Plant Location Selection in Natural Stone Industry

Transcription

1 Key Engineering Materials Online: ISSN: , Vol. 548, pp doi: / Trans Tech Publications, Switzerland Plant Location Selection in Natural Stone Industry Mahmut Yavuz 1,a and Secil Ozer Colpan 2,b 1 Eskisehir Osmangazi University, Mining Engineering Department, Turkey 2 Turkish Coal Enterprises, Turkey a myavuz@ogu.edu.tr, b colpans@tki.gov.tr Keywords: Analytic Hierarchy Process (AHP), Multiple Attribute Decision Making (MADM), Plant Location, Processing of Natural Stone. Abstract. Determining the most convenient plant location is one of the commonly encountered problems in engineering applications. This paper presents an Analytic Hierarchy Process (AHP) model, which is developed for selecting the optimum plant location for marble/travertine factories in natural stones processing industry. The whole criteria which affect the decision making process in marble industry were determined to solve plant location problem in the AHP model. To determine optimum marble plant location for a new marble factory, which is planned to install by a mining firm located in the Eskisehir region in Turkey, an analysis was carried out by introducing the AHP method which is one of the well-known classical Multiple Attribute Decision Making (MADM) methods. This analysis shows that the AHP method can successfully be applied for the selection of plant location as well as any decision making process in natural stone industry. Introduction All branch of engineering plant location is a common problem and critical to all companies eventual success. Selecting a plant location is very important for all companies in minimizing cost and maximizing the use of resources. The new plant location should be evaluated carefully for the company s competitiveness. To achieve this goal, any potential criteria must be considered in selecting a particular plant location, including investment cost, human resources, availability of acquirement material, climate, etc. All the criteria affecting optimum plant location selection can be classified into two categories as subjective and objective. Subjective criteria are qualitatively defined, e.g. climate, manpower while objective criteria are quantitatively defined, e.g. land and installation cost. Multiple Criteria Decision Making (MCDM) is one of the most considerable branches of decision-making. MCDM refers to making decisions in the presence of multiple, usually conflicting, criteria. The problems in MCDM are classified into two categories: Multiple Attribute Decision Making (MADM) and Multiple Objective Decision Making (MODM). However, very often the terms MADM and MCDM are used to mean the same class of models and mostly confused in practice. Usually, MADM is used when the model cannot state in mathematical equations and otherwise MODM is used. Therefore, determining the plant location is a MCDM problem but this kind of problem is mostly categorized in MADM so that the decision maker can evaluate the subjective criteria in the problem. The decision maker wants to maximize more than one objective criterion for selection of plant location stage. Among the plant location alternatives, the most suitable plant location must be selected according to objectives and alternatives. MADM applications can help the decision maker to reach the optimal solution for the selection of plant location. In the marble industry, MADM methods can be applied for the selection of marble factory plant location because it is a process that includes subjective and objective criteria affecting the selection of plant location among the alternatives. Turkey s income from natural stone exports came to a total of $626 million in It is constituted around 53% of the Turkey s total mining export income in that year [1]. Natural stone export reached to total of $808 million in 2005 [2]. In 2011, natural stone exports increased to All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, (# , Pennsylvania State University, University Park, USA-19/09/16,11:09:01)

