Multi-criteria Decision Support System Research Articles

Multi-Criteria Decision Support System for Recommendation of PhD Supervisor

A decision support system (DSS) is a computerized system used to discover determinations, judgments, and courses of action within a business or an organization. In every arena of life, DSS play an important role. In higher education, DSSs are recognized for diverse reasons, for example to conjugate data and intelligence, to pull off the unrivaled and likely interpretations, and to fine-tune decisions under hesitancy. The PhD supervisorselection is itself a very difficult and mind tickling task which makes the student very nervous and sometime make him desperate because he is unable to make the right decision. This work will facilitate the scholars to get proper guidance to make it successful. For the selection of particular supervisor there are some set of criterions which a supervisor should also follow while selecting a particular scholar. Though, the lack of information about the supervisor can hamper scholars in making the selection of the supervisor. Correspondingly, the identification of thought-provoking criteria might be stimulating for potential scholars due to their level of immaturity. Therefore, a system is required which can facilitate scholars in selecting the research work advisors in accordance with the research topic based on multi-criteria like relevancy of the supervisor’s research area of interest and the relevance of the publications. In this research effort, a user-friendly conceptual DSS framework has been suggested to recommend the Ph.D. supervisor to scholars in academics. A multi-criteria DSS framework has been proposed for the facilitation of the scholars while selecting their Ph.D. supervisor. This recommendation is based on several criteria of selecting potential supervisor including the area of research interest along with the publications i.e., in journals and conferences in addition to number of Ph.Ds. produced so far. Finally, the publications of the Ph.D. produced so far and the research projects involved. Preliminary results of the proposed work has been discussed along with the future directions.

A Multi Criterion Decision Approach for Identification of Potential Flood Risk Zones in Hussainsagar Watershed using Geomatics- A Case Study

Urban flooding is becoming a major threat in the recent times due to changes in the land use land cover leading to chaos, loss of livelihood, damage to property, infrastructure and utilities. The main objective of the present study is to assess the current state of land use, assess the impact of these temporal changes on the basin hydrology over the past few years. The study was initiated to identify how increase in impervious layer due to construction activities over a decade have significantly increased the potential flood risk zones in and around the Hussainsagar catchment using multi criterion decision support system using GIS and RS techniques. From the analysis it was observed that there is an increase of almost 32.6% in the built-up area from 104.05 km2 to 154.39 km2,38.20% reduction in open space, 51.75% in agricultural and, 14.10% decrease in vegetation cover, of the total catchment area between the years 2005 and 2019. These changes in the land use have not only altered the basin hydrology but also augmented the extent of potentially high flood risk zones in the proposed study leading to frequent flooding over the past few years.

Municipal decision-making for sustainable transportation: Towards improving current practices for street rejuvenation in Canada

Many cities have adopted plans or policies to attain sustainable transportation objectives, including the redesign and rehabilitation of streets that favor active and public transportation. However, the implementation of any sustainable transportation plan is often riddled with various organizational and communicational obstacles. In order to understand the reasons behind these obstacles, a qualitative research study was conducted with professionals employed in eleven cities, ranging in size from 40,000 to 500,000 inhabitants, in the province of Quebec, Canada. The objective was to paint a picture of current decision-making practices, to describe the characteristics of the participants’ ideal decision-making process and to explore the suitability of spatial multicriteria decision support systems within sustainable transportation planning. Based on the findings, a series of guidelines are proposed to improve the integration of sustainable transportation concepts in street rejuvenation decision-making process.

