A decision model for interdependent information system project selection

Existing methods for information system (IS) project selection neglect an important aspect of information technology, namely the interdependencies that exist among various IS applications (projects). Recognizing and modeling these project interdependencies provides valuable cost savings and greater benefits to organizations. In this paper, an IS project selection model is developed that identifies and models benefit, resource and technical interdependencies among candidate projects. The proposed model is formulated as a nonlinear 0–1 programming problem and represents a significant addition to existing IS, capital budgeting and R&D project selection models. The model is converted, using linearization techniques, and tested (validated) by applying it to real-world IS project selection data. By comparing the performance of this model with existing project selection models, the contribution of this model is highlighted.     

“I can name that Bayesian network in two matrixes!”

For a number of situations, a Bayesian network can be split into a core network consisting of a set of latent variables describing the status of a system, and a set of fragments relating the status variables to observable evidence that could be collected about the system state. This situation arises frequently in educational testing, where the status variables represent the student proficiency and the evidence models (graph fragments linking competency variables to observable outcomes) relate to assessment tasks that can be used to assess that proficiency. The traditional approach to knowledge engineering in this situation would be to maintain a library of fragments, where the graphical structure is specified using a graphical editor and then the probabilities are entered using a separate spreadsheet for each node. If many evidence model fragments employ the same design pattern, a lot of repetitive data entry is required. As the parameter values that determine the strength of the evidence can be buried on interior screens of an interface, it can be difficult for a design team to get an impression of the total evidence provided by a collection of evidence models for the system variables, and to identify holes in the data collection scheme. A Q-matrix - an incidence matrix whose rows represent observable outcomes from assessment tasks and whose columns represent competency variables - provides the graphical structure of the evidence models. The Q-matrix can be augmented to provide details of relationship strengths and provide a high level overview of the kind of evidence available. The relationships among the status variables can be represented with an inverse covariance matrix; this is particularly useful in models from the social sciences as often the domain experts’ knowledge about the system states comes from factor analyses and similar procedures that naturally produce covariance matrixes. The representation of the model using matrixes means that the bulk of the specification work can be done using a desktop spreadsheet program and does not require specialized software, facilitating collaboration with external experts. The design idea is illustrated with some examples from prior assessment design projects.     

An Activity-Based Costing decision model for life cycle assessment in green building projects

Carbon emissions are an increasingly important consideration in sustainable environmental development. In the green building industry, green construction cost controls and low-carbon construction methods are considered to be the key barriers encountered. Based on Corporate Social Responsibility (CSR) policy, management of carbon emissions from green building projects contributes to the acquisition of accurate building cost information and to a reduction in the environmental impact of these projects. This study focuses on the CO₂ emission costs and low-carbon construction methods, and proposes a 0–1 mixed integer programming (0–1 MIP) decision model for integrated green building projects, using an Activity-Based Cost (ABC) and life cycle assessment (LCA) approach. The major contributions of this study are as follows: (1) the integrated model can help construction company managers to more accurately understand how to allocate resources and funding for energy saving activities to each green building through appropriate cost drivers; (2) this model provides a pre-construction decision-making tool which will assist management in bidding on environmentally-friendly construction projects; and (3) this study contributes to the innovation operation research (OR) literature, especially in regard to incorporating the life cycle assessment measurement into construction cost management by utilizing a mixed decision model for green building projects.     

Structuring resource allocation decisions: A framework for building multi-criteria portfolio models with area-grouped options

Multi-criteria portfolio modelling has been extensively employed as an effective means to allocate scarce resources for investment in projects when considering costs, benefits and risks. Some of these modelling approaches allow the grouping of projects into organisational areas, thus also supporting the decision of resource allocation among organisational units in a way that is collectively efficient for the organisation. However, structuring in practice a portfolio model using this latter type of approach is not a trivial task. How should areas be defined? Where should new projects be included? How should one define the criteria to evaluate performance? As far as we know, there is very little indication in the operational research and decision sciences literatures on how to structure this type of model. This paper suggests different ways to structuring portfolio models where projects are divided into areas and evaluated by multiple criteria, and illustrates their use in two action-research projects. Drawing on these experiences it then suggests a general framework for the structuring of such models in practice. Directions for future research are also identified.     

