This Master´s Thesis focuses on the issue of renewal of fleet in Deblice-lesy, s.r.o. company. The aim of this thesis is selection of suitable semitrailer combination for transportation of long timbers and also selection of truck for transportation of ACTS containers for transportation of firewood, which will correspond to the selected criteria. Selection will be realized by using TOPSIS method. Theoretical part focuses on issues of business in road transport, trucks and combinations in road transport and limits of their dimensions and weights. It also describes some methods of multi-criteria decision making. Practical part focuses directly on selection of suitable semitrailer combination for transportation of long timbers and also selection of truck for transportation of ACTS containers according to the defined criteria. One of the aims is also mapping of the market and choices which exist when selecting combination for transportation of long timbers.

L'aide à la décision multicritères regroupe des méthodes permettant de choisir la meilleure solution en fonction des différents critères et compte tenu des préférences des experts. Toutefois, ces préférences sont parfois exprimées de manière imparfaite. La théorie des fonctions de croyance modélise de manière souple les connaissances et fournit des outils mathématiques pour gérer les différents types d'imperfection. Ainsi dans cette thèse, nous nous intéressons à la prise de décision multicritères dans un cadre incertain en étendant la méthode d’Analyse Hiérarchique des Procédés (AHP) à la théorie des fonctions de croyance. Après avoir présenté les fondements théoriques de la méthode AHP, nous avons proposé une approche qui permet de réduire le nombre de comparaisons par paires en jugeant des sous-ensembles de critères et d’alternatives. En outre, nous avons examiné la dépendance entre les critères et les alternatives. Dans ce cas, l'incertitude au niveau des évaluations est donnée sous forme de masses conditionnelles. Une autre partie de nos travaux répond aux critiques concernant la procédure de comparaison. Pour cela, nous avons proposé deux approches. La première technique d’élicitation des jugements de l’expert est fondée sur des distributions de masses, alors que la seconde s'appuie sur des relations de préférence. Dans ce cadre, nous avons introduit un modèle qui permet de générer des distributions de masse quantitatives à partir des relations de préférence. Ainsi, nous avons développé une méthode multicritères qui permet d'imiter le raisonnement humain. Cette méthode produit des résultats meilleurs et plus robustes que les approches de la littérature. / Multi-criteria decision making is the study of identifying and choosing alternatives to find the best solution based on different criteria and considering the decision makers’ expectations. However, the expert assessments are sometimes expressed imperfectly. Belief function theory can then provide more flexible and reliable tools to manage different types of imperfection. Thus, in this thesis, we are interested in multi-criteria decision making in an uncertain framework by extending the Analytic Hierarchy Process (AHP) method to the belief function framework. After presenting the theoretical foundations of the AHP method, we proposed an approach that reduces the number of pair-wise comparisons by judging subsets of criteria and alternatives. In addition, we examined the dependence between the criteria and alternatives. In this case, the uncertainty is given in terms of conditional mass distributions. Another part of the work provides critical concerning the pair-wise comparison process. For this purpose, we proposed two approaches. The first expert judgment elicitation method is based on mass distributions, while the second one is based on preference relations. In this context, we have introduced a model that is able to generate quantitative mass distributions from preference relations. Thus, we have developed a multi-criteria decision making method that imitates human reasoning. This method gives better and more robust results than existing approaches.

The doctoral thesis deals with the interdisciplinary issue of variants evaluation from real estate engineering, multi-criteria analyses and geographic information systems, and represents the technological ways of the visual aspect related to mutual spatial relations of real estates. The thesis describes the design and verification of methodology dealing with the use of GIS technology in real estate engineering. The primary element of the methodology is the locality as a fundamental determinant of the real estate market objects to which strictly applies a unique characteristic of spatial information. The object location as such is defined by a large number of criteria that need to be spatially analysed. For the needs of spatial analysis was chosen combination of GIS technology with multi-criteria methods (MCDA) which evaluate variants of the problem. Objective determination of a suitable combination of weighting methods and multi-criteria methods is part of the methodology. An objective determination should lead to a reduction in the human factor risk by determining preferences and variants. The proposed procedures are validated on case studies which were dealt with in specific projects. This methodology should be beneficial not only for the criteria evaluation of price comparison in real estate engineering but also for several areas of multi-criteria decision making in terms of space and location-related data.

