Croatian Operational Research Review

Tiered Data Envelopment as method for clustering suppliers

Imre Dobos — 2023-12-19

Effective supplier management requires continuous monitoring of the performance and capabilities of one’s supplier base. Although the literature often focuses on ranking, it has lately become increasingly important to group suppliers according to their capabilities. In this paper, we compare two clustering methods. The application of Cluster Analysis (CA) has been widely discussed in the literature. Tiered Data Envelopment Analysis (TDEA) is also well-known in the decision-making literature, but is nonetheless seldom employed in supplier evaluation. CA is only suitable for group formation on a nominal scale, whereas the TDEA method during group formation allows the groups to be formed on an ordinal scale. TDEA may therefore prove to be the more suitable method for ordinal group formation. This article attempts to bridge a research gap, which arises since the two methods are infrequently employed in supplier selection. A numerical example is used to compare their application.

An inverse multi-period FDH model with undesirable outputs

Farzaneh Asadi — 2023-12-19

In some examinations, the changes in performance measures and the efficiency of entities should be addressed over a span of time despite the undesirable outputs and unsatisfactory convexity property. In this study, the performance of processes is analyzed over several periods of time by presenting a multi-period model based on the free disposal hull (FDH) approach, incorporating undesirable outputs. Using the product of availability, performance (efficiency) and quality, the overall equipment effectiveness (OEE) is calculated which shows the level of productivity. After estimating the performance and OEE, an inverse multi-period FDH model is introduced to measure the input changes for the changes made to outputs in different time periods. The proposed approaches are used in the automotive industry to analyze the performance and changes in input measures over multiple periods of time. The results show that the proposed technique is reasonable for assessing the multi-period efficiency, the period and overall OEE of the systems, and the input changes related to several periods while the convexity assumption is violated.

A Monte Carlo study on the size and power of panel unit root tests: Limitations in small data sets

Ivana Mravak — 2023-12-19

The aim of this paper is to explore the properties of various panel unit root tests in terms of their power and size regarding different panel data structures, with a special focus on small data samples. In particular, different values of the cross-sectional and time dimensions for both heterogeneous and homogeneous data settings with intercept and with and without time trend are observed. We examine and compare the results of five commonly used first-generation panel unit root tests: Levin, Lin and Chu test, Im Pesaran and Shin test, Harris–Tzavalis test, Breitung test and a Fischer type test. The results are derived using Monte Carlo simulations and show that all the observed panel unit root tests suffer from a serious lack of power and tend to either over or under reject the null hypothesis when the time dimensions are small. It is evident that the results of conducting panel unit root tests for data with $T<30$ are erroneous and unreliable, and it is therefore concluded that panel unit root tests should not be conducted for such samples in the first place. This is even more pronounced when there is a time trend or heterogeneity.

Classifying scuba diving sites through diver reviews with a web scraping based UTADIS application

Yunus Yildirim — 2023-12-19

This research addresses the utilization of user-defined and web-based data for decision-making in recreational activities, focusing on the case of scuba diving, a globally significant recreational pursuit. While a wealth of user-generated information is available on the internet for various leisure activities, harnessing this data involves extensive data collection and organization efforts. Our proposed methodology involves the collection and organization of user reviews, both in verbal and quantitative forms, to create a pertinent dataset for applying multiple criteria decision methodologies to classify diving sites worldwide. An initial dataset containing over 14,000 diving sites worldwide is aggregated into 721 regions and these regions are classified using UTADIS methodology. The research showcases how user-generated review data can be transformed into valuable information, applying classification algorithms of multiple criteria decision analysis within the context of scuba diving. Furthermore, the proposed approach in this research holds the potential to serve as a model for leveraging user-generated data in decision-making processes across various service sectors such as hospitality and leisure that highly rely on customer experience by providing new insights on how more data-driven approaches can be utilized.

Solving multi level multi-objective linear programming (ML MOLP) problems with fuzzy parameters (FPs)

Tunjo Perić — 2023-12-19

Making numerous business decisions in complex decentralized organizations can be presented as a ML MOLP problem with FPs expressed as triangular fuzzy numbers. This paper presents a methodology that uses several multi-objective programming methods to solve this problem. The Iskander’s method was used for defuzzification of the objective functions and constraints of the problem and a multi-objective programming method based on the cooperation among decision makers was used to determine the aspired values of the variables controlled by the decision-makers and to obtain the preferred non-dominated solution of the entire problem. The efficiency of the proposed methodology was tested on an example of production planning in a complex decentralized company.

What factors influence Bitcoin’s daily price direction from the perspective of machine learning classifiers?

Tea Kalinic Milicevic — 2023-12-19

The paper examines the factors that influence Bitcoin price direction from the perspective of machine learning (ML) models. The observed factors cover Bitcoin market data, technical indicators, blockchain variables, sentiment analysis, and other macro-financial variables. Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) classifiers are employed. Three train-test ratios are considered. Grid search and blocking time series cross-validation are used to adjust the hyperparameters of the proposed ML algorithms resulting in the three most accurate models for each train-test ratio. Variables that affect the next-day price direction are ranked using LR and RF best models. For each method and train-test ratio, the smallest subsets of independent variables with the highest test set accuracy were chosen to reduce dimensionality. Models show that technical indicators influence daily Bitcoin price direction the most, followed by blockchain and Bitcoin market variables. Contrarily, models disagree on the importance of Tweets and macro-financial variables. Finally, SVM performed better on the test set when the LR optimal sets of independent variables were considered, indicating that the analysis of individual factors' influence on the Bitcoin price is not important only for corresponding model. Combining only influential independent variables and 90:10 train-test ratio yielded the greatest accuracy of 58.18 % achieved by RF model.

Non-linear averaging-based operators of pseudo-hesitant fuzzy elements and an application

Abazar Keikha — 2023-12-19

Data modeling/aggregating, in many uncertain real-world' problems such as decision-making processes, has gotten more attention in recent years. Due to a variety of uncertainty sources, various types of fuzzy sets, and various types of averaging-based aggregation functions have been proposed. The power average operator (PAO), as a nonlinear operator, is more appropriate than other averaging-based functions for situations where different values are given on a single subject. In this paper, PAO will be extended to be used in the aggregation process of given pseudo-hesitant fuzzy elements (pseudo-HFEs), and some needed properties have been discussed, too. Then, four kinds of PAO with pseudo-HFEs, i.e., power average operator of pseudo-HFEs, power weighted average operator of pseudo-HFEs, power ordered weighted average operator of pseudo-HFEs and power hybrid average operator of pseudo-HFEs, will be defined. To solve a multi-attribute group decision-making (MAGDM) problem, the evaluation step done by both decision-makers and self-assessment will be quantified by pseudo-HFEs. Then the PAO will be applied to aggregate the row elements of the resulting decision matrix. The ranking orders of obtained pseudo-HFEs, show the options' orders. Finally, the proposed method will be used to solve a multi-attribute group decision-making problem, illustrated numerically, analyzed, and validated.

A new search direction for full-Newton step infeasible interior-point method in linear optimization

Behrouz Kheirfam — 2023-12-19

In this work, we investigate a full Newton step infeasible interior-point method for
linear optimization based on a new search direction which is obtained from an algebraic equivalent transformation of the central path system. Furthemore, we prove that the proposed method obtains an $\varepsilon$-optimal solution to the original problem in polynomial time.