ESTIMATED MODEL FOR CONTRACTOR USING NEURO-FUZZY

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 7, July 2018, pp , Article ID: IJCIET_09_07_102 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed ESTIMATED MODEL FOR CONTRACTOR USING NEURO-FUZZY Qurrotus Shofiyah Civil Engineering Department, Sepuluh Nopember Institute of Technology, Surabaya, Indonesia Tri Joko Wahyu Adi Civil Engineering Department, Sepuluh Nopember Institute of Technology, Surabaya, Indonesia Mat Syaiin Automation Engineering Department, Shipbuilding Institute of Polytechnic Surabaya, Indonesia ABSTRACT In bidding process, the important decissions are to bid or not to bid for a project and to determine how much bid price to allocate, if then decide to bid. There decisions influenced by many factors, but the decision also based on the contractor s instinct. Bid price becomes very important because company s profit come from here. Wrong bid price will make a a right decission to bid became useless. Then its value must be low enough to ensure a good chance of winning the tender and high enough to gain profit from it. The purpose of the study is to identify the factors that infuence bid/no bid and bid price decision among general contractors bidding on construction project and to get the optimum bid price that can be proposed in bidding. Identification of these factors will be done by distributing questionnaires to contractors who participate in the government construction project tenders. Furthermore, the data will be analyzed using relative importance index to get the rank of the factors. Then the top five of the factors will be the input in the estimated model for contractor using neurofuzzy. Neuro-fuzzy is a combination of neural network and fuzzy logic, which aims to benefit from both methods by covering each other s deficiencies. This study has found that the most important factors on bid/no bid decisions are expected profits, project size, contractor financial ability, historical data of profit/loss on similar projects, and experience on similar projects. While the most important factors on the decision to determine the bid price are expected profits, project size, project cost, project location, and historical data of profit/loss on similar projects. The model decide to bid/no bid in tender as well as determining how much the bid price can be submitted. Model achieves 100% accuracy for bid/no bid decission and 94,48% accuracy for bid price decission. Modeling has a rules combination with a winning probability is 75% editor@iaeme.com

2 Qurrotus Shofiyah, Tri Joko Wahyu Adi, Mat Syaiin Key words: Bid/no bid, bid price, bidding factor, neuro-fuzzy, strategic decision. Cite this Article: Qurrotus Shofiyah, Tri Joko Wahyu Adi, Mat Syaiin, Estimated Model for Contractor Using Neuro-Fuzzy, International Journal of Civil Engineering and Technology, 9(7), 2018, pp INTRODUCTION A contractor firm need to winning a construction bidding and making profit for survive. In bidding process, the important decissions are to bid or not to bid for a project and to determine how much bid price to allocate, if then decide to bid. There decisions influenced by many factors, but the decision also based on the contractor s instinct. This is because in the tender process there is not much time available, so the contractor is required to decide in a short time [1]. Previous study tries to get the factors that influence the contractor's decision either in determining bid/no bid or in determining the bid price. However, the many factors that influence the contractor make it harder to decide. The different factors that influence the bid/no bid and the bid price make it more complicated. Factors influencing the contractor's decision to bid/no bid such as need for work, project profitability, strength of firm, and client's financial ability. While the factors that influence the bid price decision such as competition and risk [2]. In addition to determining the bid price to be submitted is also an important thing that must be decided by the contractor. To determaining that, contractor face the difficult conditions. On the one side, the contractor wants to offer a high bid price with expectation of earning a large profit, but the contractor may lose the tender, considering the winner is a contractor with the lowest bid price. On the other side, the contractor wants to put a low bid price with expectation of winning the tender, but the project may cost overrun. Then its the value must be low enough to ensure a good chance of winning the tender and high enough to gain profit from it. The previous studies have tried to develop a model to get an optimum bid price. In the early development of the model formed an analytical model by considering historical project cost data and contractor bidding behavior. The model can determine the mark-up of the project with the probability of winning in the tender [3]. But the disadvantage of the analytical model is the simplification of the parameters used in determining the bid price makes the accuracy of the model diminish. In addition the model fails to capture the influence of objective and subjective factors in determining the bid price [4]. The next development model is computational models. One is a neural network based model. This model can measure both quantitative and qualitative factors. These factors include job complexity, durations, cost, economy status, firm's need for work, etc. Neural network will get the combination in determining output in the form of mark up and bid outcome (win/lose) [5]. But the disadvantage of this model is the result obtained is the best predictor of the learning process undertaken by neural network, so the output is not the optimum value [4]. In addition, other models developed based on fuzzy logic. Fuzzy logic has been widely used in the decision-making process, one of them in the tender process. In the process of tendering many factors that are uncertain and ambiguous, besides the existence of time constraints makes the contractor difficult to make the right decision. Fuzzy logic can measure the uncertainty and ambiguity, but the disadvantage is the more factors are taken into account the longer the rules need to be made, so it will take a long time to resolve [6] editor@iaeme.com