2 372 Global Stone Congress $1.675 billion and 43% of the country s total mining exports. In the future, it is expected that the Turkish marble industry s production rate will increase and new marble factories will be constructed in different region of Turkey. The most suitable location must be selected to achieve planned production target in natural stone industry. To solve this problem, the analytic hierarchy process (AHP) that is MADM technique, was used in this study and an AHP model was developed to help the decision maker working in natural stone processor. In the developed model, an expert team determined all the criteria affect the plant location of natural stone industry and weighted the pairwise comparison matrices at the first stage of solving process of the problem. The AHP, since its invention, has been a tool at the hands of decision makers and researchers; and it is one of the most prevalent used the MADM tools. Although many outstanding works have been published based on the AHP in the literature, the only two works related with plant location are; a new alumina cement plant location in East-Azerbaijan province of Iran by Ataei [3] and shaft location selection by Kazakidis et al. [4]. The AHP method have been increasingly incorporated in mining applications such as drilling technology investment analysis, ground support design, tunnelling systems design, mine planning risk assessment, underground mining method selection and open cast/pit mining equipment selection [4, 5, 6, 7, 8]. In this paper, an analysis was done by this method in order to determine the optimum plant location for a new marble factory planned to establish by a mining firm in Turkey. MCDM and Analytic Hierarchy Process (AHP) Methodology MCDM is one of the most well-known branches of decision-making [9]. The problems for MCDM are common occurrences in everyday life and broadly classified into two categories in this respect: MADM and MODM. Usually, MADM is used for selection (evaluation) and MODM is used for design [9]. The AHP method is one of the powerful MADM techniques. The AHP method developed by Thomas L. Saaty (1980) gives an opportunity to represent the interaction of multiple factors in complex unstructured situations [10, 11]. The method is based on the pair-wise comparison of components with respect to attributes and alternatives. A pair-wise comparison matrix n n is constructed, where n is the number of elements to be compared. The method is applied for the hierarchy problem structuring. The problem is divided in to three levels: problem statement, object identification to solve the problem and selection of evaluation criteria for each object. After the hierarchy structuring, the pair-wise comparison matrix is constructed for each level where a nominal discrete scale from 1 to 9 is used for the evaluation (Table 1) [10, 11, 12]. Relative Intensity Table 1. Scale for pair-wise comparisons. Definition 1 Of equal value 3 Slightly more value 5 Essential or strong value 7 Very strong value 9 Extreme value 2,4,6,8 Intermediate values between two adjacent judgments The next step is to find the relative priorities of criteria or alternatives implied by this comparison. The relative priorities are worked out using the theory of eigenvector. For example, if the pair comparison matrix is A, then, ( A λ max I) w = 0 (1)

3 Key Engineering Materials Vol To calculate the eigenvalue λ max and eigenvector w = (w 1, w 2,..., w n ), weights can be estimated as relative priorities of criteria or alternatives [13]. Since the comparison is based on the subjective evaluation, a consistency ratio is required to ensure the selection accuracy. The Consistency Index (CI) of the comparison matrix is computed as follows: λ max n CI= (2) n 1 where λ max is maximal or principal eigenvalue, and n is the matrix size. The consistency Ratio (CR) is calculated as: CI CR = (3) RI where RI Random Consistency Index. Random consistency indices are given in Table 2. Table 2. The consistency indices of randomly generated reciprocal matrices. Order of the matrix RI As a general rule, a consistency ratio of 0.10 or less is considered acceptable. In practice, however, consistency ratios exceeding 0.10 occur frequently. General Information about the Analysed Company ELMAS marble factories are the important groups of Turkey s travertine industries. They have got own travertine and marble quarries and two marble factories. The first ELMAS marble factory was founded in 1986, and they have been supplying the raw material from their own travertine quarry in Denizli for 20 years, so far. In 1997, the second marble factory was established in Eskisehir that is located in the north-western part of Turkey (Fig. 1). After a travertine block produced from the travertine quarry in Denizli, the block is transported to the Denizli or Eskisehir factory. The travertine blocks can either go to gang saw machine or to a block cutting machine. In Denizli factory, travertine blocks are only cut as a strip and rough draft slabs (Fig. 2). The strips and rough draft slabs are transported to Eskisehir marble factory to process polished marble slabs. In Eskisehir factory, marble strips and rough draft slabs are processed in a sequence of calibrating, wax filling, dimensioning, polishing, chamfering, quality controlling and packing operations (Fig. 2). The terminal products are transported to Izmir port for export. In 2006, the firm management decided to establish a new marble factory. The four alternative plant locations are determined by the management as Eskisehir, Bozuyuk, Afyon-Iscehisar and Denizli district. In Fig. 1, the location of travertine quarry, two present factories and four facility alternatives can be seen in the map. The Application of AHP Model for a New Factory of ELMAS The criteria and sub-criteria assessed by an expert team consisting of one geology engineer who is the manager of the firm and 20 years of experience in marble industry, two mining engineer who have 10 years of experience in marble industry and one firm owner who have 40 years of experience. All decisions have a common hierarchical structure whereby options are evaluated against the various criteria that promote the ultimate decision objective. The problem of the new marble factory plant location was structured in a hierarchy of different levels constituting goal, criteria and alternatives as shown in Fig. 3 [13].

