection problem via learning with a mixture model can be reformulated as a linear program or a second-order cone program, which can be effectively solved in polynomial time. We present the results of simulation analyses and primary empirical tests to illustrate a significance of the proposed approach and demonstrate its pros and cons.     

An industrial ecology virtual framework for policy making in China

ABSTRACT

This study develops a multi-regional input–output (MRIO) laboratory for policy-relevant applications in China. The Chinese IELab features unique flexibility and advances the previous state of the art in terms of three novel aspects. First, it can generate regionally and sectorally very detailed MRIO tables based on users’ own research questions. Second, it covers the entire territorial economic boundary and has the longest and most up to date annual time series from 1978 to 2015. Third, it can be used to provide insight to a wide range of research and policy questions including social, economic, and environmental issues, thus significantly improving all applications that rely on input–output tables. These features are illustrated by generating a Beijing–Tianjin–Hebei multi-regional supply and use table for 2014 at city level and applying it to the case study of transferring Beijing’s non-capital functions according to The Beijing-Tianjin-Hebei Coordinated Development Strategy set by the Chinese government.     

针织企业管理信息系统的设计及其优化研究

文章结合开发某针织企业管理信息系统的实践,对企业管理信息系统的设计、实现过程进行了探讨,对其性能的优化进行了必要的研究。该系统的实现产生了较好的经济效益和社会效益。