![International Journal of Management Research and Development (IJMRD) ISSN 2248-938X (Print),
ISSN 2248-9398 (Online) Volume 4, Number 1, January-March (2014)
42
RESEARCH METHODOLOGY
To apply TOPSIS to our problem, numeric score is required to generate for each criteria. So,
each criteria were given an evaluation scale from 1 to 9. Evaluation pattern was decided and
finalized with expert advice. A Survey Questionnaire was prepared in the form of numeric value for
each criteria and distributed to selected stakeholders and then data for the collected survey
questionnaires was analyzed by Technique for Order Preference by Similarity to Ideal Solution
(TOPSIS).
TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION
(TOPSIS)
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) was developed by
Yoon and Hwang [1980] as an alternative to the ELECTRE method and can be considered as one of
its most widely accepted variants.
The basic concept of this method is that the selected alternative should have the shortest distance
from the ideal solution and the farthest distance from the negative-ideal solution in a geometrical
sense. The method evaluates the decision matrix, which refers to n alternatives that are evaluated in
terms of m criteria. The only subjective input needed is relative weights of attributes.
STEP BY STEP PROCEDURE OF TOPSIS
Step 1: Construct the Normalized Decision Matrix.
The TOPSIS method first converts the various criteria dimensions into non-dimensional Criterias:
…(1)
Step 2: Construct the Weighted Normalized Decision Matrix.
A set of weights W = (w1, w2, w3... wn), (where: wi = 1) defined by the decision maker is next used
with the decision matrix to generate the weighted normalized matrix V as follows:
...(2)
Step 3: Determine the Ideal and the Negative-Ideal Solutions.
The ideal, denoted as A*
, and the negative-ideal, denoted as A-
, alternatives (solutions) are defined
as follows:
A*
= {(max vij | j ���� J), (min vij | j ���� J’), i = 1, 2, 3,…,m}
= {v1
*
, v2
*
,…,vn
*
} ...(3)](https://image.slidesharecdn.com/10-200422051004/85/AN-INTEGRATED-APPROACH-FOR-ENHANCING-READY-MIXED-CONCRETE-SELECTION-USING-TECHNIQUE-FOR-ORDER-PREFERENCE-BY-SIMILARITY-TO-IDEAL-SOLUTION-TOPSIS-3-320.jpg)
![International Journal of Management Research and Development (IJMRD) ISSN 2248-938X (Print),
ISSN 2248-9398 (Online) Volume 4, Number 1, January-March (2014)
44
RESULTS OF TOPSIS
Table 1: Overall Ranking of Respondents
Rank Relative closeness (ci*
) Respondents
1 0.562180827 R3 [BEST]
2 0.558387359 R2
3 0.551909084 R45
4 0.546507279 R47
5 0.535887527 R56
6 0.532909125 R13
7 0.529689378 R30
8 0.529483387 R43
9 0.528939549 R26
10 0.518105777 R53
11 0.517865047 R9
12 0.515199708 R50
13 0.514257412 R10
14 0.510165536 R40
15 0.504832708 R28
16 0.504766006 R18
17 0.504503259 R27
18 0.500215879 R55
19 0.489517005 R58
20 0.484016571 R59
21 0.474961290 R60
22 0.473420146 R31
23 0.472813964 R51
24 0.472375635 R7
25 0.467873551 R24
26 0.467045674 R20
27 0.455194614 R12
28 0.453784050 R35
29 0.449556899 R49
30 0.446141966 R48
31 0.444929380 R34
32 0.443374273 R57
33 0.438709771 R16
34 0.431465487 R8
35 0.429148738 R22
36 0.425613901 R17
37 0.425250493 R5
38 0.411072505 R46
39 0.408179181 R54
40 0.406758924 R33
41 0.400437626 R52
42 0.399121735 R32
43 0.396657799 R19
44 0.394833193 R1
45 0.393538366 R6
46 0.387904518 R38
47 0.386201044 R11
48 0.382785861 R15
49 0.378026694 R29
50 0.372115534 R39
51 0.369306445 R36
52 0.369055533 R37
53 0.368268961 R4
54 0.359377965 R44
55 0.358163682 R41
56 0.357608058 R14
57 0.354201127 R25
58 0.353154447 R23
59 0.320541156 R21
60 0.314084336 R42 [WORSE]](https://image.slidesharecdn.com/10-200422051004/85/AN-INTEGRATED-APPROACH-FOR-ENHANCING-READY-MIXED-CONCRETE-SELECTION-USING-TECHNIQUE-FOR-ORDER-PREFERENCE-BY-SIMILARITY-TO-IDEAL-SOLUTION-TOPSIS-5-320.jpg)
AN INTEGRATED APPROACH FOR ENHANCING READY MIXED CONCRETE SELECTION USING TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS)
