ANALYSIS OF CRITERIA SYSTEM MODEL FOR CONSTRUCTION CONTRACT EVALUATION

One of the major accompaniments of the globalization is the rapid growing of urban areas. At the end of the 1970th only 38% of world lived in cities, this number increased to more than 50% by 2008. In 2030 two third of all people worldwide are expected to live in cities, many of them in megacities. Urban sprawl is a major environmental concern affecting cities and urban. Urban sprawl depends on the socio-economic situation in the cities. Thus, reducing migration, sustainable handling of the limited resources and smart growth are acknowledged as key tasks for urban planning. Coping with these tasks requires precise and adaptive planning instruments. The presented study is part of the research project GAUS (Gaining Additional Urban Space) aiming at inventorying the open space available in urban environments and, moreover, providing flexible multi-criteria spatial decision support system for its development. The method is based on VHR (Very high resolution) optical satellite data (QuickBird (QB) and IKONOS (IK)) which is applied on three study areas: Berlin, Istanbul, and Ruhr Area. Object-based image analysis is applied to map land cover and land use and derive metrics describing urban form and inner-urban structure on multiple scales. The workflow has been standardized and leads to comparable results across different test sites and datasets. In intersection with available GIS (Geographical Information System) and local ancillary data, the outputs of image analysis serve as input for a multi-criteria spatial decision support system. Flexible multi-criteria spatial decision support (MC-SDSS) tool has been created by using MATLAB (Matrix Laboratory) software and its tools (Mapping toolbox etc.). Users can change their weights and parameters with this tool for their different study areas. Urban planners can use final suitability maps of this tool. Thus complex decisions are supported by numerical calculation and spatial visualization in order to come to objective solutions. This work contribute to close the gap between remote sensing methods and applied urban planning. © 2011 IEEE.

This paper presents a fuzzy multicriteria group decision making approach for evaluating and selecting information systems projects. The inherent subjectiveness and imprecision of the evaluation process is modeled by using linguistic terms characterized by triangular fuzzy numbers. A new algorithm based on the concept of the degree of dominance is developed to avoid the complex and unreliable process of comparing fuzzy numbers usually required in fuzzy multicriteria decision making. A multicriteria decision support system is proposed to facilitate the evaluation and selection process. An information systems project selection problem is presented to demonstrate the effectiveness of the approach.KeywordsInformation systems project selectionMulticriteria decision makingFuzzy numbersDecision support systems

ABSTRACTWith the increased frequency of natural hazards and disasters and consequent losses, it is imperative to develop efficient and timely strategies for emergency response and relief operations. In this paper, we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management. It combines a high-performance computing environment (cyberGIS-Jupyter) and multi-criteria decision analysis models (Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution Model (TOPSIS)) with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation. Social media data (e.g. Twitter data) was used as an additional tool to support the decision-making process. Our case study involves two decision goals generated based on a past flood event in the city of Austin, Texas, U.S.A. As our result shows, WSM produces more diverse values and higher output category estimations than the TOPSIS model. Finally, the model was validated using an innovative questionnaire. This cyberGIS- enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers, where different emergency responders can formulate their decision objectives, select relevant evaluation criteria, and perform interactive weighting and sensitivity analyses.

The Decision Support Systems (DSS) were widely applied to the field of Multi-Criteria Decision Aid (MCDA). In fact, the most popular MCDA aggregating procedures are those supported by a DSS. Our literature review reveals that the MultiCriteria Decision Support Systems(MCDSS) have applied in several fields such as : Production and Operation Management , Finance, Education, Human Resources, Real Estate and Multi Media. The aim of this paper is to provide a state-of-the Art and a classification by field of MCDSS applications.

BackgroundIn the energy transition framework towards decarbonization, green hydrogen, obtained through water electrolysis powered by renewable energy, is gaining importance. In order to pave the way to its production and trade, it is required to assess its main advantages and challenges, which are not only energy-related but involve also techno-economic, social, environmental, and geopolitical aspects. In line with this, the current article aims to provide a Multi-criteria Spatial Decision Support System to investigate the suitability of North African countries with respect to the production of solar-based hydrogen and its potential trade, and to apply it to Tunisia, as one of the most promising countries for becoming a competitive hydrogen exporting leader. Combining the Analytic Hierarchy Process and the Geographical Information System, this study focuses on evaluating the land suitability for solar hydrogen production at a country level, serving as the foundation for a methodology applicable across the entire North African region. After defining ten different criteria, these are spatially analysed and then prioritized according to different experts’ preferences, so that a final suitability map is obtained. The added value of the study is the inclusion of social and geopolitical criteria in this kind of assessments, often focused on techno-economic parameters alone.ResultsThe suitability map allows to classify the majority of the Tunisian areas as moderately or highly suitable, even if the most favourable areas in terms of availability of resources are often negatively influenced by the geopolitical or economic assessment. The sensitivity analysis has also proved the high suitability of Tunisia, with no areas assessed as very low suitable even if the different criteria are extremized.ConclusionsAmong the several influencing factors addressing the suitability for green hydrogen uptake, this article makes it possible to explore the social and geopolitical externalities, as well as the environmental and techno-economic dimensions. Even if stakeholders’ preferences affect the final results, the sensitivity analysis makes it possible to test their robustness. Supporting the adoption of new clean technologies towards the carbon-neutrality target, the methodological framework could be applied for other countries and also tailored on other specific technological pathways.

