1996~2017年《Operations Research》期刊发文统计分析

《Operations Research》(OR)是运筹与管理科学领域的顶级期刊,对其刊文进行统计分析,剖析国际运筹学科领域的主流研究机构及合作影响,探究当前研究热点问题及学科发展趋势,对于促进我国运筹学科领域的发展具有十分重要的指引作用.基于对WOS核心数据库中该期刊1996至2017年间全部刊文的统计分析,得出以下结论:近年来我国学者发文量稳步提升,表明我国运筹学科领域研究水平的显著提高,但其影响力仍有待进一步提升;机构合作、国际合作都有助于文章引文量的提升;引证分析显示柔性制造(流程柔性及效率)、巢式Logit模型的应用、网络收益管理等7个研究方向是当前、并继续成为运筹领域的研究热点.     

Agent-based simulation of innovation diffusion: a review

Mathematical modeling of innovation diffusion has attracted strong academic interest since the early 1960s. Traditional diffusion models have aimed at empirical generalizations and hence describe the spread of new products parsimoniously at the market level. More recently, agent-based modeling and simulation has increasingly been adopted since it operates on the individual level and, thus, can capture complex emergent phenomena highly relevant in diffusion research. Agent-based methods have been applied in this context both as intuition aids that facilitate theory-building and as tools to analyze real-world scenarios, support management decisions and obtain policy recommendations. This review addresses both streams of research. We critically examine the strengths and limitations of agent-based modeling in the context of innovation diffusion, discuss new insights agent-based models have provided, and outline promising opportunities for future research. The target audience of the paper includes both researchers in marketing interested in new findings from the agent-based modeling literature and researchers who intend to implement agent-based models for their own research endeavors. Accordingly, we also cover pivotal modeling aspects in depth (concerning, e.g., consumer adoption behavior and social influence) and outline existing models in sufficient detail to provide a proper entry point for researchers new to the field.     

Reviews on the Research of Dynamic Mortality Models

Mortality forecasting is the basis of population forecasting. In recent years, new progress has been made in mortality models. From the earliest static mortality models, mortality models have been developed into dynamic forecasting models including time terms, such as Lee-Carter model family, CBD model family and so on. This paper reviews and sorts out relevant literature on mortality forecasting models. With the development of dynamic models, some scholars have developed a series of mortality improvement models based on the level of mortality improvement. In addition, with the progress of mortality research, multi-population mortality modeling attracted the attention of researchers, and the multi-population forecasting models have been constantly developed and improved, which play an important role in the mortality forecasting. With the continuous enrichment and innovation of mortality model research methods, new statistical methods (such as machine learning) have been applied in mortality modeling, and the accuracy of fitting and prediction has been improved. In addition to the extension of classical modeling methods, issues such as small-area population or missing data of the population, the elderly population, the related population mortality modeling are still worth studying.     

50 years of data mining and OR: upcoming trends and challenges

Data mining involves extracting interesting patterns from data and can be found at the heart of operational research (OR), as its aim is to create and enhance decision support systems. Even in the early days, some data mining approaches relied on traditional OR methods such as linear programming and forecasting, and modern data mining methods are based on a wide variety of OR methods including linear and quadratic optimization, genetic algorithms and concepts based on artificial ant colonies. The use of data mining has rapidly become widespread, with applications in domains ranging from credit risk, marketing, and fraud detection to counter-terrorism. In all of these, data mining is increasingly playing a key role in decision making. Nonetheless, many challenges still need to be tackled, ranging from data quality issues to the problem of how to include domain experts' knowledge, or how to monitor model performance. In this paper, we outline a series of upcoming trends and challenges for data mining and its role within OR.     

Operations research methodology life cycle trend phases as recorded in journal articles

This paper applies life cycle concepts to forecast and explore trends in the publication rates of operations research (OR) methodology journal articles. The confirmatory part of this study seeks to show the accuracy of life cycles to forecast a trend for a single OR methodology. The exploratory part of this study extends the use of life cycle forecasting to a broader collection of OR methodologies. This part of the study entails a collection of keywords for OR methodologies from over 8000 journal articles. The results show a life cycle can be used to forecast a trend in OR journal article methodology productivity based on keyword listings. This paper also reveals trends in methodology usage for leading OR journals in Europe and the US. The existence and use of life cycle trends in the OR methodology research have ramifications on new directions for research, research funding, and OR textbook content.     

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.     

Perspectives on operations research in data and knowledge management

A number of problems in the design and management of database systems and knowledge base systems (KBSs) can be addressed using techniques from operations research (OR). This article provides a perspective on these problems and the types of models that have been applied to them, and identifies some areas that pose interesting modeling and analysis questions for researchers working in areas such as mathematical programming, stochastic modeling, dynamic programming and simulation.     