4 374 Global Stone Congress Fig. 1. The location of ELMAS marble factories in Turkey. Marble Blocks Eskisehir Factory Denizli Factory Gang saw machine Block cutting machine Gang saw machine Bridge cutting machine Side cutting machine Bridge cutting machine Calibrating Wax filling Dimensioning Chamfering Polishing Epoxy filling Quality Control Packing Transport to Izmir Port Fig. 2. ELMAS marble factories production lines. After structuring a hierarchy, the pair-wise comparison matrix for each level is constructed. During the pair-wise consideration, a nominal scale is used for the evaluation given in Table 1. As shown in Table 3, each main criterion affecting plant location selection was compared with the others and the pair-wise comparison matrix was constructed. The expert team carried out these comparisons. It is apparent that the economy criterion is the most important factor (priority 0.581) and it is followed by the production criterion (Table 3). After constructing the pair-wise comparison matrix of main criteria, all the subgroup of each main criterion should be compared with the others as shown in Table 4, 5, 6 and 7.

5 Key Engineering Materials Vol Goal 1. Level Marble Plant Location Criteria 2. Level Sub-criteria 3. Level Economy Production Marketin Environmental Land Installation Transportation Tax reduction Raw material Manpower Technology Climate Water supply Close to market New markets Waste water Waste marble Visual Local Alternatives 4. Level A-Eskisehir B-Bozuyuk C-Afyon D-Denizli Fig. 3. Hierarchy structure for marble factory location. Table 3. Pair-wise comparison matrix of main criteria. Main Criteria Economy Production Marketing Environmental W λ max CR Economy Production 1/ Marketing 1/7 1/ Environmental 1/5 1/3 1/ Mean Table 4. Pair-wise comparison matrix the economy main criterion with sub-criteria of it. Econom. Crit. Land Installation Transportation Tax W λ max CR Land 1 2 1/5 1/ Installation 1/2 1 1/9 1/ Transportation Tax reduction 3 2 1/ Mean Table 5. Pair-wise comparison matrix production criterion with sub-criteria. Prod. Crit. Raw Manpower Technology Climate Water W λ max CR Raw Manpower 1/ Technology 1/3 1/ Climate 1/4 1/3 1/ Water 1/5 1/4 1/3 1/ Mean

6 376 Global Stone Congress Table 6. Pair-wise comparison matrix marketing criterion with sub-criteria. Market. Crit. Close to market New markets W λ max CR Close to market New markets 1/ Mean 2 0 Table 7. Pair-wise comparison matrix environmental criterion with sub-criterion. Environ. Crit. Waste Waste Visual Local water marble pollution regulations W λ max CR Waste water 1 1/3 1/7 1/ Waste marble 3 1 1/ Visual pollution Local regulations 2 1/2 1/ Mean The pair-wise comparison matrices are constructed by comparing the each plant location area with each sub-criterion of all main criteria. The comparison matrix for land sub-criterion of economic main criteria is given in Table 8 as an example, and general evaluation of economic criterion is given in Table 9. The same procedure was applied for the other main criteria and subcriterion of them. Table 8. Pair-wise comparison matrix of the land sub-criterion of Economic criterion. Land/Economic Eskisehir Bozuyuk Afyon Denizli W λ max CR A-Eskisehir 1 1/7 1/3 1/ B-Bozuyuk C-Afyon 3 1/2 1 1/ D-Denizli 2 1/ Mean Table 9. Total priorities of Economic criterion. Alternatives Sub-criteria of economic main criterion Main Criterion Total Land Installation Transportation Tax reduction Priorities Priorities A-Eskisehir B-Bozuyuk C-Afyon D-Denizli The overall rating for each alternative is calculated by summing the product of the relative priority of each criterion and the alternatives considering the corresponding main criteria. For example, the overall rating of alternative Eskisehir is calculated as: ( ) + ( ) + ( ) + ( ) = Similarly, the final matrix is constructed as shown in Table 10. Table 10. Overall result/final matrix. Alternatives Main criteria Criterion Overall Economic Production Marketing Environmental Priority Result A - Eskisehir B - Bozuyuk C - Afyon D - Denizli