- 1. International Journal of Management Research and Development (IJMRD) ISSN 2248-938X (Print), ISSN 2248-9398 (Online) Volume 4, Number 1, January-March (2014) 40 AN INTEGRATED APPROACH FOR ENHANCING READY MIXED CONCRETE SELECTION USING TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS) Ashish H. Makwana 1 , Prof. Jayeshkumar Pitroda2 1 Student of final year M.E. C. E. & M., B.V.M. Engineering College, Vallabh Vidyanagar 2 Assistant Professor and Research Scholar, Civil Engineering Department, B.V.M. Engineering College,Vallabh Vidyanagar– Gujarat – India. ABSTRACT The use of Ready Mixed Concrete (RMC) by the construction industry in most industrialized countries is now well established. With the help of going over expertise of experts and their relevant specialized literature, effective Criterias in Ready Mixed Concrete (RMC) selection and the Criterias which will be used in their evaluation is extracted. For TOPSIS, the computations were carried out using Microsoft Excel 2013. The weight of the Criterias is calculated first through Analytic Hierarchy Process (AHP) and then it is analyzed by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The respondents were selected from various construction occupancy mainly Ready Mixed Concrete (RMC) Plant Managers, Consultants and Contractors. Total 100 Survey Questionnaires were distributed to Respondents in Anand, Nadiad, Vadodara, Ahmedabad, from which 60 Responses were collected as per sample size calculation, in that 21 were from Ready Mixed Concrete (RMC) Plant Managers, 26 were from consultants and 13 were from contractors.The problem solution result shows: Respondent no. 3 (R3) is best because of its largest weight age and Respondent no. 25 (R25) is worse because of its smallest weightage. Keywords: Ready Mixed Concrete, Analytic Hierarchy Process, Technique for Order Preference by Similarity to Ideal Solution, Respondents, Questionnaires IJMRD © PRJ PUBLICATION International Journal of Management Research and Development (IJMRD) ISSN 2248 – 938X (Print), ISSN 2248 – 9398(Online), Volume 4, Number 1, January - March (2014), pp.40-46 © PRJ Publication, http://www.prjpublication.com/IJMRD.asp
- 2. International Journal of Management Research and Development (IJMRD) ISSN 2248-938X (Print), ISSN 2248-9398 (Online) Volume 4, Number 1, January-March (2014) 41 INTRODUCTION Without doubt the Ready Mixed Concrete industry is now a major sector of the construction industry. It makes an effective contribution to that industry’s performance. More understanding of its activities by engineers and contractors would lead to a further step forward in the overall efficiency of all sectors. Several Roles regarding Ready Mixed Concrete are mention below: First Role - To Provide A Product:For the engineer to have confidence in Ready Mixed Concrete, he requires assurance that the concrete is of the required quality, contains suitable materials, has been manufactured under conditions of quality control by experienced staff using reliable equipment. Second Role - To Provide A Service:For the contractor to have confidence in Ready Mixed Concrete, he requires assurance that the supplier can meet all his delivery requirements and has sufficient capacity of production, material supplies and vehicles, and that the supplier will provide the correct quantities. Third Role - To Provide Value for Money:There is a growing demand by customers for the assurance that any product or service is suitable for the purpose and of uniform quality. NEED OF THE STUDY Present approach lacks scientific methodology and does not consider multi-criteria in decision making. There is a need of scientific methodology for Ready Mixed Concrete selection approach. Hence, the need of this Research work based upon various utility measures like quality control, cost, delivery, quantity at which owners or plant managers have to concentrate for enhancing profit as well as maintaining standard by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). OBJECTIVES OF THE STUDY 1. To Study,Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). 2. To derive the relation between various Criterias for enhancing utility of Ready Mixed Concrete. 3. To achieve optimization by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). SCOPE OF THE STUDY The scope of this research work of development of Ready Mixed Concrete selection process is limited to four cities of Central Gujarat Region of India: Ahmedabad, Nadiad, Anand, and Vadodara.