项目网络中考虑支配与扩散关系的组合决策

单纯侧重项目自身属性而不考虑项目关联性以及由项目衍生而来的技术、经验/信息扩散对项目组合决策时的影响,易导致决策偏差,低估具有潜在技术先导性项目的价值。对此,引用复杂网络理论,以项目关联性的视角,将项目间支配和扩散关系分别抽象为有向加权网络,运用K-shell分解方法构建项目组合网络中基于支配关系的项目影响力模型以及技术、经验/信息在项目间扩散传播的模型。然后,基于PageRank算法,综合考虑项目间支配与扩散关系,建立了项目优先级排序决策模型。最后,通过算例分析说明了该模型与算法的可行性与有效性,为企业项目组合决策提供了有益的参考。     

Uncertainty Modelling in Software Development Projects (With Case Study)

A project scheduling model tailored specifically for software development projects is proposed in this study. The model incorporates uncertainties related to activity durations and network topology. The first type of uncertainty exists due to error-prone coding which might result in elongated task durations caused by validation and debugging sessions. Furthermore, in practice, macro-activities represent groups of sub-tasks in order to simplify the planning and monitoring of the project. Due to the aggregation, it is more difficult to be precise on the duration of a macro-activity.The uncertainty related to the network topology is due to common database design issues or program modules shared among parallel tasks in the project network. These tasks become associated with each other through uncertain Start-to-Start (SS) precedence relationships. On the other hand, SS lags may also be the outcome of technological precedence relationships among pairs of activities. However, the imprecision underlying the work content of a predecessor activity leads to uncertain SS lags.Software development projects are human-intensive projects and hence, the duration of a task depends on the skill of the person assigned to the job as well as his/her learning rate. Thus, a task may be realized by alternative staff members which results in different expected task durations. Hence, a realistic model proposed for software development projects should incorporate staff assignment features under the uncertainties discussed above. In this study, we develop a mathematical model for software development projects and propose heuristic solution methods to be used by the project co-ordinator in preparing the project plan. The heuristic algorithms developed here are tested on real data provided by a consulting firm undertaking software development projects from manufacturing companies in Turkey.     

A reliability assessment tool for distributed software development environment based on Java and J/Link

In this paper, we propose a software reliability assessment tool based on software reliability growth models for distributed development environment by using Java programming language and J/Link technology in Mathematica. Java is an object-oriented and platform-free programming language which has several advantages such as modularization and reusability of the existing code. Especially J/Link can use and control the Mathematica kernel from a Java program. By using this J/Link technology, we can reduce some efforts to develop the tool because of reusing the existing Mathematica code used for the research. Our tool proposed here is useful for software developers in terms of the management of the system-testing process in distributed development environment.     

A model and approach to the challenge posed by optimal power systems planning

Currently, there is a need to plan and analyze the electric power transmission system in greater detail and over larger geographic areas. Existing models approach the problem from different perspectives. Each model addresses different aspects of and has different approximations to the optimal planning process. In order to scope out the huge challenge of optimal transmission planning, this paper presents a new modeling approach for inter-regional planning and investment in a competitive environment. This modeling approach incorporates the detailed generator, topology and operational aspects found in production cost planning models into a larger framework that can find optimal sets of transmission expansion projects. The framework proposed here can be used in an auction to award investment contracts or as a part of a more general policy analysis. The solution yields the set of transmission projects that have the highest expected benefits, while also representing generic generation expansions under the same objective. The model is a two-stage, mixed-integer, multi-period, N-1-reliable model with investment, unit commitment, and transmission switching. The combination of combinatorial, stochastic and operational elements means this model may be computationally intractable without judicious modelling aggregations or approximations to reduce its size and complexity. Nevertheless we show via a dual problem that analysing the economics and sensitivity of the solution is computationally more straightforward.     

The development of computer software for risk analysis: A decision support system development case study

Over the past eight years the Projects Department of BP's Group Engineering and Technical Centre has evolved a risk analysis methodology which is applied during the project appraisal phase to all major engineering construction projects. The computational methods in current use are based on the Common Interval and Memory (CIM) approach, which allows the direct enumeration of distribution combinations rather than the more common Monte Carlo simulation approach. Computer software has been developed in-house to apply these techniques. The programs are written in Fortran and are designed to be used interactively. This paper reviews the general framework of risk analysis applications on BP projects and the lessons learned in developing the first generation of computer software, used until 1985. Some of these lessons may be relevant to decision support system development by others. The development of a new generation of software, now implemented, is also discussed briefly.     