Multi-Criteria Decision Support System for Smart and Sustainable Machining Process

Sustainatableble development assumes the meeting of humanity’s everyday needs and development goals while sustaining the ability of nature to provide the resources and ecosystem on which the economy and society depend. It means that an increase of economic benefit cannot be a single optimization problem anymore, instead, the multi-criteria approach is used with the accent on ecology and social welfare. However, it is not easy to harmonize these aims with machining, which is a well known industrial pollutant. On the other hand, new industrial paradigms such as Industry 4.0/5.0, are driving toward the smart concept that collects data from the manufacturing process and optimizes it in accordance with productivity and/or ecologic aims. In this research, the smart concept is used through the development of the multi-criteria decision support system for the selection of the optimal machining process in terms of sustainability. In the case of milling process selection, it has been demonstrated that green machining, without a multi-criteria approach, will always remain an interesting research option, but not a replacement for conventional machining. However, when applying realistic ecological and social criteria, green machining becomes a first choice imperative. The multi-criteria decision-making PROMETHEE method is used for the comparison and ranking, and validation of results is made through criteria weights sensitivity analysis. The contribution of this concept is that it could also be applied to other manufacturing processes.

Fuzzy - Based Multi - Criteria Decision Support System for Maintenance Management of Wastewater Treatment Plants

Abstract Wastewater treatment plants (WWTPs) contain a large number of components, and these components in turn require a large number of maintenance activities and high costs. In this paper, a Fuzzy-based Multi-Criteria Decision Support System (FBMCDS) model was designed based on the failure mode and effect analysis FM-EA and applied to the Rustumiya Project (RP) in Iraq. Information of the RP’s components, failure modes, applied maintenance activities and costs were collected from the documented data, site visit and face-to-face interviews as well as opinions of 44 experts. Through applying the fuzzy logic to analysis the failure modes and effects, the risk priority index (FMRPI) and total intensity (FMTI) of each failure mode ware computed. Thus, maintenance priorities and weights of the RP’s failure modes were specified. In addition, the best maintenance plan was specified based on the FMRPI-FMTI diagram that shows the importance and the type of maintenance required for each failure mode. Failure of the submersible pump has the first rank in terms of priority, while the last priority was occupied by the building failure mode, where the mechanical failure modes are of the highest importance, followed by the electrical failure modes. Finally, the construction failure modes are ranked last due to the small probability of failure. The designed model is considered an efficient tool due to the similarity of the results with the reality of the situation and the ease of reading and displaying the results. In addition, this model can be applied to other projects such as water treatment plants and irrigation projects.

Toward a web-based multi-criteria decision support system for the layered evaluation of interactive adaptive systems

Toward a web-based multi-criteria decision support system for the layered evaluation of interactive adaptive systems

Potential measurement of Iran's western regional wind energy using GIS

Energy is essential for socio-economic development and due to the increase in energy consumption it is necessary to look for renewable and clean energy over the coming decades. Wind energy has received more attention than other sources due to better economic conditions as well as lack of environmental pollution and unlimited wind resources. This paper describes a practical method for measuring the potential of wind resources with the ability to generate electricity, using the geographic information system and considering the selection criteria of wind farms. The present study analyzed a multi-criteria decision support system to define wind energy resources in western Iran. In the method of calculating wind potential in this paper, the desired items are considered with equal importance and these criteria include technical, environmental, economic and geographical factors. The aims of this research are measuring the potential of areas suitable for generating electricity from wind energy for large turbines according to international standards, stating the standards and criteria for measuring the potential of wind resources and finally implementing the items mentioned in regional electricity. The results show that assuming the use of Gamesa G58 turbine, a maximum of 1897 MW of wind power can be generated in the study area, which is 26% of the region's electricity supply in 2025 horizon. The method used in this study can be applied for future wind potential measurements and it is recommended to be integrated with fuzzy AHP method to enhance the accuracy of the results.

GIS-Based Multi-Criteria Decision-Support System and Machine Learning for Hospital Site Selection

The selection of hospital sites is one of the most important choice a decision maker has to take so as to resist the pandemic. The decision may considerably affect the outbreak transmission in terms of efficiency , budget, etc. The main targeted objective of this study is to find the ideal location where to set up a hospital in the willaya of Oran Alg. For this reason, we have used a geographic information system coupled to the multi-criteria analysis method AHP in order to evaluate diverse criteria of physiological positioning , environmental and economical. Another objective of this study is to evaluate the advanced techniques of the automatic learning . the method of the random forest (RF) for the patterning of the hospital site selection in the willaya of Oran. The result of our study may be useful to decision makers to know the suitability of the sites as it provides a high level of confidence and consequently accelerate the power to control the COVID19 pandemic.