There has been an increasing relevance of the cultural sector in the economic and social development of different countries. However, this sector continues without much input from multi-criteria decision-making (MDCM) techniques and sustainability analysis, which are widely used in other sectors. This paper proposes an MCDM model to assess the sustainability of a musical institution’s program. To define the parameters of the proposed model, qualitative interviews with relevant representatives of Catalan cultural institutions and highly recognized professionals in the sector were performed. The content of the 2015–2016 season of the ‘Palau de la Música Catalana’, a relevant Catalan musical institution located in Barcelona, was used as a case study to empirically test the method. The method allows the calculation of a season value index (SVI), which serves to make more sustainable decisions on musical season programs according to the established criteria. The sensitivity analysis carried out for different scenarios shows the robustness of the method. The research suggests that more complex decision settings, such as MCDM methods that are widely used in other sectors, can be easily applied to the sustainable management of any type of cultural institution. To the best of the authors’ knowledge, this method was never applied to a cultural institution and with real data. / Universitat Oberta de Catalunya

Multi-Criteria Decision Making (MCDM) problems are often associated with tradeoffs between performances of the available alternative solutions under decision making criteria. These problems become more complex when performances are associated with uncertainty. This study proposes a stochastic MCDM procedure that can handle uncertainty in MCDM problems. The proposed method coverts a stochastic MCDM problem into many deterministic ones through a Monte-Carlo (MC) selection. Each deterministic problem is then solved using a range of MCDM methods and the ranking order of the alternatives is established for each deterministic MCDM. The final ranking of the alternatives can be determined based on winning probabilities and ranking distribution of the alternatives. Ranking probability distributions can help the decision-maker understand the risk associated with the overall ranking of the options. Therefore, the final selection of the best alternative can be affected by the risk tolerance of the decisionmakers. A Group Decision Support System (GDSS) is developed here with a user-friendly interface to facilitate the application of the proposed MC-MCDM approach in real-world multiparticipant decision making for an average user. The GDSS uses a range of decision making methods to increase the robustness of the decision analysis outputs and to help understand the sensitivity of the results to level of cooperation among the decision-makers. The decision analysis methods included in the GDSS are: 1) conventional MCDM methods (Maximin, Lexicographic, TOPSIS, SAW and Dominance), appropriate when there is a high cooperation level among the decision-makers; 2) social choice rules or voting methods (Condorcet Choice, Borda scoring, Plurality, Anti-Plurality, Median Voting, Hare System of voting, Majoritarian iii Compromise ,and Condorcet Practical), appropriate for cases with medium cooperation level among the decision-makers; and 3) Fallback Bargaining methods (Unanimity, Q-Approval and Fallback Bargaining with Impasse), appropriate for cases with non-cooperative decision-makers. To underline the utility of the proposed method and the developed GDSS in providing valuable insights into real-world hydro-environmental group decision making, the GDSS is applied to a benchmark example, namely the California‘s Sacramento-San Joaquin Delta decision making problem. The implications of GDSS‘ outputs (winning probabilities and ranking distributions) are discussed. Findings are compared with those of previous studies, which used other methods to solve this problem, to highlight the sensitivity of the results to the choice of decision analysis methods and/or different cooperation levels among the decision-makers