3 Estimated Model for Contractor Using Neuro-Fuzzy By combining the two components of soft computing forming the neural network and fuzzy logic, it is expected to complement each other's weaknesses with the advantages possessed by each component. The weakness of neural network in producing the result of the best prediction of learning process conducted by neural network can be overcome with fuzzy by determining the rules in the learning process. While the fuzzy weaknesses in the process of forming the rules can be automated by the neural network with the learning ability it has. 2. METHODOLOGHY 2.1. The Factors that Influence the Contractor's Decision In the bidding process, two important decisions that the contractor must decide are to bid or not to bid and to determine the bid price if it decides to bid. It is necessary to find out what factors that influence the contractor's decision in the bidding process. Factors derived from the process of study literature on previous research and then carried out the distribution of questionnaires to contractors who follow the general tender process of transportation facilities construction projects organized by the government of East Java, Indonesia to get the order of factors that have the highest influence. The analysis uses relative importance index so that the rank will be obtained. Importance ratings were from 0 to 5, where 0 represented of no importance and 5 represented of most importance Neuro-fuzzy Based Modeling The five main factors that each have the highest influence on bid/no bid decisions and in bid price decission are used as an input in the model to be developed. The developed model consists of two stages, where each stage will be completed with neuro-fuuzy. The first stage is to decide to bid/no bid in the bidding process, and then the second stage is to determine the bid price that can be submitted if it decides to bid. The proposed model to be developed is shown in Figure 1. Figure 1 Propose Model for Estimated Model for Contractor Using Neuro-fuzzy Neuro-fuzzy or so-called adaptive neuro fuzzy inference system (ANFIS) is a soft computing that combines neural network components and fuzzy logic. Neuro-fuzzy consists of 5 layers in the process of completion as shown in Figure 2 [7] editor@iaeme.com

4 Qurrotus Shofiyah, Tri Joko Wahyu Adi, Mat Syaiin Figure 2 ANFIS Architecture Figure 2 shows that there are 2 inputs x and y and 1 output f. x and y are the inputs of reasoning factors to the contractor's decision in terms of tendering process and the output as a result of bid/no bid and bid price. It is assumed that fuzzy rules are the if-then rule of Takagi and the Sugeno type [8]. Rule 1: if x is A 1 and y is B 1, then f 1 = p 1 x + q i y + r 1, Rule 2: if x is A 2 and y is B 2, then f 2 = p 2 x + q 2 y + r 2. From figure 2 it can also be seen that the neuro-fuzzy process consists of five layers, where each layer will process the rules that have been made. On each layer the rules will be processed using certain equations [7]. In layer 1 is a fuzzification layer where input is defined into several more detailed categories. The advantage of ANFIS is that it can measure something that is linguistic (small, medium, large, etc). The input can have values ranging from 0 to 1. Then it can be concluded that in this layer the process of defining the operator IF takes place. While in layer 2 and 3 process forming of rules by using an operator AND. This is because IF-THEN rules are used following the Takagi and Sugeno type. Next on the fourth layer is the defuzzification layer where the process of determining THEN takes place so that the output will be obtained on the fifth layer Model Validation Model validation is done by implementing the model against the test data. Test data is data outside the data used for modeling. To obtain model accuracy in determining bid/no bid method is applied match-not match, and to get model accuracy in determining bid price got from mean of output difference produced by model with test data. Meanwhile, to know the probability of model can yield the lowest bid by comparing output of bid price generated by model with actual tender winning bidder price Data Collection The data collection process is conducted by interviewing contractors following the tender process of a transport facility construction project organized by the government of East Java, Indonesia. The purpose of the interview is to obtain information about the conditions in accordance with the input and the output required in the development of the model. Then the collected data is divided into two categories, first data used as training model and the second data used as model validation editor@iaeme.com

5 Estimated Model for Contractor Using Neuro-Fuzzy 3. RESULTS AND DISCUSSION 3.1. Characteristic the Respondent Respondents are those who are in charge of decision making in following the tender which is on the top contractor who participate in the tender in the means of obtaining the project. Tender is a construction project of transportation facilities held by the government of East Java, Indonesia Selected Factors for the Final Model From the study literature, there are 49 factors that have influence to the contractor's decision both in bid/no bid and in determining bid price have been found. Furthermore, the questionnaires were distributed and analyzed using relative importance index. The results of the questionnaire and weight of interest are shown in Table 1 for bid/no bid and table 2 for the bid price. Table 1 List of Factors and Their Importance Wight Considered for Bid/No Bid and Bid Price Decission by Contracting Organizations Importance weights No Factor's description Bid Bid/No Bid Price 1 Expected profit 0,953 0,920 2 Project size 0,840 0,813 3 Contractor's financial ability 0,813 0,520 4 Historical profit/lose on similar project 0,787 0,693 5 Experience on similar project 0,787 0,420 6 Firm's ability 0,780 0,447 7 Owner's financial ability 0,773 0,560 8 Design quality 0,773 0,613 9 Project cost 0,753 0, Owner reputation 0,753 0, Availability of other project 0,700 0, Availability of required materials 0,680 0, Project complexity 0,667 0, Current workload of projects relative to capacity of firm 0,667 0, Clarity of contract 0,660 0, Need for work 0,653 0, The level of security required in project implementation 0,580 0, Project risk 0,573 0, Relationship between firm and bank 0,567 0, Project location 0,553 0, Contract specification 0,547 0, Project duration 0,540 0, Amount of another tender 0,540 0, Amount of subcontract 0,540 0, Policies and legislation in the country 0,533 0, Experience and competence of contractor employees 0,527 0, Economic/market condition 0,520 0, Availability of project equipment 0,520 0, Clarity of project technical specifications 0,507 0, Risk to invest 0,507 0, Work environment 0,480 0, The possible number of competitors 0,467 0, Project start time 0,460 0, Continuity of work 0,453 0, Hazard 0,453 0, Characteristics of the contractor 0,447 0, Contractor behavior 0,440 0, editor@iaeme.com

