Establishment and optimization of an evaluation index system for brownfield redevelopment projects: An empirical study

Brownfield redevelopment has recently become the focus of attention of governments, communities, environmental advocates, scientists, and researchers around the world. The purpose of this study is to provide a framework for establishing and optimizing an evaluation index for brownfield redevelopment projects (BRPs). This framework involves three steps: the initial design, testing and optimization, and verification. With the help of two standard statistical software packages, the reliability and validity of the initialized index system are established, and then the optimization of the initial index system is carried out by means of Factor Analysis. The effectiveness of the optimization of the index system is verified through Structural Equation Modeling. Furthermore, an illustration example is used to show how to apply the established index system in the real world.     

A decision support system for strategic asset allocation

Strategic asset allocation is a crucial activity for any institutional or individual investor. Given a set of asset classes, the problem concerns the definition and management over time of the best asset mix to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. Although a considerable attention has been placed by the scientific community to address this problem by proposing sophisticated optimization models, limited effort has been devoted to the design of integrated framework that can be systematically used by financial operators. The paper presents a decision support system which integrates simulation techniques for forecasting future uncertain market conditions and sophisticated optimization models based on the stochastic programming paradigm. The system has been designed to be accessed via web and takes advantages of the increased computational power offered by high performance computing platforms. Real-world instances have been used to assess the performance of the decision support system also in comparison with more traditional portfolio optimization strategies.     

A decision support system for constructing an alert classification model

As the rapid growth of network attacking tools, patterns of network intrusion events change gradually. Although many researches had been proposed to analyze network intrusion behaviors in accordance with low-level network data, they still suffer a large mount of false alerts and result in difficulties for network administrators to discover useful information from these alerts. To reduce the load of administrators, by collecting and analyzing unknown attack sequences systematically, administrators can do the duty of fixing the root causes. Due to the different characteristics of each intrusion, none of analysis methods can correlate IDS alerts precisely and discover all kinds of real intrusion patterns. Therefore, an alert-based decision support system is proposed in this paper to construct an alert classification model for on-line network behavior monitoring. The architecture of decision support system consists of three phases: Alert Preprocessing Phase, Model Constructing Phase and Rule Refining Phase. The Alert Processing Phase is used to transform IDS alerts into alert transactions with specific data format as alert subsequences, where an alert sequence is a kind of well-aggregated alert transaction format to discover intrusion behaviors. Besides, the Model Constructing Phase is used to construct three kinds of rule classes: normal rule classes, intrusion rule classes and suspicious rule classes, to filter false alert patterns and analyze each existing or unknown alert patterns; each rule class represents a set of classification rules. Normal rule class, a set of false alert classification rules, can be trained by using sequential pattern mining approach in an attack-free environment. Intrusion rule classes, a set of known intrusion classification rules, and suspicious rule classes, a set of novel intrusion classification rules, can be trained in a simulated attacking environment using several well-known rootkits and labeling by experts. Finally, the Rule Refining Phase is used to change the classification flags of alert sequence across different time intervals. According to the urgent situations of different levels, Network administrators can do event protecting or vulnerability repairing, even or cause tracing of attacks. Therefore, the decision support system can prevent attacks effectively, find novel attack patterns exactly and reduce the load of administrators efficiently.     

Microcomputer based multiple criteria decision support system for strategic planning

There are two major approaches currently used for developing Decision support Systems (DSS) for strategic planning, especially in the objective formulation stage. Several mathematical models have been developed to abstract the decision situation. However, they do not take into account either behavioral aspects of decision making or the presence of multiple and conflicting objectives. A second approach is to consider the several qualitative factors that go into decision making; such considerations are normally situation-dependent and hence it is difficult to provide a system for general managerial situations.The Multiple Criteria Decision Making (MCDM) approach combines the advantages of both the approaches and, therefore, is an excellent alternative for designing DSS. This paper develops an MCDM approach to strategic planning. The model is applied to such a problem in a simulated environment and the problem is solved interactively. Our experience shows that the proposed methodology is a viable approach for solving practical decision problems in strategic planning.     

An integrative methodology to improve brownfield redevelopment planning in Chinese cities: A case study of Futian, Shenzhen

Brownfields are real property, the expansion, redevelopment, or reuse of which may be complicated by the presence or potential presence of a hazardous substance, pollutant, or contaminant (USEPA, 2002). In recent years, there have been a rising number of brownfield redevelopment practices in Chinese cities. However, some redevelopment practices have been unsuccessful in spite of cautious planning whereas others have been successful in the absence of any planning. It is largely due to China’s rapid urbanization on one hand, and inadequate information on the locations and conditions of brownfield on the other. To address the gaps, an integrative methodology is devised based on two frameworks, one for identifying potential instead of actual brownfields, and one for establishing priorities for redevelopment. The first framework identifies potential brownfields through four steps: (1) define input sites; (2) verify environmental liability; (3) confirm tax delinquency; and (4) cross-check with industrial classification code. The second framework prioritize the identified potential brownfield sites with a set of criteria which are selected and weighed based on key interviews and the study of local reference cases. The utility of this methodology is exemplified with the case study of Futian in Shenzhen. Local data including land use data, tax and environmental records of 2005 and the development plan for 2006-2010 are utilized. We conclude that this methodology properly responds to the increasing need of urban planners in making proactive plans for brownfield redevelopment in Chinese cities.     