Application of fuzzy analytic hierarchy process and linear goal programing for selection of best available techniques of the cold rolled coil manufacturing processes: A case study in Binh Duong, Vietnam

The cold rolled coil manufacturing is one of the steel making processes with great environmental concerns due to the high energy consumption and greenhouse gas emissions, significant wastewater discharge, and hazardous waste disposal. A large number of technical solutions have been introduced to improve energy efficiency and environmental sustainability in the steel rolling sub-sector, particularly the best available techniques (BATs) adopted by European IPPC Bureau (2001). In this study, fuzzy analytic hierarchy process and linear goal programing are combined in the development of a multi-criteria decision support system for evaluating and selecting best available techniques (BATs) in the cold rolled coil manufacturing industry. The proposed approach comprises of four steps and results in the rankings of the potential BATs for the cold rolled coil installations. In a case study of a cold rolling mill in Vietnam, amongst eleven BATs that were recommended for the installation, two BATs of the highest priority were further analyzed, including: (1) improving the spent degreasing baths and the alkaline wastewater treatment system using membrane technology; and (2) recovering spent emulsion to supply fuels for the steam boiler. It is expected that the application of these BATs can enhance environmental performances and significantly reduce energy cost for the steam boiler during its operation.

Policies for Rapid Mitigation of the Crisis’ Effects on Agricultural Supply Chains: A Multi-Criteria Decision Support System with Monte Carlo Simulation

This paper proposes an integrated approach towards rapid decision-making in the agricultural sector aimed at improvement of its resilience. Methodologically, we seek to devise a framework that is able to take the uncertainty regarding policy preferences into account. Empirically, we focus on the effects of COVID-19 on agriculture. First, we propose a multi-criteria decision-making framework following the Pugh matrix approach for group decision-making. The Monte Carlo simulation is used to check the effects of the perturbations in the criteria weights. Then, we identify the factors behind agricultural resilience and organize them into the three groups (food security, agricultural viability, decent jobs). The expert survey is carried out to elicit the ratings in regard to the expected effects of the policy measures with respect to dimensions of agricultural resilience. The case of Lithuania is considered in the empirical analysis. The existing and newly proposed agricultural policy measures are taken into account. The measures related to alleviation of the financial burden (e.g., credit payment deferral) appear to be the most effective in accordance with the expert ratings.

A decision support approach to achieve competitive advantage for a hypermarket chain

This paper describes the decision support approach used in the development process of the S Group's Prisma hypermarket chain in Finland. The management was looking for a new and sustainable operating model for the rapidly growing chain, and contacted the author to consult in the process. Fierce competition forced the search for new business ideas, tools and methods that would provide a clear competitive advantage. To find new perspectives, we decided to use statistical approaches and various decision support system options, such as multi-criteria modelling. A database was available for research and analysis, including data on purchasing behavior and key performance indicators (KPI). The approach had to take into account the role and impact of customers. It was highly important to include customer behavior in the analysis using shopping basket data. Shopping basket data was central in the current paper. From these, an observation matrix was created combining shopping basket data, product data and customer background information. Using multivariate methods, customer groupings and profiles were created with the data from the observation matrix. Using the customer profile and KPI data, a multi-criteria decision support system was produced to support strategic planning. The decision support system (DSS) model was created together with a market chain operational expert and an external methodological expert. We used the VIG software package developed by Korhonen (Belg J Oper Res Stat Comput Sci 27(3):15, 1987) to solve the problem because it is easy to use and requires no prior knowledge of computers or multi-objective linear programming models. Pareto Race plays a central role in the VIG system. The chain expert easily learned how to use and work with the model. The results were immediately visible and could be used to examine alternatives and assess their appropriateness. It was decided to present five different scenarios to the hypermarket chain management. The main objective of the development process was to develop a strategy that would provide the Prisma hypermarket chain with a long-term competitive advantage. Various models were developed and used to support the strategy work by analysing and exploring the data collected, prioritising and selecting decision options. Two currently retired managers (Mönkkönen, S Group, the chain manager, Prisma chain, Interview 02.06.2021, 2021), who were involved in the development process, rated the strategy process as very successful and the modelling carried out during the process significantly supported decision-making. The immediate help of DSS modelling for decision making comes from being able to provide decision makers with reasonable, better solution options to support their decision making. The final impact of decisions could be evaluated after a longer period of time, which in the case of the Prisma development project results means several comparable financial years. Finland suffered exceptionally badly from the financial crisis and the global economic downturn in 2008–2009. The Prisma chain has survived the periods and crises described above without any loss-making years, and the whole chain has grown from 16 units in 1992 to 68 units in 2020.

