Multi-criteria Decision Support Research Articles

Managing Urban Plant Invasions: a Multi-Criteria Prioritization Approach.

Alien plant invasions in urban areas can have considerable impact on biodiversity and ecosystem services (ES). Managing urban plant invasions is particularly challenging given the complex interactions between ecological, economic and social elements that exist in the urban milieu. Strategic landscape-scale insights are crucial for guiding management, as are tactical site-scale perspectives to plan and coordinate control efforts on the ground. Integrating these requirements to enhance management efficiency is a major challenge. Decision-support models have considerable potential for guiding and informing management strategies when problems are complex. This study uses multi-criteria decision tools to develop a prioritization framework for managing invasive alien plants (IAPs) in urban areas at landscape and local scales. We used the Analytic Hierarchy Process (AHP; a multi-criteria decision support model) to develop and rank criteria for prioritising IAP management in the City of Cape Town (CoCT), South Africa. Located within a global biodiversity hotspot, Cape Town has a long history of alien plant introductions and a complex socio-political make-up, creating a useful system to explore the challenges associated with managing urban plant invasions. To guide the prioritization of areas for IAP management across the CoCT, a stakeholder workshop was held to identify a goal and criteria for consideration, and to assess the relative importance given to each criterion in IAP management. Workshop attendees were drawn from multiple disciplines involved with different aspects of IAP research and management: government departments, scientists and researchers, and managers with a diverse set of skills and interests. We selected spatial datasets and applied our multi-criteria decision analysis in a Geographic Information System (GIS) to develop a landscape-scale prioritization map. To address issues relevant in an urban setting, we also modified an existing IAP management framework to develop a tactical (site-level) prioritization scheme for guiding on-the-ground control operations. High-priority sites for IAP management were identified at landscape- and local scales across the study area. Factors related to safety and security emerged as pivotal features for setting spatially-explicit priorities for management. The approach applied in this study can be useful for managers in all urban settings to guide the selection and prioritization of areas for IAP management.

A semiotic-inspired machine for personalized multi-criteria intelligent decision support

The need for appropriate decisions to tackle complex problems increases every day. Selecting destinations for vacation, comparing and optimizing resources to create valuable products, or purchasing a suitable car are just a few examples of puzzling situations in which there is no standard form to find an appropriate solution. Such scenarios become arduous when the number of possibilities, restrictions, and factors affecting the decision rise, thereby turning decision makers into almost mere spectators. In such circumstances, decision support systems (DSS) can play an important role in guiding people and organizations towards more accurate decision making. However, conventional DSS lack the necessary adaptability to account for dynamic changes and are frequently inadequate to tackle the subjectivity inherent in decision-maker's preferences and intention. We argue that these shortcomings can be addressed by a suitable combination of Semiotic Theory and Computational Intelligence algorithms, which together can make up a new generation of DSS. In this article, a formal description of an Intelligent Semiotic Machine is provided and tried out in practical decision contexts. The results obtained show that our approach can provide well-suited decisions based on user preferences, achieving appropriateness while fanning out subjective options without losing decision context, objectivity, or accuracy.

Spatial multicriteria decision support for robust land-use suitability: The case of landfill site selection in Northeastern Greece

Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of geographical information systems (GIS) and multiple criteria decision aid (MCDA) methods for incorporating conflicting objectives and decision makers’ preferences into spatial decision models. In this paper, we present spatial UTASTAR (S-UTASTAR), a raster-based MC-SDSS for land-use suitability analysis. The multicriteria component of the system is based on the UTA-type disaggregation-aggregation approach. S-UTASTAR is applied in a raster-based case study concerning land-use suitability analysis to identify appropriate municipal solid waste landfill (MSW) sites in Northeast Greece. Moreover, robustness analysis tools are implemented to guarantee robust decision support results. More specifically, during the aggregation phase, the Stochastic Multiobjective Acceptability Analysis (SMAA) is used to indicate the frequency at which a site achieves the best ranking positions within a large set of alternative landfill sites.

A practical application approach to argumentation for multicriteria analysis and decision support

This paper explores, through an application example, connections between argumentation and another type of methods often involved in decision problems, namely, multicriteria analysis. A novel argumentative analysis framework is proposed and applied to assess a short supply chain device for food aid, regarding different categories of criteria. The analysis leads to some feedback concerning argumentation systems.