The paper presents a multicriteria analysis decision support system called MultiChoice, designed to support decision makers in solving different multicriteria analysis problems. Various well-known methods and software systems are discussed. The basic features of the solving modules, the interface modules and the system modules are described.

Almost all decisions people make are based on multiple factors or criteria. Decision makers generally pursue multiple, and often conflicting, objectives. A feasible solution that is optimum with respect to all such objectives or decision criteria almost never exists, and a satisfactorily compromised solution is generally sought. Multi-criteria decision analysis as a field of research deals with problems, theory and solution approaches directly involving multiple decision criteria. This minitrack focuses on solution approaches, technology, and systems that support decision-making under consideration of multiple decision criteria.

Every design engineer has an experience of facing common problems of short lead times, clear specification, sufficient design criteria, material costing, environmental regulations, and product reliability constraints. However, for maintaining sustainable growth of a company, a new product should be time-in-market that keeping business growth strategy. Under such environment, it is clear that more comprehensive multi-criteria decision support systems are required to assist the designer to develop green product to achieve a goal in short period of time. This highlights the need for a more effective smart system that will collect, analyze and provide solution to design engineer. Overall, a variety of artificial intelligent (AI), multi-criteria decision support (MCDS) system, expert system, design for environment (DfE) methods are available, however they limited in their application to solve the life cycle assessment (LCA) problems and design problems in early stage of design with uncertain data in early stage of design. By examination the research focus, this study aims an understanding of environmental impact and how far from design criteria. Furthermore, investigating the LCA of green product and generating fundamental research information on their environmental impact and product reliability. With the availability of faster and more user-friendly computers and sophistical software, it has been possible to use simulation modeling to investigate the parameter effectively. The significance of this research is that development of framework by using LCA, theory of Inventive of Problems solving (TRIZ) to measure and examine product life cycle assessment, inventive problems solver.

A proposed Multi Criteria Analysis decision support tool for international environmental policy issues: a pilot application to emissions control in the international aviation sector

Multiple Criteria Decision Analysis/Making (MCDA/MCDM) is constantly increasing its presence in various scientific fields but also in new fields of its application. This expansion became possible mainly due to the integration and implementation of its methodologies in the Decision Support Systems (DSS). This enabled their successful applications in many new fields resulting in their further expansion as Multi-Criteria Decision Support Systems (MCDSS). Their combination with Artificial Intelligence (intelligent agents, recommender systems, expert systems) in general and especially in recent years with the field of Machine Learning and Data Mining has led to the development of Intelligent Multi-Criteria Decision Support Systems (IMCDSS) with enormous application in analytics. At the same time, MCDA was used to solve various problems and to develop new methodologies in the field of Machine Learning. The aim of this speech is to present on the one hand the theoretical issues and methodologies of the cooperation of the Multi-Criteria DSS with the Artificial Intelligence and on the other hand to present a series of such IMCDSS and their applications in various fields such as marketing, health, defense, agricultural, etc.

In many European countries, the noise protection infrastructure (e.g. mainly noise barriers and more generally noise reducing devices) is constantly expanded as volumes of traffic increase and public awareness of noise grows. This often involves building materials with a large carbon footprint such as concrete, steel or glass contradicting the European goals of decarbonization. More recently, noise reducing devices have been introduced that create additional environmental and social benefits through greening or energy production in attached photovoltaic systems. This results in an optimisation problem addressed in this paper: With which infrastructure can we achieve the required noise protection and the greatest possible additional benefit for people and the environment at minimum cost and with minimal greenhouse gas emissions? To solve this optimization problem, we introduce a multicriteria decision support system for sustainable design of noise reducing devices. It considers the systems performance in noise protection as well as its carbon footprint, its life-cycle costs and additional environmental and social benefits. It uses expert knowledge on different systems performances in noise protection (based on empirical data), Life Cycle Assessment for building materials (including transport and construction processes) and Life Cycle Costing. In this study the multicriteria decision support system is applied to evaluate different types of noise reducing devices including conventional systems such as steel supporting structures with panels as well as “green” barriers and earth barriers. The results show which general design parameters influence the overall performance of a noise protection system regarding different sustainability goals. Based on our results we highlight the application of the multicriteria decision support system at different design stages and discuss design choices at each stage, data requirements and limitations due to data uncertainties.