Similarity Analysis of Methods for Objective Determination of Weights in Multi-Criteria Decision Support Systems

Decision support systems (DSS) are currently developing rapidly and are increasingly used in various fields. More often, those systems are inseparable from information-based systems and computer systems. Therefore, from a methodical point of view, the algorithms implemented in the DSS play a critical role. In this aspect, multi-criteria decision support (MCDA) methods are widely used. As research progresses, many MCDA methods and algorithms for the objective identification of the significance of individual criteria of the MCDA models were developed. In this paper, an analysis of available objective methods for criteria weighting is presented. Additionally, the authors presented the implementation of the system that provides easy and accessible weight calculations for any decision matrix with the possibility of comparing results of different weighting methods. The results of weighting methods were compared using carefully selected similarity coefficients to emphasise the correlation of the resulting weights. The performed research shows that every method should provide distinctive weights considering input data, emphasising the importance of choosing the correct method for a given multi-criteria decision support model and DSS.

Web-Based DSS for Resource Allocation in Higher Education

The allocation of scarce resources is a complex higher education decision problem, especially when it comes to budget constraints. Therefore, the authors propose a multicriteria web-based decision support system for resource allocation in higher education organizations. To do so, they define an MCDA/M resource allocation model, based on a project portfolio selection problem to set the percentage of the total budget that every alternative should receive. For the web-based DSS, they develop a database model to store and retrieve data, define the user's interface, and they use a web platform to transform the prototype into a web-based system. Also, they run an empirical analysis with an end-user to test the DSS. They show that the system can provide a clear vision of how the resource allocation system works; the mechanism as a whole becomes more transparent to those involved, enabling them to make efficient and reasonable decisions.

Land suitability determination for citrus cultivation using a GIS-based multi-criteria analysis in Mersin, Turkey

Determination of suitable locations for agricultural products is an important subject to study, as it is essential to identify optimal areas for crop productivity and sustainable farming activities. This study proposes a site selection assessment model for citrus cultivation using Geographic Information System (GIS) Based Multi-Criteria Decision Analysis (MCDA) in the Mersin province of Turkey. The purpose of the study is to create a Multi-Criteria Assessment-Based Spatial Decision Support System which takes into account the opinions of local citrus farmers as well as those of experts. 14 preference factors and 10 exclusion factors were determined for the analysis of land suitability for citrus farming. The weighting of preference factors for citrus cultivation was determined using the Analytic Hierarchy Process, and a land suitability map was generated using the weighted overlay method in the GIS environment. Based on the opinions of farmers and experts, higher weights were assigned to the average temperature in the flowering period, the average temperature in the winter season, and elevation, as compared to all other preference factors. The results also demonstrate that WorldClim, a global climate dataset, is an essential source of data in such studies, as climate conditions play a crucial role in potential citrus expansion. The final land suitability map shows that the most suitable and moderately suitable areas for citrus cultivation have surface areas of 85 824 ha and 85 430 ha, respectively. The Receiver Operating Characteristic (ROC) curve was used to evaluate validation of the land suitability for citrus and the Area Under the Curve (AUC) value was calculated at 0.87, indicating substantial consistency. This study proposes a methodology for the determination of suitable sites for citrus cultivation which relies on realistic data. The land suitability map produced in the study will be essential for the creation of sustainable agriculture strategies in the eastern Mediterranean region.