- 3. International Journal of Management Research and Development (IJMRD) ISSN 2248-938X (Print), ISSN 2248-9398 (Online) Volume 4, Number 1, January-March (2014) 42 RESEARCH METHODOLOGY To apply TOPSIS to our problem, numeric score is required to generate for each criteria. So, each criteria were given an evaluation scale from 1 to 9. Evaluation pattern was decided and finalized with expert advice. A Survey Questionnaire was prepared in the form of numeric value for each criteria and distributed to selected stakeholders and then data for the collected survey questionnaires was analyzed by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS) TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) was developed by Yoon and Hwang [1980] as an alternative to the ELECTRE method and can be considered as one of its most widely accepted variants. The basic concept of this method is that the selected alternative should have the shortest distance from the ideal solution and the farthest distance from the negative-ideal solution in a geometrical sense. The method evaluates the decision matrix, which refers to n alternatives that are evaluated in terms of m criteria. The only subjective input needed is relative weights of attributes. STEP BY STEP PROCEDURE OF TOPSIS Step 1: Construct the Normalized Decision Matrix. The TOPSIS method first converts the various criteria dimensions into non-dimensional Criterias: …(1) Step 2: Construct the Weighted Normalized Decision Matrix. A set of weights W = (w1, w2, w3... wn), (where: wi = 1) defined by the decision maker is next used with the decision matrix to generate the weighted normalized matrix V as follows: ...(2) Step 3: Determine the Ideal and the Negative-Ideal Solutions. The ideal, denoted as A* , and the negative-ideal, denoted as A- , alternatives (solutions) are defined as follows: A* = {(max vij | j ���� J), (min vij | j ���� J’), i = 1, 2, 3,…,m} = {v1 * , v2 * ,…,vn * } ...(3)
- 4. International Journal of Management Research and Development (IJMRD) ISSN 2248-938X (Print), ISSN 2248-9398 (Online) Volume 4, Number 1, January-March (2014) 43 A- = {(min vij | j ���� J), (max vij | j ���� J’), i = 1, 2, 3,…,m} = {v1 - , v2 - ,…,vn - } ...(4) Where: J = {j = 1, 2, 3... n and j is associated with benefit criteria}, Jl = {j = 1, 2, 3... n and j is associated with cost/loss criteria}. The previous two alternatives are fictitious. However, it is reasonable to assume here that for the benefit criteria, the decision maker wants to have a maximum value among the alternatives. For the cost criteria, the decision maker wants to have a minimum value among the alternatives. In this step, alternative A* indicates the most preferable alternative or the ideal solution. Similarly, alternative A- indicates the least preferable alternative or the negative-ideal solution. Step 4: Calculate the Separation Measure. The n-dimensional Euclidean distance method is next applied to measure the separation distances of each alternative from the ideal solution and negative-ideal solution. Thus, distances from the ideal solution is defined as follows: ! " ! # $ %& $ $ $ $ …(5) Where, Si* is the distance (in the Euclidean sense) of each alternative from the ideal solution. Similarly, distances from the negative-ideal solution is defined as follows: ' ! " ! '# $ %& $ $ $ $ …(6) Where, Si- is the distance (in the Euclidean sense) of each alternative from the negative-ideal solution. Step 5: Calculate the Relative Closeness to the Ideal Solution. The relative closeness of an alternative Ai with respect to the ideal solution A* is defined as follows: ( ' ) ' …(7) Step 6: Rank the Preference Order. The best (optimal) alternative can now be decided according to the preference rank order of C* . Therefore, the best alternative is the one that has the shortest distance to the ideal solution. The previous definition can also be used to demonstrate that any alternative which has the shortest distance from the ideal solution is also guaranteed to have the longest distance from the negative- ideal solution.
