Bipolar method and its modifications

Bipolar is one of the multiple criteria decision analysis methods, proposed by Konarzewska-Gubała (in Archiwum Automatyki i Telemechaniki 32(4):289–300, 1987). The main feature of the method is that alternatives are not compared directly with each other, but they are confronted to the two reference sets of objects: desirable and non-acceptable. Practical application of the method revealed its shortcomings, therefore improvements of the method were desirable. The aim of the paper is to formulate some modifications of the classical Bipolar approach and consider a case where reference sets are numerous. Unified Bipolar procedure which contains classical Bipolar method as well as the modifications described in the paper is given. Numerical illustrations of the modifications and unified approach are also presented

Qualitative Characteristics and Quantitative Measures of Solution's Reliability in Discrete Optimization: Traditional Analytical Approaches, Innovative Computational Methods and Applicability

The purpose of this thesis is twofold. The first and major part is devoted to sensitivity analysis of various discrete optimization problems while the second part addresses methods applied for calculating measures of solution stability and solving multicriteria discrete optimization problems. Despite numerous approaches to stability analysis of discrete optimization problems two major directions can be single out: quantitative and qualitative. Qualitative sensitivity analysis is conducted for multicriteria discrete optimization problems with minisum, minimax and minimin partial criteria. The main results obtained here are necessary and sufficient conditions for different stability types of optimal solutions (or a set of optimal solutions) of the considered problems. Within the framework of quantitative direction various measures of solution stability are investigated. A formula for a quantitative characteristic called stability radius is obtained for the generalized equilibrium situation invariant to changes of game parameters in the case of the H¨older metric. Quality of the problem solution can also be described in terms of robustness analysis. In this work the concepts of accuracy and robustness tolerances are presented for a strategic game with a finite number of players where initial coefficients (costs) of linear payoff functions are subject to perturbations. Investigation of stability radius also aims to devise methods for its calculation. A new metaheuristic approach is derived for calculation of stability radius of an optimal solution to the shortest path problem. The main advantage of the developed method is that it can be potentially applicable for calculating stability radii of NP-hard problems. The last chapter of the thesis focuses on deriving innovative methods based on interactive optimization approach for solving multicriteria combinatorial optimization problems. The key idea of the proposed approach is to utilize a parameterized achievement scalarizing function for solution calculation and to direct interactive procedure by changing weighting coefficients of this function. In order to illustrate the introduced ideas a decision making process is simulated for three objective median location problem. The concepts, models, and ideas collected and analyzed in this thesis create a good and relevant grounds for developing more complicated and integrated models of postoptimal analysis and solving the most computationally challenging problems related to it.Siirretty Doriast

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously. In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems

Preference Learning

This report documents the program and the outcomes of Dagstuhl Seminar 14101 “Preference Learning”. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies

VIŠECILJNA OPTIMIZACIJA BLAGOSTANJA U ZEMLJAMA ČLANICAMA EU-ROPSKE UNIJE

Well-being is of crucial importance for both individual and society as a whole. It is therefore important to quantify performance and progress made by certain states, regions, communities, social groups, and individuals in improving their well–being. The aim of study was to offer a new framework for multi–criteria assessment as well as international comparison of objective well–being. Well–being is a multi–dimensional phenomenon; hence the appropriate indicator system should be capable to identify the most important underlying processes influencing well–being. For our research we have established the indicator system of twelve indicators identifying various dimensions of well–being. Therefore we propose MULTIMOORA, a model which can be used for approaching the objective of societal well–being. It is applied for international comparison of the well-being in the EU Member States. Consequently, it was revealed that Ireland, the Netherlands, Denmark, Austria, France, Cy-prus, Finland, Germany, and Belgium have achieved the highest level of well–being as of 2009. At the other end of spectrum, Czech Republic, Lithuania, Slovakia, Bulgaria, Poland, Hungary, Estonia, Lat-via, and Romania can be considered as those peculiar with relatively lowest well–being.Blagostanje je od ključnog značaja kako za pojedinca tako i za društvo u cjelini. Stoga je važno kvantificirati performanse i napredak određenih država, regija, zajednica, društvenih grupa i pojedinaca kako bi se unaprijedilo njihovo blagostanje. Cilj istraživanja je ponuditi novi okvir za višeciljnu procjenu kao i međunarodnu usporedbu objektivnog blagostanja. Blagostanje je višedimenzionalna pojava; stoga bi prikladni sustav indikatora trebao biti u mogućnosti identificirati najvažnije temeljne procese koji utječu na blagostanje. Za potrebe našeg istraživanja ustanovili smo indikatorski sustav od dvanaest indikatora koji identificiraju razne dimenzije blagostanja. Stoga predlažemo MULTIMOORA, model koji se može koristiti za približavanje cilju društvenog blagostan-ja. Primjenjuje se u svrhu međunarodne usporedbe blagostanja u zemljama članicama EU. Tako se otkrilo da su Irska, Nizozemska, Danska, Austrija, Francuska, Cipar, Finska, Njemačka i Belgija do-segle najviši stupanj blagostanja od 2009. Na drugom kraju spektra se nalaze Češka, Litva, Slovačka, Bugarska, Poljska, Mađarska, Estonija, Latvija i Rumunjska u kojima je blagostanje najniže

Decision support system for project monitoring portfolio

Vallejo Antich, RA. (2010). Decision support system for project monitoring portfolio. http://hdl.handle.net/10251/8632.Archivo delegad

A hybrid multiattribute decision making model for evaluating students’ satisfaction towards hostels

This paper proposes a new hybrid multiattribute decision making (MADM) model which deals with the interactions that usually exist between hostel attributes in the process of measuring the students’ satisfaction towards a set of hostels and identifying the optimal strategies for enhancing their satisfaction. The model uses systematic random stratified sampling approach for data collection purpose as students dwelling in hostels are “naturally” clustered by block and gender, factor analysis for extracting large set of hostel attributes into fewer independent factors, λ-measure for characterizing the interactions shared by the attributes within each factor, Choquet integral for aggregating the interactive performance scores within each factor, Mikhailov’s fuzzy analytical hierarchy process (MFAHP) for determining the weights of independent factors, and simple weighted average (SWA) operator to measure the overall satisfaction score of each hostel. A real evaluation involving fourteen Universiti Utara Malaysia (UUM) hostels was carried out in order to demonstrate the model’s feasibility. The same evaluation was performed using an additive aggregation model in order to illustrate the effects of ignoring the interactions shared by attributes in hostel satisfaction analysis