Evaluation of alternative fuels for residential heating in Turkey using analytic network process (ANP) with group decision-making

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Abstract

Energy policies require cheap and continuous energy which is needed in Turkey for further development. Implementation of a successful energy policy requires political and economical institutions to take responsibility and to adopt adapt to changes easily. Energy policy generally consists of institutional structure, in which decisions related to technology, economy and energy are made, and also consists of supply–demand management in short term and planning in long term. Energy demand is closely related to social and economic structure of a society. In the long term development of energy demand, developmental structure of society (economic growth, life style, socio-economic factors), technological development and energy prices play important roles as determining factors. In this study, evaluation of most suitable fuel which can be used for residential heating was made using ANP with group decision-making.

Introduction

Turkey's energy production and consumption figures along with its fast developing and growing economy have grown rapidly in recent years. It is seen that mainly state institutions produce energy and that after transferring to planning period, state invested a lot in energy sector. The deficit in energy kinds, where production cannot meet demand, is met via import. Turkey is a country where local energy resources are limited and consumption is met via import. Today, it is expected by Ministry of Energy and Natural Resources that the imported energy share, 65%, will be 73% in 2010 and 78% in 2020. In this study, ANP is selected to solve the fuel selection problem. There are three reasons to use ANP. The first is ANP has a systematic approach to set priorities and trade-off among goals and criteria [1]. ANP uses a ratio scale by human judgments instead of arbitrary scales [2], [3]. The second is ANP can measure all tangible and intangible criteria in the model [4], [5], [6]. The third is ANP is a relatively simple, intuitive approach that can be accepted by managers and other decision-makers [7], [8]. Because of these reasons, we prefered ANP to other Operation Research/Management Science techniques to solve the problem.

The analytic hierarchy process (AHP) was first introduced by Saaty [4]. AHP is a well-known technique that decomposes a decision problem into several levels in such a way that they form a hierarchy. The AHP model assumes a uni-directional hierarchical relationship among decision level [9]. In AHP, the top element of the hierarchy is the overall goal for the decision model. The hierarchy decomposes from the general to a more specific attribute until a level of manageable decision criteria is met. AHP is conceptually easy to use, but it is divisionally robust so that it can handle the complexities of real-word problems [10]. Since the introduction of AHP in 1976, it is widely used in different decision-making process, such as measuring performance [11], policy development in the energy market [12], macroeconomic forecasting [13], setting priorities for objectives [14], [15], [16], [17], [18], [19], evaluation of resources [20], filling systems [21], production cycles [22], banks [23], software [24], suppliers [25], electric power plants [26], fuel systems [27], crop production technologies [28], agricultural activities [28], reliability [29], and location [30], [31], [32], [33], [34], [35] by academics and practitioners. In reality, AHP is a comprehensive framework which is designed to model the real world decision problems when we make multi-objective, multi-criterion and multi-actor decisions for any number of alternatives. An advantage of the AHP over other MCDM is that AHP is designed to incorporate tangible as well as intangible criteria especially where the subjective judgments of different individuals constitute an important part of the decision process [25], [36]. The ANP is a general form of AHP and does not require this strictly hierarchical structure and therefore allows for more complex interrelationships among the decision levels and attributes [37], [38], [39]. ANP incorporates dependencies and feedback using a multilevel (or hierarchical) decision network is well suited to modeling dependence (or interdependence) relations among components, to represent and analyze interactions, and to synthesize their mutual effects by a single logical procedure [40], [41]. In the literature, the application of ANP is not as common as the applications of AHP. But application fields of ANP are rapidly increasing [42], [43], [44], [45], [46], [47], [48].

The objective of this study is to solve the complex decision problem by using ANP with BOCR and multi-actors. This article is divided into five sections. In Section 1, we present a brief review of ANP and the problem we studied. In Section 2, proposed ANP model is presented and the components of the model and relationships among them are determined in detail. In Section 3, the data used in the model are explained. In Section 4, how the analyses were done is described and discussions of the result are given. In Section 5, the results which have been obtained from solution of the model are evaluated.

Section snippets

Proposed ANP model

Here, we attempt to develop an ANP model about the decision problem. Determining the criteria in the ANP model is based on the evaluation obtained from actors. In this process the actors called a meeting. Criterion suggestions that may be used in the model were taken from participants in the meeting. These criteria were evaluated, and designated criteria as results of this evaluation were used ANP model. The criteria which were used here could be adapted to solve similar decision problems. The

Obtaining pairwise comparison matrices

After setting up the network model and required the connections, pairwise comparisons are performed. In order to do the pairwise comparisons, separate questionnaires are prepared for each actor group. The questions in these questionnaires are structured according to the connections that are related to each actor group. The questionnaires were prepared for the users, environmental organizations, state and suppliers.

While taking the judgment for each individual in each actor group, interviews

Analysis

Super Decisions software v.1.4.1 was used for the analysis. Total of 92 pairwise comparison matrices which were obtained in Section 3 were inputted into this program. The ANP analysis we performed involved four main steps.

In the first step of the analysis, consistency of the judgments is controlled. Especially, in the large scale decision problems, while doing pairwise comparisons, the respondents may misevaluate unconsciously. That is why it must be controlled that if pairwise comparison

Discussions and conclusions

The focus of this paper is on the decision-making process itself, and not on the mathematical aspects of ANP. In this study, during the processes from defining the problem to interpretation of the results, there has been an interaction with the actors.

The consistency of the judgments is very important in ANP as it is in all the scientific research. In this study, in general all actor groups are very consistent with their judgments. In fact, our study reported very low inconsistency ratios of

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