- 5. International Journal of Management Research and Development (IJMRD) ISSN 2248-938X (Print), ISSN 2248-9398 (Online) Volume 4, Number 1, January-March (2014) 44 RESULTS OF TOPSIS Table 1: Overall Ranking of Respondents Rank Relative closeness (ci* ) Respondents 1 0.562180827 R3 [BEST] 2 0.558387359 R2 3 0.551909084 R45 4 0.546507279 R47 5 0.535887527 R56 6 0.532909125 R13 7 0.529689378 R30 8 0.529483387 R43 9 0.528939549 R26 10 0.518105777 R53 11 0.517865047 R9 12 0.515199708 R50 13 0.514257412 R10 14 0.510165536 R40 15 0.504832708 R28 16 0.504766006 R18 17 0.504503259 R27 18 0.500215879 R55 19 0.489517005 R58 20 0.484016571 R59 21 0.474961290 R60 22 0.473420146 R31 23 0.472813964 R51 24 0.472375635 R7 25 0.467873551 R24 26 0.467045674 R20 27 0.455194614 R12 28 0.453784050 R35 29 0.449556899 R49 30 0.446141966 R48 31 0.444929380 R34 32 0.443374273 R57 33 0.438709771 R16 34 0.431465487 R8 35 0.429148738 R22 36 0.425613901 R17 37 0.425250493 R5 38 0.411072505 R46 39 0.408179181 R54 40 0.406758924 R33 41 0.400437626 R52 42 0.399121735 R32 43 0.396657799 R19 44 0.394833193 R1 45 0.393538366 R6 46 0.387904518 R38 47 0.386201044 R11 48 0.382785861 R15 49 0.378026694 R29 50 0.372115534 R39 51 0.369306445 R36 52 0.369055533 R37 53 0.368268961 R4 54 0.359377965 R44 55 0.358163682 R41 56 0.357608058 R14 57 0.354201127 R25 58 0.353154447 R23 59 0.320541156 R21 60 0.314084336 R42 [WORSE]
- 6. International Journal of Management Research and Development (IJMRD) ISSN 2248-938X (Print), ISSN 2248-9398 (Online) Volume 4, Number 1, January-March (2014) 45 CONCLUSIONS From this research work, following conclusions are drawn: For this research work, three types of stakeholders are selected: Ready Mixed Concrete Plant Manager, Consultants, and Contractors. Targeted cities were Anand, Nadiad, Vadodara, and Ahmedabad. According to population, Sample size was calculated as 60 responses. The problem solution result shows: R3>R2>R45>R47>R56>R13>R30>R43>R26>R53>R9>R50>R10>R40>R28>R18> R27>R55>R58>R59>R60>R31>R51>R7>R24>R20>R12>R35>R49>R48>R34>R57>R16>R8 >R22>R17>R5>R46>R54>R33>R52>R32>R19>R1>R6>R38>R11>R15>R29>R39>R3 >R37>R4>R44>R41>R14>R25>R23>R21>R42. Therefore Respondent no. 3 (R3) is best because of its largest value and Respondent no. 25 (R25) is worse because of its smallest value. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) can be effective MCDM tool for ranking and relative closeness of the Ready Mixed Concrete. The proposed methodology can also be applied to any other selection problem involving multiple and conflicting Criterias. ACKNOWLEDGEMENT The Authors thankfully acknowledge to Dr. C. L. Patel, Chairman, Charutar Vidya Mandal, and Er. V. M. Patel, Hon. Jt. Secretary, Charutar Vidya Mandal, Dr. F. S. Umrigar, Principal, B.V.M. Engineering College, Prof. J. J. Bhavsar, Associate professor and coordinator PG (Construction Engineering & Management), Civil Engineering Department, B.V.M Engineering College, Er. Yatinbhai Desai, Jay Maharaj Construction, Vallabh Vidyanagar, Gujarat, India for their motivations and infrastructural support to carry out this research. REFERENCES 1. A.R.Santhkumar, “Concrete Technology”, Oxford higher education. 2. Alessio Ishizaka, Philippe Nemery, “Multi-Criteria Decision Analysis: Methods and Software” (2013), John Wiley & Sons. 3. Ashish H. Makwana, Prof. Jayeshkumar Pitroda, “An Approach for Ready Mixed Concrete Selection for Construction Companies through Analytic Hierarchy Process”, International Journal of Engineering Trends and Technology (IJETT), ISSN: 2231-5381, Volume-4, Issue-7, July 2013, Pg. 2878 - 2884. 