There are well-known issues in conjunction with eliciting probabilities, utilities, and criteria weights in real-life decision analysis. This article explores various computationally efficient methods for generating weights in multi-criteria decision support systems. Therefore, it constitutes an aid for MCDA modellers and tool designers in selecting surrogate methods for criteria weights. Given the challenges in eliciting precise criteria weights from decision-makers, this study evaluates a range of techniques for automatically generating surrogate weights, focusing on both ordinal and cardinal ranking approaches. With a thorough inquiry methodology never before used, we examine automatic multi-criteria weight-generating algorithms in this article. The methods tested include traditional rank-based models such as rank sum (RS), rank reciprocal (RR), and rank order centroid (ROC), alongside newer approaches like the sum reciprocal (SR) and cardinal sum reciprocal (CSR). The results show that the SR approach for the ordinal case and the CSR method for the cardinal case perform better in terms of robustness than other methods, even including the promising new geometric class of methods. It is also shown that linear programming (LP) performs poorly when compared to surrogate weight models. Additionally, as expected, the cardinal models perform better than the ordinal models. Unexpectedly, though, the well-established LP model’s performance is worse than previously thought.

Technological advancements have improved solar energy generation and reduced the cost of installing photovoltaic (PV) systems. However, challenges such as low energy-conversion efficiency and the unpredictability of electricity generation due to shading or climate conditions persist. Despite decreasing costs, access to solar energy generation technologies remains limited. This paper proposes a multi-criteria decision support system (MCDSS) for selecting the most suitable PV set (comprising PV modules, inverters, and batteries) for microgrid installations. The MCDSS employs two multi-criteria decision-making methods (MCDM) for analysis and decision-making: AHP and TOPSIS. The system was tested in two case studies: Barreiras, with a global efficiency of 14.4% and an internal rate of return (IRR) of 56.0%, and Curitiba, with a worldwide efficiency of 14.8% and an IRR of 52.0%. The research provided a framework for assessing and selecting PV sets based on efficiency, cost, and return on investment. Methodologically, it integrates multiple MCDM techniques, demonstrating their applicability in renewable energy. Managerially, it offers a practical tool for decision-makers in the energy sector to enhance the feasibility and attractiveness of microgeneration projects. This research highlights the potential of MCDSS to improve the efficiency and accessibility of solar energy generation.

Contracts accompany all stages of the life cycle of a construction object. The contractual relationship should ensure a balance of production and economic interests of numerous participants in the construction project and stakeholders of the project. When a project is being implemented by several dozen contractors, it is difficult to achieve a balance of interests, the content of contracts is often multidirectional or contradicts each other. In order to systematize the relationships of the project participants, contractual relations in the construction industry and the functions of the subjects of contracts are analyzed. The structure of contracts in construction, based on the cash flow of the project, is proposed. Allocated 6 groups of contracts: agreements concluded by the investor; contracts concluded by the developer, employer; contract agreements; contracts for the supply of goods and the provision of services; collective and individual labor contracts. The main features of each group of agreements and the functions of the subjects of agreements in Russia and abroad are considered. It is shown that, as part of the theory of project management, it is relevant and expedient to develop a section on the structure of contracts, planning a contract campaign and support for a complex of project contracts. It is proposed to develop contracts and think through the contractual terms of the project agreed, under the leadership of the project management team.

Part 1 Theoretical and conceptual issues: spatial multicriteria decision analysis developing web-based GIS/MCE - improving access to data and spatial decision support tools spatial dimensions in multicriteria analysis. Part 2 Locational decision-making and analysis: equitable approaches to location problems application of a multiple criteria trade-off approach to spatial decision-making GDSS in highway planning - their use and adoption urban land management in transition economies - a decision support prototype for Shanghai the strategic identification of suitable land for low income housing - a case study from South Africa South Africa patterns of inequality and reform relating to socio-economic status and access to services. Part 3 Systemic decision-making and analysis: apres moi le deluge - multicriteria problems in multiregional water resources decision-making distributed knowledge, distributed processing, distributed users - emerging methodologies and case based reasoning for decision-making using traditional environmental knowledge and GIS geographic tools for decision-making in watershed management GIS-approach for land suitability assessment in developing countries - the case study of forest development project in Mexico exploring the potential of multi-criteria spatial decision support systems - a system for sustainable land-use planning and design.