SELECTION OF THE BEST CONSULTANT FOR SAP ERP PROJECT USING COMBINED AHP-IBA APPROACH

Abstract: In this paper we propose a combined AHP-IBA model for selecting the best SAP consultant for an SAP ERP project. The goal of the SAP Project Manager is to choose the best consultant, the one who is able to implement standard SAP functionalities with quality and on time.When making a decision on the basis of multiple criteria, the traditional Analytic Hierarchy Process (AHP) method does not take into account the fact that attributes may correlate, assuming that there are no dependencies between them. However, the dependencies of the attributes can often be used to model important knowledge for multiple criteria decision analysis. We propose an extension to the traditional AHP method by applying Interpolative realization of Boolean algebra (IBA), using AHP to determine the criteria weights, and IBA to model the logical interactions among criteria. The research conducted on ERP consultant selection suggests that the decision making process is modelled more accurately if logical interactions between attributes are modelled before applying AH

Decision making with fair ranking

Abstract and Figures Ranking is a responsible process because it involves working with sensitive attributes that can discriminate alternatives. Due to the availability of a large amount of data for automated processing, ranking is increasingly in use in decision making. Therefore, concepts of algorithmic fairness in the field of classification in machine learning find their place in fair ranking methods. This paper provides an overview of fair ranking terms, fair ranking challenges, and fair ranking algorithms from the state-of-the-art literature

Analysis of the efficiency of insurance companies in Serbia using the fuzzy AHP and TOPSIS methods

The aim of this study is to propose a fuzzy multi-criteria model that will facilitate the assessment of insurance companies’ efficiency. This study includes all companies operating within the insurance sector in Serbia in the period from 2007 to 2014 and the data were used from the published financial statements of insurance companies. Five key indicators were identified for the assessment and rating of insurance companies. Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) were used for building the proposed model. In the first stage, priority weights of criteria were defined by using the FAHP, while in the second phase the insurance companies were ranked using the TOPSIS method

Achieving MAX-MIN Fair Cross-efficiency scores in Data Envelopment Analysis

Algorithmic decision making is gaining popularity in today's business. The need for fast, accurate, and complex decisions forces decision-makers to take advantage of algorithms. However, algorithms can create unwanted bias or undesired consequences that can be averted. In this paper, we propose a MAX-MIN fair cross-efficiency data envelopment analysis (DEA) model that solves the problem of high variance cross-efficiency scores. The MAX-MIN cross-efficiency procedure is in accordance with John Rawls’s Theory of justice by allowing efficiency and cross-efficiency estimation such that the greatest benefit of the least-advantaged decision making unit is achieved. The proposed mathematical model is tested on a healthcare related dataset. The results suggest that the proposed method solves several issues of cross-efficiency scores. First, it enables full rankings by having the ability to discriminate between the efficiency scores of DMUs. Second, the variance of cross-efficiency scores is reduced, and finally, fairness is introduced through optimization of the minimal efficiency scores

Prediction of skiing time by structured regression algorithm

In this paper, the application of Gaussian conditional random fields (GCRF) in the case of prediction skiing time between ski gates in ski center Kopaonik, is presented. Gaussian conditional random fields is well-known structured regression method that exploits advantages of unstructured predictors and combines them with the information concerning correlation between outputs. Four different unstructured predictors were used: ridge regression, LASSO regression, Random forest regression and support vector machine regression. Even thought, only 18 features are used for prediction of skiing time, GCRF achieved better results, concerning R2 and mean absolute error, compared to unstructured predictors

SOLVING FIRST ORDER DIFFERENTIAL EQUATIONS WITH GENETIC ALGORITHMS

U radu su predstavljene dve metode za rešavanje Košijevog problema običnih diferencijalnih jednačina prvog reda. Metode su bazirane na rešavanju običnih diferencijalnih jednačina prvog reda korišćenjem genetskih algoritama (GA). Metode su međusobno upoređene sa različitim načinama sparivanja populacije. Pored toga data su poređenja GA sa najprostijim i najčešće primenjivanim metodama za rešavanje običnih diferencijalnih jednačina. Pokazuje se da GA daju zadovoljavajuće vrednosti rešenja diferencijalnih jednačina i da su efikasniji od određenih numeričkih metoda. Runge Kuta metod pokazuje najbolje vrednosti aproksimacije rešenja, dok Ojlerov metod sa korakom 0,1 pokazuje veće vrednosti relativnih grešaka aproksimativnih rešenja u odnosu na GA. Bez obzira na to primena GA je vrlo ograničena s obzirom na vreme izvršenja istih koje je nekoliko 1000 puta veće u odnosu na preostale metode.In this paper two different methods for solving Cauchy problem of first order differential equations are preseneted. Methods are based on implementation of genetic algorithms. Results of both methods are compared with the commonly used techniques for solving differential equations. It is shown that methods based on genetic algorithms achieved satisfactory results and better performances compared to Eulers method. 5th order Runge Kutta method obtained best approximation of real results, whereas Euler method with step 0,1 achieved the worst performances. Neverthless it is shown that application of genetic algorithms in solving first order differential equations is limited due to high computational costs

PREDICTION OF TRAFFIC INTENSITY AT PAY TOLL STATIONS

U radu je predstavljen metod za predviđanje intenziteta saobraćaja na sistemu za naplatu putarine za različit broj unapred određnih naplatnih rampi koje će biti otvorene. Sistem za naplatu putarine je predstavljen kao više jednokanalnih sistema ospluživanja, gde jedan kanal predstavlja jednu naplatnu rampu. Razvijanjem metodologije zasnovane na predviđanju parametra opsluživanja za unapred zadati broj otvorenih kanala, kombinacijom neuronskih mreža i modela masovnog opsluživanja evaluirane su verovatnoće stanja sistema za naplatu putarine i ukupni troškovi istog . LSTM neuronska mreža (eng. Long short term memory) sa unutrašnjom standardizacijom korišćena je za predviđanje parametra opsluživanja. Analizirane su 24 arhitekture mreža, model sa najboljim prediktivnim performansama je izabran i korišćen u cilji predviđanja parametra opsluživanja.In this paper method for predicting states of toll station system for different number of open toll ramps is developed. The system for toll payment is modeled as single channel queuing model, where one channel presents toll ramp. The novel methodology based on combination of reccurent neural networks and queuing theory is presented. The goal of the methodlogy is to evaluate total costs and probability of traffic intensity at the pay toll stations.. Long short term memory neural network (LSTM) with layer normalization is used as a model for prediction intensity. The 24 different architectures of network are analyzed, and the best one is used as the predictor for intensity of vehicles arrivng time

Application of integrated QFD and fuzzy AHP approach in selection of suppliers

Supplier selection is a widely considered issue in the field of management, especially in quality management. In this paper, in the selection of suppliers of electronic components we used the integrated QFD and fuzzy AHP approaches. The QFD method is used as a tool for translating stakeholder needs into evaluating criteria for suppliers. The fuzzy AHP approach is used as a tool for prioritizing stakeholders, stakeholders’ requirements, evaluating criteria and, finally, for prioritizing suppliers. The paper showcases a case study of implementation of the integrated QFD and fuzzy AHP approaches in the selection of the electronic components supplier in one Serbian company that produces electronic devices. Also presented is the algorithm of implementation of the proposed approach. To the best of our knowledge, this is the first implementation of the proposed approach in a Serbian company