A multi-criteria decision support system to rank sustainable desalination plant location criteria

Due to globally increased demand of drinkable and usable water, countries are focusing more in building desalination plants. Desalination plant location selection is a strategic decision and decision makers need to deal with multiple criteria that are sometimes conflicting in nature. In addition to technical and operational aspects, sustainability (social, environmental, economical) aspect needs to be considered in desalination plant location decision. The purpose of this paper is to develop a multi-criteria decision support system (DSS) to rank desalination plant location criteria in United Arab Emirates (UAE) by considering social, environmental, economical, technical and operational aspects. Result shows that technical (21.9%) and economical (20.9%) are top two main aspects of desalination plant location criteria. Similarly, waste water discharge (22.2%), life species (13.3%), real cost of water and government subsidy (18%), quality and quantity of fresh water (12.4%), and water supply network (9%) are the top ranked sub-criteria of environmental, social, economic, technical, and operational aspects respectively.

A Multi-Criteria Approach for Assessing the Sustainability of Small-Scale Cooking and Sanitation Technologies

To reduce the consumption of firewood for cooking and to realise recycling-driven soil fertility management, three projects in Northwest Tanzania aim to provide the local smallholder community with cooking and sanitation alternatives. The present study proposes an integrated approach to assess the sustainability of the small-scale cooking and sanitation technologies. Based on the multi-criteria decision support approach (MC(D)A), we developed a decision-specific, locally adapted, and participatory assessment tool: the Multi-Criteria Technology Assessment (MCTA). Pre-testing of the tailored tool was set up with representatives of Tanzanian and German partners of case study projects. From a methodological perspective, we conclude that the MCTA uses a set of relevant criteria to realise a transparent and replicable computational Excel-tool. The combination of MC(D)A for structuring the assessment with analytical methods, such as Material Flow Analysis, for describing the performance of alternatives is a promising path for designing integrated approaches to sustainability assessments of technologies. Pre-testing of the tool served as a proof-of-concept for the general design of the method. Future applications and adjustments of the MCTA require the inclusion of end-users, a reasonable and participatory reduction of criteria, and an increase of feedback loops and group discussions between participants and the facilitator to support a common learning about the technologies and thorough understanding of the perspectives of participants.

Multi-criteria decision support system with negotiation process for vehicle technology selection

Decision Makers (DMs) in vehicle technology transportation planning, seek to understand the impact of a given technology on energy use and GHG emissions. There are decision making processes to evaluate technology solutions to meet policy maker requirements, others that evaluate technologies to satisfy consumer requirements, and others that evaluate technologies that provide the automaker with a profit. However, there does not appear to be a decision making process that incorporates all such evaluations into one model. The ideal process should incorporate economic evaluation, market diffusion and government regulations. The aim of this paper is to try and help get closer to such an ideal process, by developing an interactive Decision Support System (DSS), that incorporates as much pertinent information and modeling results published in the available literature as possible. The DSS will have all DM inputs incorporated and generate a ranking of technologies and/or scenario alternatives. The generated results will initiate a negotiation process that will help DMs understand the tradeoffs between different key parameters. Thus, the tool will enable a qualitative and quantitative comparison that provides important information to DMs, to systematically define preferred vehicle design and/or technology scenario to meet the requirements of policy, society, industry and consumers.

A risk-based approach applied to system engineering projects: A new learning based multi-criteria decision support tool based on an Ant Colony Algorithm

This article proposes a multi-criteria decision support tool fully integrated within system engineering and project management processes that allows decision makers to select an optimal scenario of a project. A model based on an oriented graph includes all the alternative choices of a new system’s conception and realization. These choices take into account the risks inherent to perform project tasks in terms of cost and duration. The model of the graph is constructed by considering all the collaborative decisions of the different actors involved in the project. This decision support tool is based on an Ant Colony Algorithm (ACO) for its ability to provide optimal solutions in a reasonable amount of time. The model developed is a multi-objective new ant colony algorithm based on an innovative learning mechanism (named MONACO) that allows ants to learn from their previous choices in order to influence the future ones. The objectives to be minimized are the total cost of the project, its global duration and the risk associated with these criteria. The risk is modeled as an uncertainty related to the increase of the nominal values of cost and duration. The optimization tool is a part of an integrated and more global process, based on industrial standards (the System Engineering process and the Project Management one) that are widely known and used in companies.

