EVALUATION OF IRANIAN SMALL AND MEDIUM-SIZED INDUSTRIES USING THE DEA BASED ON ADDITIVE RATIO MODEL – A REVIEW

Data Envelopment Analysis (DEA) is a prominent procedure in the decision-making process with a pivotal role in the sustainable development assay. Project identification is the first step of sustainability assessment in the Environmental Impact Assessment (EIA) program for the industrial projects prior to complete establishment. The present review research comprised 405 Iranian industries assessment regarding both input and output criteria via DEA integrated with the ratio model of Additive Ratio ASsessment (ARAS) and weighing systems of Kendall and Friedman's tests supported by SPSS software. The findings deployed a classification for Iranian industries pertaining to industries' nominal capacity in certain clusters. Also, the current review paved the pathway towards executing both energy and materials streams in industries

Evaluating the efficiency of heat and power systems by the data envelopment analysis method

The article describes the Data Envelopment Analysis (DEA) method and the main features of its application. The main problems of heat and power systems are described, which are addressed by the DEA method of efficiency assessment presented in the article. The approbation of this method is presented at the objects of the centralized municipal heat supply system of the fuel and energy complex: boiler houses and heat and power plants. 9 objects were analyzed according to four input indicators: available heat capacity, installed heat capacity, heat consumption for own needs, fuel consumption. Also, the efficiency of the system was evaluated according to two output indicators: the release of thermal energy to the grid and the mass of the emission. As a result of the analysis and calculations made, it was revealed that 5 objects have the maximum possible efficiency indicator equal to 1, that is, they function as efficiently as possible. 4 objects of the centralized municipal heat supply system have an efficiency indicator less than 1. Accordingly, improvements are required for the operation of the above Decision-Making Units (DMU)s. These objects have deviations in terms of the inputs and outputs of the actual data and those obtained using the DEA method. Based on the calculations obtained for these 4 objects, the article provides recommendations for changing the quantitative values of their input and output indicators. For example, for object number 2, it is recommended to reduce the installed heat capacity in the grid by 72.57%, without changing the available heat capacity and fuel consumption. Reduce the heat consumption for your own needs by 69.383%. In addition, it is recommended to increase the supply of thermal energy to the grid by 6,034%, and reduce the mass of emission by 11.5%. Specific measures have also been developed to modernize the studied objects in order to achieve the recommended indicators of inputs and outputs. The research results presented in the article are of scientific and practical interest and can be used to improve the efficiency of heat and power systems facilities. © 2021, World Scientific and Engineering Academy and Society. All rights reserve

Ein DEA-basierter Ansatz zur Messung der Performance bei zentralisierten Managementstrukturen

Traditional performance measurement approaches are usually characterized by a number of different limitations. Among other things, these approaches require the subjective determination of weights to aggregate a set of indicators to an overall performance score. Furthermore, traditional approaches are usually not able to incorporate additional improvement potentials that can be received from a centralized management. A performance measurement framework which can overcome these limitations is called data envelopment analysis (DEA). Against this background, this thesis provides a thorough overview of how different degrees of centralization are modeled in the current DEA literature. The systematic literature review identified 135 different approaches that assume a centralized or partially centralized management structure. A concluding discussion of the respective DEA approaches showed two fundamental research gaps. In response to this, this thesis has two fundamental objectives: The first objective is to propose a DEA-based performance measurement approach for measuring performance changes over time. The second objective is to develop another DEA-based approach for comparing the performance of management groups. In contrast to so far developed DEA-models, the here proposed approaches explicitly incorporate the respective management structure. Both DEA approaches thus developed are based on the combination of the metafrontier concept and the Malmquist index. The first approach evaluates productivity changes of operating entities over time and, hence, may indicate potential sources for performance changes. Thereby, the proposed approach preserves the individual characteristics of each local group technology. The second DEA approach proposed here uses the Malmquist index for comparing the performance of management groups. This index accounts for the existence of a central decision maker who can, e.g., undertake resource reallocations to improve the overall performance of its managed group. The applicability and usefulness of both proposed approaches is empirically shown with real-world data from KONE Corporation.Traditionelle Performance Measurement Ansätze gehen mit einer Reihe von Herausforderungen einher. So erfordert die Aggregation unterschiedlicher Kennzahlen zu einem einzelnen Performancemaß die Verwendung von subjektiven Gewichtungen. Darüber hinaus lassen sich in traditionellen Ansätzen nur schwer etwaige Verbesserungspotentiale modellieren, die aus zentralisierten Managementstrukturen resultieren. Eine betriebswirtschaftliche Methode, welche die genannten Limitationen nicht aufweist, ist die Data Envelopment Analysis (DEA). Aufgrund dieser Vorteile wird in dieser Arbeit zunächst ein umfassender Literaturüberblick erarbeitet, wie unterschiedliche Managementstrukturen in einer DEA modelliert werden können. Mithilfe der Literaturrecherche wurden insgesamt 135 unterschiedliche Ansätze ermittelt, die entweder ein vollkommen zentralisiertes oder teilweise zentralisiertes Managementmodell unterstellen. Eine abschließende Diskussion der verschiedenen DEA-Ansätze zeigte allerdings eine Forschungslücke, woraus die beiden folgenden Forschungsziele für diese Arbeit abgeleitet wurden: Einerseits soll ein DEA-basierter Ansatz erarbeitet werden, der zur Messung von Effizienzveränderungen einzelner Produktiveinheiten über die Zeit geeignet ist. Andererseits soll eine DEA-basierte Methode entwickelt werden, welche bei Performancevergleichen zwischen Managementgruppen anwendbar ist. Im Gegensatz zu den bisher in der Literatur diskutierten Ansätzen sollten die entwickelten Methoden dabei die jeweils vorliegende Managementstruktur berücksichtigen. Die entwickelten DEA-Ansätze basieren auf der Kombination des Metafrontier-Konzepts mit dem Malmquist-Index. Der erste Ansatz erlaubt es, Performanceveränderungen von einzelnen Produktiveinheiten über mehrere Zeitperioden zu messen. Im Gegensatz zu konventionellen Metafrontier-basierten Malmquist-Indizes berücksichtigt der vorgeschlagene Ansatz die individuellen Eigenschaften der lokalen Produktionstechnologien. Der zweite vorgeschlagene DEA-Ansatz nutzt den Malmquist-Index für den Vergleich der Performance von Managementgruppen. Der Index berücksichtigt dabei explizit, dass eine zentrale Entscheidungsinstanz existiert, welche Ressourcenumverteilungen durchführen kann. Beide Ansätze werden anhand eines Datensatzes des Unternehmens KONE illustriert