A strategic decision support system for supply network design and management in the semiconductor industry

This paper presents a strategic decision support system (DSS) which has been conceptualized and designed by SEMATECH^* to assist the large semiconductor manufacturing organization in managing its extensive supply chain network. This DSS has been named “Manufacturing Enterprise Model” or “MEM”. MEM ties each factory and its primary metrics to the rest of the business enterprise to assess how changes in wafer fabrication affect other factories, the distribution system, and customer deliveries. The model is intended to be used to evaluate future factory concepts and to assist business planners in strategic decisions about product allocation and major resource/facility planning.     

A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology

We propose an inventory classification system based on the fuzzy analytic hierarchy process (AHP), a commonly used tool for multi-criteria decision making problems. We integrate fuzzy concepts with real inventory data and design a decision support system assisting a sensible multi-criteria inventory classification. We report on a study conducted in a small electrical appliances company and validate the design of the proposed multi-criteria inventory classification system and its underlying fuzzy AHP model.     

Computer support for strategic organizational decision-making

While information systems continue to be promoted within organizations as tools to support strategic decision-making, there is growing concern over the ability of such systems to model the social and political complexity of the situations to which they are being applied. This paper examines the nature of organizational decision-making and the use of computer-based systems to support this activity. The debate queries the extent to which such artifacts should be allowed to become enmeshed and embedded within the strategic decision-making activities of organizations which operate within increasingly complex environments.     

Multiagent meta-model for strategic decision support

This research describes a meta-model which can generate multiagents system (MAS) of strategic decision support. These MAS profit from new concepts of bargaining with learning to determine the cooperation between agents. They answer an insufficiency of the concept of bargaining in the game theory which is proved in this article. The original framework of the meta-model is characterized by three essential functions provided with a technique of learning: the search for typical plan of decisions adapted to the problem and the search for the coalitions in the spatial and temporal dimension. It allows an adaptation to all the types of strategic decision support.     

A patent intelligence system for strategic technology planning

Patent intelligence—the transformation of content found in multiple patents into technical, business, and legal insight—is considered a key factor in gaining a competitive advantage in technologically competitive business environments. Although keyword-based patent intelligence tools are widely used due to their simplicity and ease of use, they are limited in that they cannot represent key technological concepts and inventive knowledge by relying only on the frequency of occurrence of defined keywords. As a remedy, this paper proposes a Subject–Action–Object (SAO)-based patent intelligence system. SAO structures that can be extracted from textual patent information are known as the expertise and inventive findings of the relevant patent. On the basis of semantic analysis of patent SAO structures, our proposed intelligence system constructs patent maps and patent networks. Building on the maps and networks, the system provides specific functionalities including identification of technology trends and significant patents, detection of novel technologies, and identification of potential infringement. This paper describes the architecture of our proposed patent intelligence system in detail, and illustrates the system’s functionalities using case studies. We anticipate that our proposed system will be incorporated into the technology planning process to assist experts in the formulation of technology strategies.     

A novel multi-parameter support vector machine for image classification

The support vector machine (SVM) classification algorithm has received increasing attention in recent years in remote sensing for land-cover classification. However, it is well known that the performance of the SVM is sensitive to the choice of parameter settings. The traditional single optimized parameter SVM (SOP-SVM) attempts to identify globally optimized parameters for multi-class land-cover classification. In this article, a novel multi-parameter SVM (MP-SVM) algorithm is proposed for image classification. It divides the training set into several subsets, which are subsequently combined. Based on these combinations, sub-classifiers are constructed using their own optimum parameters, providing votes for each pixel with which to construct the final output. The SOP-SVM and MP-SVM were tested on three pilot study sites with very high, high, and low levels of landscape complexity within the Sanjiang Plain – a typical inland wetland and freshwater ecosystem in northeast China. A high overall accuracy of 82.19% with kappa coefficient (κ) of 0.80 was achieved by the MP-SVM in the very high-complexity landscape, statistically significantly different (z-value = 3.77) from the overall accuracy of 72.50% and κ of 0.69 produced by the traditional SOP-SVM. Besides, for the moderate-complexity landscape a significant increase in accuracy was achieved (z-value = 2.44), with overall accuracy of 84.03% and κ of 0.80 compared with an overall accuracy 76.05% and κ of 0.71 for the SOP-SVM. However, for the low-complexity landscape the MP-SVM was not significantly different from the SOP-SVM (z-value = 0.80). Thus, the results suggest that the MP-SVM method is promising for application to very high and high levels of landscape complexity, differentiating complex land-cover classes that are spectrally mixed, such as marsh, bare land, and meadow.     