Multi-criteria fuzzy decision support for conceptual evaluation in design of mechatronic systems: a quadrotor design case study

Designing mechatronic systems is known to be a very complex and tedious process due to the high number of system components, their multi-physical aspects, the couplings between the different domains involved in the product, and the interacting design objectives. This inherent complexity calls for the crucial need of a systematic and multi-objective design thinking methodology to replace the often-used sequential design approach that tends to deal with the different domains and their corresponding design objectives separately leading to functional but not necessarily optimal designs. Thus, a new approach based on a multi-criteria profile for mechatronic systems is presented in this paper for the conceptual design stage. Additionally, to facilitate fitting the intuitive requirements for decision-making in the presence of interacting criteria, three different methods are proposed and compared using a case study of designing a vision-guided quadrotor drone system. These methods benefit from three different aggregation techniques such as Choquet integral, Sugeno integral and fuzzy-based neural network. To validate the decision yielded by the results of global concept score for each aggregation methods, a computer simulation of a visual servoing system on all design alternatives for quadrotor drone has been performed. It is shown that although the Sugeno fuzzy can be a useful aggregation function for decisions under uncertainty, but the approaches using Choquet fuzzy and fuzzy integral-based neural network seem to be more precise and reliable in a multi-criteria design problem where interaction between the objectives cannot be overlooked.

Toward a Decision Support System for Regulation in an Urban Transport Network

In this article, a solution to the problem of perturbation in the urban transport network has been proposed. This solution is based on the multicriteria decision support method, which is an efficient way of identifying appropriate solutions to different perturbations situations. The proposed model should provide synthesis, evaluation and updating of available information in order to facilitate the task of the network monitoring operator. To achieve this objective, the authors propose formal modeling of the perturbation concept through an effective and above all significant decision support system exploiting the diversity of the criteria as well as the decision maker's subjectivity. This modeling makes it possible to capitalize the knowledge available within a checkpoint and to monitor the process. The authors show how, from the information available on the network, the modeling of the process represented by a perturbation makes it possible to enrich the possibilities of evaluation of the state of the network.

Decision support model for the selection of asphalt wearing courses in highly trafficked roads

The suitable choice of the materials forming the wearing course of highly trafficked roads is a delicate task because of their direct interaction with vehicles. Furthermore, modern roads must be planned according to sustainable development goals, which is complex because some of these might be in conflict. Under this premise, this paper develops a multi-criteria decision support model based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution to facilitate the selection of wearing courses in European countries. Variables were modelled using either fuzzy logic or Monte Carlo methods, depending on their nature. The views of a panel of experts on the problem were collected and processed using the generalized reduced gradient algorithm and a distance-based aggregation approach. The results showed a clear preponderance by stone mastic asphalt over the remaining alternatives in different scenarios evaluated through sensitivity analysis. The research leading to these results was framed in the European FP7 Project “DURABROADS” (No. 605404).

DECISION-MAKING FOR FLEXIBLE MANUFACTURING SYSTEMS’ TECHNOLOGY CHOICE

The purpose of this paper is to present a method for decision-making support for technology´s choice in Flexible Manufacturing Systems (FMS). The object is a manufacturing cell of the automotive industry. The research method is the qualitative-quantitative modeling. In the qualitative part, specialists in automation engineering created three technological scenarios for the implementation of the FMS. In the quantitative part of the research, six members of the management team used a multicriteria decision support method to distribute importance among competitive priorities and to assess how much each technological scenario can influence each competitive priority. The research also proposes two calculation methods for the final merit of the scenarios. According to both methods, the best scenario was the third one, based on the use of robots. The study also concluded that the adoption of the third scenario is better than do nothing, i.e., continuing with the current manual installation.

A research challenge vision regarding management of agricultural waste in a circular bio-based economy

Agricultural waste is a huge pool of untapped biomass resources that may even represent economic and environmental burdens. They can be converted into bioenergy and bio-based products by cascading conversion processes, within circular economy, and should be considered residual resources. Major challenges are discussed from a transdisciplinary perspective, focused on Europe situation. Environmental and economic consequences of agricultural residue management chains are difficult to assess due to their complexity, seasonality and regionality. Designing multi-criteria decision support tools, applicable at an early-stage of research, is discussed. Improvement of Anaerobic Digestion (AD), one of the most mature conversion technologies, is discussed from a technological point of view and waste feedstock geographical and seasonal variations. Using agricultural residual resources for producing high-value chemicals is a considerable challenge analysed here, taking into account innovative eco-efficient and cost-effective cascading conversion processes (bio-refinery concept). Moreover, the promotion of agricultural residues-based business is discussed through industrial ecology, to promote synergy, on a local basis, between different agricultural and industrial value chains. Finally, to facilitate a holistic approach and optimise materials and knowledge flows management, the connection of stakeholders is discussed to promote cross-sectorial collaboration and resource exchange at appropriate geographic scales.