4. Ashish H. Makwana and Prof. Jayeshkumar Pitroda, 2013, “Ready Mixed Concrete Selection for Infrastructure Development through Analytic Hierarchy Process (AHP) in the New Millennium”, International Journal of Management (IJM), Journal Impact Factor (2013): 6.9071 (Calculated by GISI), Volume: 4, Issue: 5, Pages: 109-126. 5. Ashish H. Makwana, Prof. Jayeshkumar Pitroda, “An Approach for Ready Mixed Concrete Selection For Construction Companies through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Technique”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Impact Factor: 1.00, ISSN: 2278-3075, Volume-3, Issue-5, October 2013, Pg. 92 – 96. 6. Bhavik K. Daxini, Prof. (Dr.) R.B. Bhatt, Prof. Jayeshkumar Pitroda, “An Approach for Supplier Selection for Construction Companies through Analytical Hierarchy Process”, IJSR– International Journal of Scientific Research, Volume: 2 | Issue: 5 | May 2013 • ISSN No. 2277 – 8179. 7. Chang, K.F, C.M. Chiang and P.C. Chou, 2007, “Adapting aspects of GBTool 2005 - searching for suitability in Taiwan, Building and Environment”, 42: 310-316.
- 7. International Journal of Management ISSN 2248-9398 (Online) Volume 4, N 8. Dweiri, F. and F.M. Al-Oqla, International J. Computer Applic 9. Evangelos Triantaphyllou, “Mu (Applied Optimization, Volume 10. Hwang, C.-L., and Yoon, K. Applications”, New York: Sprin 11. Lee, G.K.L. and E.H.W. Chatt assessment of urban renewal pro 12. M.S. SHETTY, “Concrete Tech 13. Yoon, K. P.; Hwang, C.-L., “ Publications, California, 1995. AUTHOR’S BIOGRAPHY Ashish Haren Gujarat. He re from the Cha Technological Master's Degr Vishwakarma published pape Prof. Jayeshku his Bachelor Vishvakarma received his M from Birla Vi Birla Vishvak is Assistant Pr of 12 years in M.E. (Constru Civil/ Constr Conferences a nt Research and Development (IJMRD) ISSN 2248 , Number 1, January-March (2014) a, 2006, “Material selection using Analytic Hie lications in Technol, 26(4): 182-189. ulti-Criteria Decision Making Methods: A Com e 44). . (1981), “Multiple Attribute Decision Makin inger-Verlag. att, 2008, “The Analytic Hierarchy Process (AH roposals”, Soc. Indi. Res., 89: 155-168. chnology, Theory and Practice”, S.Chand- New D “Multiple Attribute Decision Making: An Intr endrabhai Makwana was born in 1988 in Ja received his Bachelor of Engineering degree in C harotar Institute of Science and technology in al University in 2012. At present he is Final gree in Construction Engineering and Manage a Mahavidyalaya, Gujarat Technological Uni apers in National Conferences and International Jo kumar R. Pitroda was born in 1977 in Vadodara C r of Engineering degree in Civil Engineering a Mahavidyalaya, Sardar Patel University in 20 Master’s Degree in Construction Engineering a Vishvakarma Mahavidyalaya, Sardar Patel Unive akarma Mahavidyalaya Engineering College as a Professor of Civil Engineering Department with a in the field of Research, Designing and educatio truction Engineering & Management) Thesis wor struction Engineering. He has published pap and International Journals. 48-938X (Print), ierarchy Process”, omparative Study” ing: Methods and HP) approach for Delhi. ntroduction”, Sage Jamnagar District, Civil Engineering n Changa, Gujarat al year student of gement from Birla niversity. He has Journals. a City. He received ng from the Birla 2000. In 2009 he and Management iversity. He joined a faculty where he h a total experience tion. He is guiding ork in the field of apers in National