ABSTRACT Cities throughout Asia have experienced unprecedented economic development over the past decades. In many cases this has contributed to their rapid and uncontrolled growth, which has resulted in a multiplicity of problems, including rapid population increase, enhanced environmental pollution, collapsing traffic systems, dysfunctional waste management, and rapid increases in the consumption of energy, water and other resources. The significant energy use in cities is not very well perceived in Asian countries. Although a number of studies into energy consumption across various sectors have been conducted, most are from the national point of view. Energy demand analysis is not considered important at the level of the city. The thesis is focused on the dynamics of energy utilization in Asian mega-cities, and ultimately aims at providing strategies for maximizing the use of renewable energy in large urban systems. The study aims at providing an in-depth understanding of the complex dynamics of energy utilization in urban mega-centers. An initial general analysis is complemented by a detailed study of the current situation and future outlook for the city of Bangkok, Thailand. An integrated approach applied to the study includes identification of the parameters that affect the utilization of energy in mega-cities and a detailed analysis of energy flows and their various subsystems, including commercial, industrial, residential and that of transportation. The study investigates and evaluates the energy models most commonly used for analyzing and simulating energy utilization. Its purpose is to provide a user-friendly tool suitable for decision-makers in developing an energy model for large cities. In addition, a Multi-Criteria Decision-Making (MCDM) process has been developed to assess whether or not the energy systems meet the sustainability criteria. A metabolic approach has been employed to analyze the energy flow and utilization in selected Asian mega-cities, including Bangkok, Beijing, Shanghai, and Tokyo. The approach is applied to measure the majority of indirect energy flows or the energy embodied in the flows of goods and services involving the residents of those cities. Since the function of cities is to serve the lives of the residents, indirect energy consumption could be regarded as being of equal importance as that of direct energy use. The essence of embodied energy is that an indirect reflection upon behavior following direct energy consumption. It can illustrate how a city relies on the outside, for example other cities, countries, etc. and provides some interesting information that cannot be easily drawn from the direct energy demand. The study reveals that the indirect energy demand is more significant than the direct energy demand in Bangkok, Shanghai, and Tokyo, while direct energy demand is greater than the indirect energy demand in Beijing. This can be explained by the fact that Bangkok, Shanghai, and Tokyo have a greater reliance upon the outside in terms of energy demand. The Long-range Energy Alternative Planning (LEAP) system has been selected to perform Bangkok energy modeling. In a Bangkok case study a range of policy interventions are selected and how these would change the energy development in Bangkok by the year 2025 is examined. Different policies can be grouped by the sectors analyzed. The only supply-side policy considered meets an existing target of having 10% of electricity generated from renewable sources. The study period for the model started in 2005 and ends in 2025, with the year 2000 taken as the base year. The proposed scenarios were evaluated using the MCDM approach to rate their sustainability. Team members found that this method provided a methodology to help decision-makers to systematically identify management objectives and priorities. / QC 20101123

Yes / Purpose - The COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and ser- vices. To mitigate these disruptions, it is essential to identify the barriers that have im- peded the seamless operation of SCs. This study identifies these barriers and assesses their impact on supply chain network (SCN). Design/methodology/approach - To determine the relative importance of different bar- riers and rank the affected industries, a hybrid approach was employed, combining the best-worst method (BWM) and the technique for order preference by similarity to an ideal solution (TOPSIS). To accommodate the inherent uncertainties associated with the pan- demic, a triangular fuzzy TOPSIS was used to represent the linguistic variable ratings provided by decision-makers. Findings - The study found that the airlines and hospitality industry was the most affected by the barriers, accounting for 46% of the total, followed by the healthcare industry (23%), the manufacturing industry (19%), and finally the consumer and retail industry (17%). Research limitation/implications - This study is limited to the four critical industries and nine identified barriers. Other industries and barriers may have different weights and rankings. Nevertheless, the findings offer valuable insights for decision-makers in SC management, aiding them in mitigating the impact of COVID-19 on their operations and enhancing their resilience against future disruptions. Originality/value - This study enhances our understanding of COVID-19’s impact on SCN and provides a framework for assessing disruptions using multi-criteria decision-making processes. The hybrid approach of BWM and TOPSIS in a fuzzy environment is unique and offers potential applicability in various evaluation contexts.