Accuracy Assessment of Different Digital Surface Models

Digital elevation models (DEMs), which can occur in the form of digital surface models (DSMs) or digital terrain models (DTMs), are widely used as important geospatial information sources for various remote sensing applications, including the precise orthorectification of high-resolution satellite images, 3D spatial analyses, multi-criteria decision support systems, and deformation monitoring. The accuracy of DEMs has direct impacts on specific calculations and process chains; therefore, it is important to select the most appropriate DEM by considering the aim, accuracy requirement, and scale of each study. In this research, DSMs obtained from a variety of satellite sensors were compared to analyze their accuracy and performance. For this purpose, freely available Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m, Shuttle Radar Topography Mission (SRTM) 30 m, and Advanced Land Observing Satellite (ALOS) 30 m resolution DSM data were obtained. Additionally, 3 m and 1 m resolution DSMs were produced from tri-stereo images from the SPOT 6 and Pleiades high-resolution (PHR) 1A satellites, respectively. Elevation reference data provided by the General Command of Mapping, the national mapping agency of Turkey—produced from 30 cm spatial resolution stereo aerial photos, with a 5 m grid spacing and ±3 m or better overall vertical accuracy at the 90% confidence interval (CI)—were used to perform accuracy assessments. Gross errors and water surfaces were removed from the reference DSM. The relative accuracies of the different DSMs were tested using a different number of checkpoints determined by different methods. In the first method, 25 checkpoints were selected from bare lands to evaluate the accuracies of the DSMs on terrain surfaces. In the second method, 1000 randomly selected checkpoints were used to evaluate the methods’ accuracies for the whole study area. In addition to the control point approach, vertical cross-sections were extracted from the DSMs to evaluate the accuracies related to land cover. The PHR and SPOT DSMs had the highest accuracies of all of the testing methods, followed by the ALOS DSM, which had very promising results. Comparatively, the SRTM and ASTER DSMs had the worst accuracies. Additionally, the PHR and SPOT DSMs captured man-made objects and above-terrain structures, which indicated the need for post-processing to attain better representations.

Land Suitability Analysis as Multi Criteria Decision Making to Support the Egyptian Urban Development

Sustainability in urban development is considered as a main concrete stone that effect directly the quality of life for its users. Land Suitability Analysis (LSA) using GIS as a multi criteria support tool reveals the best alternatives for the suitability of sustainable land development. Urban planners working under the umbrella of sustainability using recent technology should contribute their work directly to LSA. This paper aims to develop a new technique to be used by planner to reach best alternative for five main urban sectors (agriculture, Industry, Trade, Tourism, & Residential) using GIS as a multi criteria decision support tool (MCDS), accordingly choosing best city location will be accurately and analyzed upon LSA studies. LSA and MCDS are going to be applied on one survey unit map called Monof along Cairo – Alexandria Road. Results showed that different alternatives could be applied on the area of interest, and all of them are sustainable, but choosing the best deepened on the priority of querying the development sector. The paper suggests a pilot method for land development planning and choosing best city location that would be a guide for the governmental planning organization to support in taking right and analyzed planning decisions.

A multi-criteria decision support model for optimal cotton fibre blending

As cotton is a natural fibre, its different physical properties are likely to immensely vary depending on the variations in its seed type, area where it is grown and the climatic conditions. This may affect the characteristics of the final products. Blending is a process of combining different cotton fibres together to synergize their physical properties. For successful blending, it is always required to determine the types of the constituent fibres in the final mix and their appropriate proportions. In this paper, a multi-criteria optimization model in the form of a decision support framework is developed while integrating preference ranking organization method for enrichment of evaluations (PROMETHEE II and V) and geometrical analysis for interactive aid (GAIA) approach. This model is observed to have great potentiality in ranking the considered cotton fibre alternatives from the best to the worst, identifying the top ranked cotton fibres and finding out the constituent fibres in the final blend along with their proportions. Two cotton fibre varieties, such as the Egyptian and Ethiopian types are considered here to demonstrate the applicability of the developed model.

Prevention of landfill pollution by multicriteria spatial decision support systems (MC-SDSS): development, implementation, and case study.