Predicate based association rules mining with new interestingness measure

Association Rule Mining (ARM) is one of the fundamental components in the field of data mining that discovers frequent itemsets and interesting relationships for predicting the associative and correlative behaviours for new data. However, traditional ARM techniques are based on support-confidence that discovers interesting association rules (ARs) using predefined minimum support (minsupp) and minimum confidence (minconf) threshold. In addition, traditional AR techniques only consider frequent items while ignoring rare ones. Thus, a new parameter-less predicated based ARM technique was proposed to address these limitations, which was enhanced to handle the frequent and rare items at the same time. Furthermore, a new interestingness measure, called g measure, was developed to select only highly interesting rules. In this proposed technique, interesting combinations were firstly selected by considering both the frequent and the rare items from a dataset. They were then mapped to the pseudo implications using predefined logical conditions. Later, inference rules were used to validate the pseudo-implications to discover rules within the set of mapped pseudo-implications. The resultant set of interesting rules was then referred to as the predicate based association rules. Zoo, breast cancer, and car evaluation datasets were used for conducting experiments. The results of the experiments were evaluated by its comparison with various classification techniques, traditional ARM technique and the coherent rule mining technique. The predicate-based rule mining approach gained an accuracy of 93.33%. In addition, the results of the g measure were compared with a state-of-the-art interestingness measure developed for a coherent rule mining technique called the h value. Predicate rules were discovered with an average confidence value of 0.754 for the zoo dataset and 0.949 for the breast cancer dataset, while the average confidence of the predicate rules found from the car evaluation dataset was 0.582. Results of this study showed that a set of interesting and highly reliable rules were discovered, including frequent, rare and negative association rules that have a higher confidence value. This research resulted in designing a methodology in rule mining which does not rely on the minsupp and minconf threshold. Also, a complete set of association rules are discovered by the proposed technique. Finally, the interestingness measure property for the selection of combinations from datasets makes it possible to reduce the exponential searching of the rules

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

An integrated data envelopment analysis and mixed integer non-linear programming model for linearizing the common set of weights

Abstract Theproblemofrankingef?cientdecisionmakingunits(DMUs)isofinterest from both theoretical and practical points of view. In this paper, we propose an integrated data envelopment analysis and mixed integer non-linear programming (MINLP)modelto?ndthemostef?cientDMUusingacommonsetofweights.Welinearize the MINLP model to an equivalent mixed integer linear programming (MILP) model by eliminating the non-linear constraints in which the products of variables are incorporated. The formulated MILP model is simpler and computationally more ef?cient. In addition, we introduce a model for ?nding the value of epsilon, since the improper choice of the non-Archimedean epsilon may result in infeasible conditions. We use a real-life facility layout problem to demonstrate the applicability and exhibit the ef?cacy of the proposed model

\u3ci\u3eThe Conference Proceedings of the 1998 Air Transport Research Group (ATRG) of the WCTR Society, Volume 1 \u3c/i\u3e

UNOAI Report 98-6https://digitalcommons.unomaha.edu/facultybooks/1154/thumbnail.jp