Information technology (IT) investment decision makers are required to process large volumes of complex data. An existing body of knowledge relevant to IT portfolio management (PfM), decision analysis, visual comprehension of large volumes of information, and IT investment decision making suggest Multi-Criteria Decision Making (MCDM) and hypervariate display techniques can reduce cognitive load and improve decision confidence in IT PfM decisions. This dissertation investigates improving the decision confidence by reducing cognitive burden of the decision maker through greater comprehension of relevant decision information. Decision makers from across the federal government were presented with actual federal IT portfolio project lifecycle costs and durations using hypervariate displays to better comprehend IT portfolio information more quickly and make more confident decisions. Other information economics attributes were randomized for IT portfolio projects to generate Balanced Scorecard (BSC) values to support MCDM decision aids focused on IT investment alignment with specific business objectives and constraints. Both quantitative and qualitative measures of participant comprehension, confidence, and efficiency were measured to assess hypervariate display treatment and then MCDM decision aid treatment effectiveness. Morae Recorder Autopilot guided participants through scenario tasks and collected study data without researcher intervention for analysis using Morae Manager. Results showed improved comprehension and decision confidence using hypervariate displays of federal IT portfolio information over the standard displays. Both quantitative and qualitative data showed significant differences in accomplishment of assigned IT portfolio management tasks and increased confidence in decisions. MCDM techniques, incorporating IT BSC, Monte Carlo simulation, and optimization algorithms to provide cost, value, and risk optimized portfolios improved decision making efficiency. Participants did not find improved quality and reduced uncertainty from optimized IT portfolio information. However, on average participants were satisfied and confident with the portfolio optimizations. Improved and efficient methods of delivering and visualizing IT portfolio information can reduce decision maker cognitive load, improve comprehension efficiency, and improve decision making confidence. Study results contribute to knowledge in the area of comprehension and decision making cognitive processes, and demonstrate important linkages between Human-Computer Interaction (HCI) and Decision Support Systems (DSS) to support IT PfM decision making.

A dynamic decision making framework implementing multi criteria routing of multimedia services at private-public network border with access technology convergence is presented. The ingredients of the framework include information model, semantics capturing via ontology, information sharing and dissemination mechanisms and rule/policy specifications methodology. The control and management over the infrastructure is carried out by revamping the sole signaling protocols (SIP, diameter and SNMP). DEN-ng is enhanced and tagged in accordance with the requirements over the underlying framework. A dedicated language for the platform is proposed that has its deep roots inside the framework to avoid conflicts and overlapping. A dynamic decision engine is developed for routing the requests/sessions at private-public network border over the underlying multi-homed environment. Multi Criteria Decision Making (MCDM) theory is used for decision computation/calculation and the adapted methods are exploited according to the scenario and decision computation mode while keeping in view the corresponding enforcement mode. A test bed is developed to validate the proposed framework. The proposed system offers higher throughput and lowers call-dropping probability with an add-on susceptible delay.

The increased demand for biofuels is expected to put additional strain on the available agricultural resources while at the same time causing environmental degradation. Hence, new energy policies need to be formulated and implemented in order to meet global energy needs while reducing the impact of biofuels farming and production. This research focuses on proving a decision support system which can aid the formulation of policies for the sustainable biofuel production. The system seeks to address policy formulation that requires reconciliation of the qualitative aspects of decision making (such as stakeholder’s viewpoints) with quantitative data, which often may be imprecise. To allow this, based on: Fuzzy logic and Multi Criteria Decision Making (MCDM) in the form of Analytical Hierarchy Process (AHP). Using these concepts, three software functionalities, “Options vs. Fuzzy Criteria Matrix”, “Analytical Hierarchy Process” and “Fuzzy AHP” were developed. These were added within the framework of pre-existing base software, Compendium (developed by the Open University, UK). A number of case study based models have been investigated using the software. These models made use of data from the Philippines and India in order to pinpoint suitable land and crop options for these countries. The models based on AHP and Fuzzy AHP were very successful in identifying suitable crop options for India by capturing both the stakeholder viewpoints and quantitative data. The software functionalities are very effective in scenario planning and selection of policies that would be beneficial in achieving a desired future scenario. The models further revealed that the newly developed software correctly identified many of the important issues in a consistent manner.