A decision support system for strategic planning in large-scale marketing channels: An example in the tile industry

This paper presents a decision support system for strategic planning in marketing channels. A dynamic model of the marketing channel is employed which comprises manufacturer and retailer levels. Decision making is achieved through a game theoretical inference mechanism in which each player (manufacturer/retailer) optimises for a long-term profit maximisation objective. Both historical data and managerial expertise are used for the parameterisation of the system's knowledge base. The decision support system provides a forecast of profit and sales and computes pricing and shelf space allocation strategies that maximise long-term profit. It offers facilities such as the study of coalitions, long-term decision-making in all phases of a product's life cycle, the impact of pricing, allocation strategies, production expansion, cost regulation, and others. The operationality of the system is illustrated in decision-making situations in the tile industry.     

A support system for interactive languages

The implementation of a support system for interactive languages is described, which include a controlling routine, a macro processor, lexical and syntactic analysis routines, semantic actions, error handling and analysis, log and code generation routines. There are also utility programs for generating the state tables for the syntax analysis. By using a structured programming approach these routines have been developed very quickly, but can still be developed and enhanced due to a well-defined working environment, a simple approach to coding and reasonable internal documentation.     

A support system for optimization modelling

With widespread use of executive decision support systems, the activity of modelling itself needs to be supported by a user friendly interface. This paper outlines an effort in this direction with respect to optimization modelling using linear programs. The approach is through a synthesis of a modelling language (LAMP) under development, for manipulating the structure, and a commercially available database management system for manipulating the data. Such a synthesis provides a very powerful ‘what if’ analysis capability paving the way for a new generation of modelling systems.     

A strategic knowledge-based planning system for freight forwarding industry

In recent years, over half of the Hong Kong freight forwarding firms experienced a decline in business volume due to the growing challenge from the neighbour ports of Yantian and Shekou in Shenzhen, China, which operated in a much cheaper mode. In order to remain competitive, local freight forwarders in Hong Kong must establish a long-term union relationship with their customers such as the provision of customized logistics services. One of the ways is through the use of a co-loading shipment plan, which is a knowledge intensive and complex process involving multiple knowledge source and decision rules. This paper presents hybrid knowledge and model system, which integrates mathematical models with knowledge rules, in the formulation of such co-loading shipment plans. A strategic knowledge-based planning system, (SKPS) integrates knowledge rules with mathematical model for solving problems of co-loading shipment plans formulation and market constriction prevention, is proposed. The system was implemented in Elite World Logistics Service Limited (EW), a local freight forwarding company, for supporting the planning process of a co-loading shipping plan. The result reveals that both customer retention rate and resource utilization has increased significantly.     

A framework for investments in support building activities (SBAs): support mechanisms and strategic scenarios

The success of a new technology implementation effort is ultimately dependent upon both management and user support for the project. While recent literature is replete with instances in which measures of support have been correlated with measures of success, potential objective analysis has not been well examined. Furthermore, although these effects may be inherently specific to a given organization, existing literature has instead focused on developing industry-wide generalizations. The purpose of the present work is to propose a decision support methodology for pricing and analyzing user/manager support effects on implementation, with associated justifications for investments in Support Building Activities (SBAs).     

Intelligent system for time series classification using support vector machines applied to supply-chain

To be able of anticipate demand is a key factor for commercial success in the supply-chain sector. The benefits can be grouped around two main concepts: firstly the optimization of operations through the development of optimal strategies for procurement and secondly the stock reduction that reduces storage costs, handling, etc. There is currently a variety of methods for making predictions, these methods vary from pure statistical methods such as exponential smoothing Holt-Winters or ARIMA models, to those based on artificial intelligence techniques like neural networks or fuzzy systems. However, despite being able to build accurate models, in managing the supply chain based on forecasts there is a problem known as “Forrester effect” irrespective of the model chosen. To monitor the impact of this effect, given the volume of information handled in large corporations, is a very expensive task (often manual) for such corporations because it requires investigating issues such as the adequacy of the model, allocation of known models to the sales time series, discovery of new patterns of behavior, etc. This article proposes an intelligent system based on support vector machines to solve problems concerning the allocation and discovery of new models. With this focus in mind, the system objective is to build groups of time series that share the same forecasting model. For the identification of new models, the system will assign “virtual models” for those groups that do not have a predefined pattern. Using the proposed method, it has been possible to group a sample of more than 14,000 time series (real data taken from a store) in around 70 categories, of which only 12 of them already grouped over 98% of the total.