The Organization and Improvement of Market Forecasting

Quantitative forecasting techniques are not much used in organizations. Instead, organizations rely on the judgement of managers working close to the product market. Increasingly however, developments at the interface between marketing and operations require more accurate forecasting. Quantitative marketing models have that potential. Drawing on theories from the ‘diffusion of innovation’ literature and results on ‘the barriers to effective implementation’, this paper first considers those factors that should be included in any complete evaluation of market forecasting. Using this framework and based on detailed survey work in a multi-divisional organization, the paper then describes how this company produces its market forecasts, and the perceptions of its managers as to inadequacies in the procedures. Reasons are proposed as to why quantitative forecasting techniques are not effectively used. The paper concludes with a discussion of the causes behind the organization's mismanagement of their forecasting activity and how these activities might best be improved.     

Innovative research in OR/MS?

This paper is a contribution to the continuing debate about research priorities and publications in OR/MS. It categorises research publications in several different ways. First it uses approaches suggested in the information systems and operations management literature. It then takes previous examinations of leading OR/MS journals to gain a different perspective on the same issues. Finally, it uses a multi-faceted view of OR/MS to try to capture its full richness. These analyses confirm the view that much published research in OR is unengaged and focuses on decision mathematics or optimisation, apparently for its own sake. That is, most papers published in the leading OR/MS journals take little or no account of how their findings might be used in practice, nor do they express much concern about this issue. The reasons for this state of affairs are discussed, so as to consider whether a change might be overdue.     

Management science methodologies in environmental management and sustainability: discourses and applications

This paper investigates and discusses the use of systemic methodologies (SMs) developed in management science/operational research (MS/OR), in particular, those SM that have been informing the complexity inherent in environmental management and sustainable (EM/S) practices. By surveying a sample of the top MS/OR and systems journals, we assess the extent to which systemic management science methodologies developed recently have been used in tackling EM/S problems. Titles and abstracts of EM/S applications published in MS/OR and systems journals between 1989 and 2009 were queried for the occurrence of typical keywords associated with a set of SMs (eg, complexity theory, systems dynamics, soft systems, critical systems, viable systems model). The survey identifies a set of articles representing the practice of either a particular methodology or of a mixture of various SMs in EM/S setting. By assembling and critically reviewing a sample of applications in EM/S the paper hopes to raise awareness among environmentalists, operational researchers and management scientists of the benefits of using systemic approaches developed in MS/OR and, in this way, to encourage further exchange and conversation between these fields of management.     

ARMS: An automated resource management system for British Telecommunications plc

Accurate demand forecasting combined with resource planning is critical to a company’s performance and profitability. This paper describes ARMS (automated resource management system), an integrated system developed for the customer service operations of British Telecommunications plc to help with the operational/tactical planning and deployment of the company’s 20,000-strong field engineer workforce. ARMS integrates a forecasting tool with a resource planning tool and a resource balancing tool providing an end-to-end automated resource management solution for the organisation. OR techniques are used throughout the system, including ARIMA for forecasting, constraint satisfaction for problem modelling, heuristic search for problem solving thus demonstrating the value and relevance of OR in solving today’s business problems.     

Dynamic construction of statistical models in managerial DSS

The fields of operations research (OR) and artificial intelligence (AI) provide complementary methods that may be combined into managerial decision support systems (DSS). However, the management domain is substantially different from domains in which prior expert systems have been developed. Consequently, successful application of OR/AI techniques in managerial DSS requires careful analysis and additional development. Ongoing research concerning design and implementation of managerial DSS is discussed. A prototype system capable of constructing linear statistical models of direct and indirect relationships from a knowledge base of relationships is described and evaluated.     

Fifty years of operational research and emergency response

Over the past 50 years, a wealth of applications has resulted from researchers turning their attention to operations such as fire suppression, law enforcement and ambulance services. The 1970s might even be argued as the ‘golden age’ of this particular effort, producing many of the seminal works in fire station location planning, unit assignment and ambulance queuing models. Such efforts naturally continue through to the present, but with a focus shifting away from earlier contexts of established urban emergency service systems. Simultaneously, current evidence from the field suggests that far more work remains. In this paper, we review the operational research (OR) foundation in emergency response so far, highlighting the fact that most of what has been accomplished addresses the well-structured problems of emergency services. This, in turn, offers an explanation for some paradoxical challenges from the field: most of emergency response itself is semi-structured, at best. While OR has traditionally focused on the management of an organization, emergency response ultimately requires the management of disorganization, suggesting an important OR growth area for the next 50 years.     

Multicriteria classification and sorting methods: A literature review

The assignment of alternatives (observations/objects) into predefined homogenous groups is a problem of major practical and research interest. This type of problem is referred to as classification or sorting, depending on whether the groups are nominal or ordinal. Methodologies for addressing classification and sorting problems have been developed from a variety of research disciplines, including statistics/econometrics, artificial intelligent and operations research. The objective of this paper is to review the research conducted on the framework of the multicriteria decision aiding (MCDA). The review covers different forms of MCDA classification/sorting models, different aspects of the model development process, as well as real-world applications of MCDA classification/sorting techniques and their software implementations.     