Optimizing color picture tubes by high-cost nonlinear programming

In this paper we discuss the application of optimization techniques for the design of several parts of the color picture tube, the key component of television sets and computer monitors. These projects have been carried out for Philips Display Components in Eindhoven. Philips developed several computer simulation models of picture tube parts. Designers use these models to simulate the physical behavior of a particular part design. Depending on the amount of detail in the model, the running time of a typical simulation ranges from one up to 10 hours. Tube designers are confronted with the problem of finding settings for a large number of design parameters that are optimal with respect to several simulated tube characteristics. This problem can be modeled as a so-called high-cost nonlinear programming problem. This paper reports on the successful application of our four-step compact model approach to solve this problem. The presented results are based on four projects in which we optimized picture tube parts. Among the realized benefits for Philips are a design improvement of 30% and a time-to-market reduction of 50–60%.     

The SEXTANT software: A tool for automating the comparative analysis of mental models of dynamic systems

The comparison of mental models of dynamic systems improves our understanding of how people comprehend, interpret, and subsequently influence dynamic management tasks. Approaches to comparing mental models currently used in managerial and organizational cognition research, especially the distance-ratio and the closeness-approaches, have been criticized for not considering essential characteristics of dynamic managerial situations. This paper builds on a recent analysis method developed to compare mental models of dynamics systems, and introduces this mathematical approach to management and organizational researchers by means of the SEXTANT software. It presents the process of mental model elicitation, analysis, comparison, and interpretation. An example with four elicited mental models illustrates the software’s features to analyze and present the results. Then, the software is compared with existing software to map and compare mental models. Our conclusion is that SEXTANT marks a significant step in enabling large-scale studies about mental models of dynamic systems.     

Discrete Multiattribute Utility Approach to Project Selection

The purpose of this paper is to review various structures of the project-selection problem with discrete multiattribute utility. The approach of maximizing the utility for project selection is discussed where the utility function cannot be measured on a continuous scale. The review includes reference to models of uncertainty versus certainty, and to models assuming the existence of dependence and complementary relations among projects versus independent projects. Project selection in hierarchical organizations with a large number of projects and group decisions is also referred to. The various types of models are formulated and analysed, including applications in the areas of water resources, R&D and nuclear plant location. Finally, directions for future research are suggested.     

Interactive Optimization Aspects of Electronic Spreadsheet Models for Design and Planning

This paper extends the discussion of the relationship between optimization and electronic spreadsheet modelling. Previous authors have described two connections. These are, first, the use of spreadsheets to facilitate optimization projects and, second, the use of optimization within the spreadsheet model itself such as for cell value computation. In this paper we argue that use of the spreadsheet model for planning and design problems often may actually be viewed as interactive multi-criterion optimization per se. This provides a third connection between optimization and spreadsheet models. We base our arguments on a simplified example arising in insurance consulting work and note other examples as well. We also propose a method for incorporating this observation into existing spreadsheet models and indicate desirable innovations in spreadsheet software.     

VTS雷达站选址优化研究

雷达站选址及雷达配置方案对于提高整个船舶交通管理系统(简称VTS)的运行性能起到重要作用。针对当前VTS雷达站选址模型因较少考虑水域风险和环境遮挡等相关因素而较难进行实际应用的问题,提出了一种基于水域单元风险评价的VTS雷达站选址方法。该方法充分考虑水域风险和环境遮挡等环境因素,利用ArcGIS软件对水域进行划分、风险评价和视域分析,并建立雷达站选址配置双目标优化模型,设计多目标粒子群算法进行求解。最后,通过对肇庆市的VTS雷达站选址项目进行验证分析,结果表明最终方案能够满足模型约束并实现优化,在考虑水域风险、环境遮挡的情况下提高了VTS雷达站的运行效果,推动VTS雷达站选址模型的实际应用。     

基于Fuzzy-Rough-TOPSIS的工程项目投标风险评价模型研究

在工程项目招投标阶段,项目风险评价是建筑施工企业进行投标决策和作出项目选择的重要依据.基于理想化与主客观相结合的思想,提出了一种确定风险评价指标综合权重的方法;采用梯形模糊数处理模糊性信息,并将其与粗集理论和TOPSIS(a technique for order preference by similarity to ideal solution)方法相融合,建立了工程项目风险评价的Fuzzy-Rough-TOPSIS模型.实例运行表明,模型可操作性强,适用于多个项目的风险分析和比较,并能够在一定程度上克服以往模型存在的主观性强、应用条件限制严格等不足.     