7 Key Engineering Materials Vol Since the comparison is based on the subjective evaluation, a Consistency Ratio (CR) should be calculated from Equation [3] to ensure the selection accuracy. It can be seen from Table 3 to 8, the maximum Eigen values (λ max ) are near to the size of the matrix (In Table 3, the size of matrix is 4 4 and mean value of λ max is 4.081) and CR values are less than 0.1 (In Table 3, the mean CR value is 0.030), so these values are in the desired range. Therefore, the decision is taken without repeating the procedure. Considering the overall results in Table 10, the alternative Bozuyuk must be selected as the optimum plant location site to satisfy the goals and objectives of the ELMAS marble management because the priority of this alternative (0.313) is the highest value than that of the others. Conclusions Plant location involves the interaction of several subjective and objective factors or criteria. Decisions are often complicated and many even embody contradiction. In this study, the AHP model was developed which contains four main criteria and fifteen sub-criteria. Among four alternatives under consideration, variant Bozuyuk (B) is the most acceptable one with allowance for all main and sub-criteria comprised in the analysis. Unlike the traditional approach to the plant location selection, AHP is more scientific method providing the integrity and objectivity of estimation process. The model is transparent and easy to comprehend and apply by the decision maker. For selection of plant location, the proposed AHP model is unique in its identification of multiple attributes, minimal data requirement and minimal time consumption. Marble processing is one of the most important parts of the natural stone industry. Today, the investment cost of an average size marble processing plant is about $5 million [1]. Because of the size of the investment, it is very important to determine the optimum marble plant location. The developed AHP model from this study can be used for all marble types, and can generate an analysis of worldwide natural stone industry. The AHP based decision making applications can be applied for different parts of the natural stone industry such as selection of best suitable cutting and polishing equipment, abrasives, diamond saw, etc. among the alternatives for a natural stone factory. The number of criteria and alternatives should be paid attention by the decision maker in the AHP applications because of the consistency and validity of the decision making process [14, 15]. The number of alternatives should be 7±2, otherwise the grouping method should be applied following the same way presented in this study. Acknowledgement The authors wish to thank Geol. Engineer Ilhan Erozlu and his colleagues for their assistance. References [1] M. Ayhan: Cost model and sensitivity analysis of cutting and processing stage at a marble plant, Industrial Diamond Review 65(3) (2005) pp [2] Information on (in Turkish) [3] M. Ataei: Multicriteria selection for alumina-cement plant location in East Azerbaijan province of Iran, The Journal of The South African Institute of Mining and Metallurgy 105(7) (2005) pp [4] V.N. Kazakidis, Z. Mayer, M.J. Scoble: Decision making using the analytic hierarchy process in mining engineering, Mining Technology 113(1) (2004) pp

8 378 Global Stone Congress [5] A. Karadogan, A. Bascetin, A. Kahriman, S. Gorgun, in: A New Approach in Selection of Underground Mining Method, Proceedings of International Conference: Modern Management of Mine Producing, Geology and Environment Protection, Varna (2001) pp [6] M.R. Bitarafan, M. Ataei: Mining method selection by multiple criteria decision making tools, The Journal of The South African Institute of Mining and Metallurgy 104(9) (2004) pp [7] B.Samanta, B. Sarkar, S.K.Mukherjee: Selection of opencast mining equipment by a multicriteria decision-making process, Transactions of the Institution of Mining and Metallurgy, Section A: Mining Technology 111(2) (2002) pp [8] A. Bascetin: An application of the analytic hierarchy process in equipment selection at Orhaneli open pit coal mine, Mining Technology 113(3) (2004) pp [9] E. Triantaphyllou: Multi Criteria Decision Making Methods: A Comparative Study (Kluwer Academic Publishers, 2000). [10] C.L. Hwang and K. Yoon: Multi Attribute Decision Making Methods and Applications (Springer-Verlag, 1981). [11] T.L. Saaty: The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation (McGraw-Hill Publications, 1980). [12] T.L. Saaty: Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process (RWS Publications, 2000). [13] S. Ozer: Determination of optimum plant location for marble factories, Master of Science thesis, Eskisehir Osmangazi University, Graduate School, Turkey (2005). [14] T.L. Saaty, M.S. Ozdemir: Why the magic number seven plus or minus two, Mathematical and Computer Modelling 38 (2003) pp [15] M.S. Ozdemir: Validity and inconsistency in the analytic hierarchy process, Applied Mathematics and Computation 161(3) (2005) pp