A formal analysis of the model management literature

This paper presents an extensive bibliography of the archival material on model management, and an analysis of that material. It argues that the three general approaches to model management can be observed, called here the data-model analogy, knowledge-based and model-representation approaches. For the period 1970 to 1989, 57 journals were exhaustively searched for articles dealing with model management; 152 articles were identified. An informal analysis shows that model creation and execution have been the major model management functions addressed by research, and that mathematical programming and simulation have been the dominant model areas. Applications have addressed the extremes of Operations Research models, namely policy and strategy at one extreme and operations at the other, with many applications in accounting and finance also prevalent. A formal analysis using factor analysis shows that research in model management has moved away from speculation and focus on certain limited aspects of the subject towards the practical implementation of model management systems. Further, a regression model shows that the model-representation approach has tended to yield implemented systems, whereas the data-model analogy approach has not. Directions for future research and development are suggested.     

Knowledge management: a review of the field and of OR's contribution

This paper examines the field of knowledge management (KM) and identifies the role of operational research (OR) in key milestones and in KM's future. With the presence of the OR Society journal Knowledge Management Research and Practice and with the INFORMS journal Organization Science, OR may be assumed to have an explicit and a leading role in KM. Unfortunately, the origins and the evidence of recent research efforts do not fully support this assumption. We argue that while OR has been inside many of the milestones there is no explicit recognition of its role and while OR research on KM has considerably increased in the last 5 years, it still forms a rather modest explicit contribution to KM research. Nevertheless, the depth of OR's experience in decision-making models and decision support systems, soft systems with hard systems and in risk management suggests that OR is uniquely placed to lead future KM developments. We suggest that a limiting aspect of whether OR will be seen to have a significant profile will be the extent to which developments are recognized as being informed by OR.     

Fuzzy linear regression and its applications to forecasting in uncertain environment

Linear regression has been used for many years in developing mathematical models for application in marketing, management, and sales forecasting. In this paper, two different sales forecasting techniques are discussed. The first technique involves non-fuzzy abstract methods of linear regression and econometrics. A study of the international market sales of cameras, done in 1968 by John Scott Armstrong, utilized these non-fuzzy forecasting techniques. The second sales forecasting technique uses fuzzy linear regression introduced by H. Tanaka, S. Uejima, and K. Asai, in 1980. In this paper, a study of the computer and peripheral equipment sales in the United States is discussed using fuzzy linear regression. Moreover, fuzzy linear regression is applied to forecasting in an uncertain environment. Finally, some possible improvements and suggestions for further study are mentioned.     

Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research

Conjoint analysis has played an important role in helping make a number of operations management decisions including product and service design, supplier selection, and service operations capacity. Many recent advances in this area have raised questions about the most appropriate form of conjoint analysis for this research. We review recent developments in the literature and provide new evidence on how the choice between ratings- and choice-based conjoint models might affect the estimates of customer demand used in operations management models.     

Operational research and ethics: A literature review

Any human activity raises ethical questions, questions about ‘good’ and ‘right’ ways to act and to live; or to put it differently, questions of values and responsibility. From its inception operational research (OR) has engaged with such questions in terms of professional behavior, the handling of preferences in OR, the societal role of OR, the process of OR intervention and the content of OR analysis. As a result, analytical methods and processes have been developed to help clients explore the ethical dimension of their decisions. The paper reviews the literature published in selected OR journals (Management Science; Operations Research; Interfaces; the European Journal of Operational Research; the Journal of the Operations Research Society; Omega; International Transactions in Operational Research; the Journal of Multi-Criteria Decision Analysis), organizing it along the lines of OR’s core competences. The review identifies a number of significant research programmes that are well established and are being energetically pursued; the research findings are being applied to a wide range of important issues. Ethical questions lie at the heart of the great governmental and commercial issues of the day: economic growth and instability; inequality and injustice; environmental degradation and sustainability. They also lie at the heart of the more mundane decisions of day-to-day OR. ‘Ethics’ therefore provides a useful focus for OR both in terms of raising the awareness of all concerned and in providing a theme for research. As a result of the review some research questions are suggested. There is much of interest, much to do.     

Medium‐term horizon volatility forecasting: A comparative study

In this paper, volatility is estimated and then forecast using unobserved components‐realized volatility (UC‐RV) models as well as constant volatility and GARCH models. With the objective of forecasting medium‐term horizon volatility, various prediction methods are employed: multi‐period prediction, variable sampling intervals and scaling. The optimality of these methods is compared in terms of their forecasting performance. To this end, several UC‐RV models are presented and then calibrated using the Kalman filter. Validation is based on the standard errors on the parameter estimates and a comparison with other models employed in the literature such as constant volatility and GARCH models. Although we have volatility forecasting for the computation of Value‐at‐Risk in mind the methodology presented has wider applications. This investigation into practical volatility forecasting complements the substantial body of work on realized volatility‐based modelling in business. Copyright © 2007 John Wiley & Sons, Ltd.