Stability analysis of polynomial fuzzy models via polynomial fuzzy Lyapunov functions

In this paper, the stability of continuous-time polynomial fuzzy models by means of a polynomial generalization of fuzzy Lyapunov functions is studied. Fuzzy Lyapunov functions have been fruitfully used in the literature for local analysis of Takagi-Sugeno models, a particular class of the polynomial fuzzy ones. Based on a recent Taylor-series approach which allows a polynomial fuzzy model to exactly represent a nonlinear model in a compact set of the state space, it is shown that a refinement of the polynomial Lyapunov function so as to make it share the fuzzy structure of the model proves advantageous. Conditions thus obtained are tested via available SOS software.     

Selection among ranked projects under segmentation,policy and logical constraints

The paper proposes a method for project selection under a specific decision situation, where a final selection is guided by two aspects: (1) satisfaction of certain segmentation, policy and/or logical constraints, and (2) assurance that the individual evaluation of the projects is respected to the maximum degree. This approach is somewhat different than the usual portfolio optimization, where combinations of projects are compared without special concern on respecting the project’s ranking. The entire process is implemented in two phases: the projects are first ranked, usually through a multicriteria approach. The obtained complete preorder of the projects is then used in an integer programming module in order to effectively drive the final selection that satisfies the segmentation and/or logical constraints. The innovative part of the proposed approach is the way it overcomes the well-known bias towards low cost projects which is caused by the knapsack formulation commonly used in the integer programming phase. Actually this is the main source of divergence between the final selection and the initial complete preorder of the projects. The proposed method improves an agreement between the final selection of projects obtained from the integer programming model and the ranking obtained from the multicriteria approach.     

基于模糊理论的软件寿命周期费用评价模型

软件寿命周期费用评价模型涉及到软件开发、使用和维护过程中各种资源最有效利用的权衡分析。由于软件开发不是一门严谨的精确科学,往往存在大量具有不确定性的需求以及许多未知和不确定因素,所有这些都给软件寿命周期费用评价带来模糊效用。本文将模糊理论应用于软件寿命周期费用的评价,给出了从评价属性模糊值的确定、模糊评价模型的建立,到模型求解和最优方案选择的模糊评价方法,并通过对一个算例的分析,证明了该模型的可行性。     

MEASURING THE COSTS OF REALLOCATING WATER FROM AGRICULTURE: A MULTI‐MODEL APPROACH

ABSTRACT. Increasing demand for water by environmental interests, coupled with the diminishing prospects for constructing new water diversion projects, is forcing farmers in the western United States to cope with reduced surface water deliveries. The cost of improving instream water quality by reducing agricultural diversions is shown to depend mainly on how the supply reductions are allocated among users and on the extent of water trading. A central contribution of this paper is a methodology for measuring the impacts of water supply policy reforms on irrigated agriculture. The paper nests three empirical models in a general conceptual framework. The models differ in terms of their degree of detail and assumptions about input substitution. By comparing model results, it is possible to place bounds on the consequences of policy changes, and to identify critical factors determining economic impacts. The models are applied to the problem of improving water quality in the San Francisco Bay/Delta estuary.     

Risk Engineering: Basic Controlled Interval and Memory Models

Risk engineering models for decision making under uncertainty are illustrated by a succession of increasingly complex example applications. The approach is based upon simple arithmetic procedures which are easy to understand and use. Offshore North Sea projects are the basis for the example applications, but the approach has been used in a range of problem environments in the U.K., Canada and U.S.A. The emphasis is a flexible structure for viewing problems which allows modelling at a level of detail and with a degree of quantification tailored to the needs of the context. The concern is gaining useful insight, not model solution optimisation.