A Multi-Criteria Decision Support System for Strategic Planning at the Swiss Forest Enterprise Level: Coping With Climate Change and Shifting Demands in Ecosystem Service Provisioning

Sustainable forest management plays a key role for forest biodiversity and the provisioning of ecosystem services (BES), including the important service of carbon sequestration for climate change mitigation. Forest managers, however, find themselves in the increasingly complex planning situation to balance the often conflicting demands in BES. To cope with this situation, a prototype of a decision support system (DSS) for strategic (long-term) planning at the forest enterprise level was developed in the present project. The DSS was applied at three case study enterprises (CSEs) in Northern Switzerland, two lowland and one higher-elevation enterprise, for a 50-year time horizon (2010 to 2060) under present climate and three climate change scenarios (‘wet’, ‘medium’, ‘dry’). BES provisioning (for biodiversity, timber production, recreation, protection against gravitational hazards and carbon sequestration) was evaluated for four management scenarios (no management, current (BAU), lower and higher management intensity) using a utility-based multi-criteria decision analysis. Additionally, four alternative preference scenarios for BES provisioning were investigated to evaluate the robustness of the results to shifting BES preferences. At all CSEs, synergies between carbon sequestration, biodiversity and protection function as well as trade-offs between carbon sequestration and timber production occurred. The BAU management resulted in the highest overall utility in 2060 for different climate and BES preference scenarios, with the exception of one lowland CSE under current BES preference, where a lower intensity management performed best. Although climate change had a relatively small effect on overall utility, individual BES indicators showed a negative climate change impact for the lowland CSEs and a positive effect for the higher elevation CSE. The patterns of overall utility were relatively stable to shifts in BES preferences, with exception of a shift toward a preference for carbon sequestration. Overall, the study demonstrates the potential of the DSS to investigate the development of multiple BES as well as their synergies and trade-offs for a set of lowland and mountainous forest enterprises. The new system incorporates a wide set of BES indicators, a strong empirical foundation and a flexible multi-criteria decision analysis, enabling stakeholders to take scientifically well-founded decisions under changing climatic conditions and political goals.

Land Suitability Assessment for Olive Mill Wastewater Disposal by Integrating Multicriteria Decision Support Tools

Many on-site waste disposal systems fail regularly due to problems concerning suitable location and management. A potential environmental threat is inevitably propagated through on-site, off-site, downstream, soil surface and ground water pollution. Soil is a key component of land suitability for waste disposal. This paper presents a Geographic Information Systems (GIS) – based integrated multicriteria decision support system for evaluating the land suitability for olive mill wastewater (OMWW) disposal in the Mediterranean region. Two-scaled classification schemes are developed, the global scheme for Central and South Greece (scale: 1:30.000) and the local scheme for the study area in Xiromero, Aetolia-Acarnania Prefecture, Western Greece, scale 1:10.000. Constrains and factors are included into the spatial decision-making framework, where geostatistical and fuzzy set theory techniques, as well as Analytical Hierarchy Process (AHP) are appropriately integrated. Physical, chemical, and socioeconomic variables adapted to the Mediterranean soil conditions are incorporated as driving forces for the land suitability assessment and the produced maps reveal valuable results for final end-users, such as municipal authorities, agriculturalists, farmers and other national and local stakeholders.

Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems

Many complex problems require a multi-criteria decision, such as the COVID-19 pandemic that affected nearly all activities in the world. In this regard, this study aims to develop a multi-criteria decision support system considering the sustainability, feasibility, and success rate of possible approaches. Therefore, two models have been developed: Geo-AHP (applying geo-based data) and BN-Geo-AHP using probabilistic techniques (Bayesian network). The ranking method of Geo-APH is generalized, and the equations are provided in a way that adding new elements and variables would be possible by experts. Then, to improve the ranking, the application of the probabilistic technique of a Bayesian network and the role of machine learning for database and weight of each parameter are explained, and the model of BN-Geo-APH has been developed. In the next step, to show the application of the developed Geo-AHP and BN-Geo-AHP models, we selected the new pandemic of COVID-19 that affected nearly all activities, and we used both models for analysis. For this purpose, we first analyzed the available data about COVID-19 and previous studies about similar virus infections, and then we ranked the main approaches and alternatives in confronting the pandemic of COVID-19. The analysis of approaches with the selected alternatives shows the first ranked approach is massive vaccination and the second ranked is massive swabs or other tests. The third is the use of medical masks and gloves, and the last ranked is the lockdown, mostly due to its major negative impact on the economy and individuals.

Modeling a Multi-Criteria Decision Support System for Prequalification Assessment of Construction Contractors using CRITIC and EDAS Models

Contractor prequalification assessment in the construction industry is an essential part of the project development process because contractors play a pivotal role in the extension of projects and resources. The main objective of the present study is comprised prequalification assessment for classifying contractors by applied the EDAS method for recognizing the contractors' potential before competitive tendering and obtaining bids. First, an inclusive, detailed list of 56 sub-factors under 5 main factors for project prequalification was compiled following a thorough literature review, and review of contractors by experts of Bandar Imam Khomeini municipality who already have done projects with contractors. Second, used the CRITIC method for obtained the weighing and importance of each factor. Third, classified the contractors by applied the EDAS system for recognizing the contractors' potential before competitive tendering and obtaining bids. Finally, the prequalification assessment process was developed to obtaining the rank of each contractor and help the stakeholders to select the right contractors. The effectiveness of the present approach was tested by applying it to a case study of the prequalification assessment of four construction companies' in Bandar Imam Khomeini municipality, Khuzestan, Iran. It is worth mentioning that the prequalification assessment by the proposed approach is approved by the project stakeholders and is consistent with their expectations. It can be concluded that based on relevant ranking and weighing of companies that procedure can be extended to the same studies in this regard, and the contribution of the present study is to propose a support system for prequalification and identification of contractors' ability, before assigning projects to companies for success in projects.

Modeling of an active multi-agent environment for the design of a multi-criteria group decision support system

In the present study, the research problem concerns business intelligence, more precisely collaborative decision-making. The authors propose a complete modeling of a multi-agent active environment for the design of a multicriteria group decision support system dedicated to the spatial problem of localization in territory planning. The proposed model is called ActiveGDSS (Active Group Decision Support System) which uses a coupling between a geographic information system and a multi agents system and is endowed by a new negotiation protocol based on the concession allowing reaching to a consensus which satisfies the territorial actors. The main purpose is to integrate the principle of contextual activation in the modeling of the system which makes the environment an active entity. The main advantages of contextual activation are efficiency gain in terms of execution, better flexibility and reuse of agent behaviors.

Techno-economic evaluation and optimization of CCGT power Plant: A multi-criteria decision support system

A key objective of the power generation industry is to achieve maximum economic benefit without over-consuming the life of power plants and over-maintaining its assets. From a CCGT power plant operator’s perspective, the stand-alone performance analysis of the plant is not enough to support the decision-making process due to the plethora of possible scenarios characterized by variable ambient conditions, engine health (fouling, erosion), electricity prices, and power demand. This study proposes a novel methodology to support decision-making for a CCGT power plant’s operational optimization. The comprehensive techno-economic performance evaluation is conducted by multidisciplinary optimization and decision-making to enhance information integration for the combined cycle power plant operated by Manx Utilities in the Isle of Man, UK. The decision support system has the capability to recommend the optimal operation schedules to plant operators. It recommends that the more severely degraded engine should run at a relatively lower power setting to decrease creep life consumption. The established power plant optimization framework has the potential to assist power plant operators in deciding the total power output and power split between gas turbines based on optimization results that considers both immediate thermo-economic benefits and life consumption. Finally, the proposed system can facilitate similar power plants to adjust daily operations to achieve thermo-economic and lifing benefits.