Landfilling of municipal solid waste (MSW) is one of the serious environmental concerns as improper location of MSW landfill site can release the pollutants into the surrounding environment. The process of selecting MSW landfill site is a complicated decision making problem since it is subjected to simultaneous assessment of several environmental criteria, rules, and restrictions besides sociocultural and economic ones. The current study suggests a framework based on Multicriteria spatial decision support systems (MC-SDSS) to select landfill site. The MC-SDSS is an advanced method to integrate multiple criteria decision analysis (MCDA) and geographical information systems (GIS) techniques. This approach enables the incorporation of several conflicting objectives and preferences into spatial decision models. In this study, 14 criteria were chosen and then divided into environmental, sociocultural, and economic categories. Finally, suitability maps were generated based on the MC-SDSS analysis. The developed method was implemented in a real case study in Arak city in northwestern region of Iran, which is environmentally sensitive area. The suitability maps of the case study in Arak showed that 10% (391km2) is least suitable area, 23% (942km2) is low suitable, 37% (1507km2) is moderate suitable, 19% (783km2) is suitable, and 11% (489km2) is most suitable locations for landfill site, and finally, three best alternative sites were introduced for the final landfill site.

Prospects for Nordic intensive forest management solutions in the Republic of Karelia

In this study, the prospects for future forest management in Republic of Karelia, Russia were analyzed. Forestry has an important role in the economy of Karelia. However, productivity and profitability in the forestry sector are extremely low, forest stand structure and quality are weak, the commercial forest land of coniferous species has declined and the wood processing industry struggles with a deficit of raw materials. The situation is typical to many forest regions in Russia with extensive forest management cited as one reason for the current situation. In contrast, the Nordic countries have significant experience in intensive and sustainable forest management and the results have been to a large extent positive. The transfer of Nordic intensive forest management solutions (NIFMS) could improve forestry in Karelia. SWOT analysis, combined with the multi-criteria decision support (MCDS) method was used to identify local operational environments and to assign priorities. Major threats included unprepared regulations, poor road infrastructure, insecure investments, low forestry productivity, forest degradation, high investment costs and a negative attitude to intensive forestry. The main opportunities are high forest resource potential in Karelia, favorable authority development programs, proven Nordic expertise, wood-based energy development and availability of new technology. Results also showed that the main weaknesses that might influence the NIFMS in Karelia are slow return on investments, low market demand for energy wood, high costs associated with young forest thinnings, high demand for skilled specialists and a lack of investment in research and development.

Spatial and economic conditions of the solar energy use in single-family houses - a case study

The provisions in the European and Polish law oblige to increase the use of energy obtained from renewable energy sources. In order to meet these demands, it is necessary to take into account the singlefamily housing sector that has a significant energy potential. Among the unconventional heat sources that have gained the highest popularity, it is worthy to note installations using solar energy for instance to prepare DHW (domestic hot water) or be used in buildings’ heating. It is also worth mentioning that this type of technical solutions can be successfully used as well in new as modernized old residential buildings. Our paper discusses the problems associated with planning of solar installations with flat and vacuum solar collectors mounted on the roofs of the single-family buildings. During the analysis we used GIS&T technology, multi-criteria decision support as well as detailed spatial data obtained by modern remote sensing techniques including airborne laser scanning. The proposed methodology and tools were used on the example of the selected part of a single-family housing estate located in Bialystok. The research focused on issues of the multi-criteria assessment of the assembly of use of roofs for solar collectors, including data on a solar potential, exposure, slope, surface and roofs’ shape. Additionally, we took into account roof zones on being in a shadow caused by roof elements, roof infrastructure and high vegetation. Based on the simulation results we analyzed possibility of plate and tube solar collectors’ application that would provide warm water for 1-4 habitants of houses. The results of our research showed high utility of the proposed methods in the field of spatial and economic assessment of the use of solar collectors in the single-family housing sector.

Towards a Group Decision Support System

This research focuses on the design and development of an interactive multi-criteria group decision support system (GDSS). This system comprises a set of agents communicating through messages. They aim to find an adequate area that can best address certain criteria among a set of variants (e.g. localization of new infrastructure, shopping mall, hospital, etc.). A GDSS is designed and relies mainly on the presentation of the territory through the functionalities of geographic information system (GIS), and the representation of the multiplicity and diversity of decision makers through the functionalities of a multi-agents system (MAS). Moreover, an argumentation-based negotiation protocol MAS module is provided. This approach is justified by the argumentative nature of the process by which participants justify their choices in order to reach a collective decision. This is achieved by exploiting the multi-criteria methods with an analytic hierarchy process (AHP) and Electre III.