6 Qurrotus Shofiyah, Tri Joko Wahyu Adi, Mat Syaiin Importance weights No Factor's description Bid Bid/No Bid Price 38 Project type 0,420 0, Supervisor 0,420 0, Tax liability 0,413 0, Rate of return 0,407 0, Indirect costs of the project 0,400 0, Relationship with subcontractors 0,393 0, Having qualified subcontractors 0,387 0, Contingency 0,380 0, Premium insurance 0,380 0, Project documentation 0,373 0, Project manager identity 0,367 0, Season of project execution 0,353 0,327 Furthermore, each of the influential factors is taken five factors that have the highest influence. These factors are shown in table 2 for factors affecting bid/no bid and table 3 for factors affecting pricing determination. Table 2 Top Factors Affecting the Bid/No Bid No Factor's description Importance weights 1 Expected profit 0,953 2 Project size 0,840 3 Contractor's financial ability 0,813 4 Historical profit/lose on similar project 0,787 5 Experience on similar project 0,787 Table 3 Top Factors Affecting the Bid Price No Factor's description Importance weights 1 Expected profit 0,920 2 Project size 0,813 3 Project cost 0,793 4 Project location 0,713 5 Historical profit/lose on similar project 0,693 In table 2 and 3 it can be seen that expected profit, project size, and historical profit/ lose on similar project become factors that have big influence on bid/no bid and bid price. The other two factors for bid/no bid are contractor's financial ability and experience on a similar project. While in determining bid price is project cost and project location Estimated Model for Contractor Using Neuro-Fuzzy In the development of the model, the input models used are the five main factors obtained in the previous stage. Then, the modeling will be processed using neuro-fuzzy with 5 layers. The structure of the developed model is shown in figure editor@iaeme.com

7 Estimated Model for Contractor Using Neuro-Fuzzy Figure 3 Estimated Model for Contractor Using Neuro-Fuzzy Architecture By applying neuro-fuzzy to MATLAB software consisting of five inputs with each input categorized into five categories obtained a combination of rules obtained as many as 3,125 rules for each neuro-fuzzy stage. This is shown in figure 4 below. Figure 4 Estimated Model for Contractor Using Neuro-Fuzzy Architecture with MATLAB editor@iaeme.com

8 Qurrotus Shofiyah, Tri Joko Wahyu Adi, Mat Syaiin 3.4. Model Implementation By implementing the model to the actual data it will be able to know the validation and accuracy of the model and the probability of the model to get the lowest bid price. Actual data is the result of a tender process that has been done by the government of East Java, Indonesia with the determination of the winner is the lowest bid price submission of the proposed project cost. The results of the model implementation of the 16 actual data are shown in table 4 below. Table 4 The Result of Model Implementation Actual Condition Model's output Error Bid Decrease in Decrease in Match-Not Bid Bid/No value No. Bid/No bid the bid price the bid match outcome bid (%) (%) price (%) 1 Bid 7,83 Bid 8,59 Match 0,76 WIN 2 Bid 4,14 Bid 11,62 Match 7,49 WIN 3 Bid 4,64 Bid 16,92 Match 12,28 WIN 4 Bid 4,22 Bid 15,25 Match 11,03 WIN 5 Bid 6,11 Bid 7,45 Match 1,34 WIN 6 Bid 10,04 Bid 17,59 Match 7,55 WIN 7 Bid 10,74 Bid 14,75 Match 4,01 WIN 8 Bid 6,63 Bid 5,16 Match 1,47 LOSE 9 Bid 5,53 Bid 11,03 Match 5,51 WIN 10 Bid 6,67 Bid 2,89 Match 3,78 LOSE 11 Bid 5,49 Bid 3,01 Match 2,48 LOSE 12 Bid 4,85 Bid 11,95 Match 7,11 WIN 13 Bid 11,16 Bid 21,64 Match 10,48 WIN 14 Bid 5,27 Bid 5,24 Match 0,03 LOSE 15 Bid 6,71 Bid 16,91 Match 10,20 WIN 16 Bid 5,70 Bid 2,89 Match 2,81 LOSE Averrage of error value (%) 0 5,52 Probability of winning (%) 75 Table 4 shows that the model result in determining bid/no bid has 100% accuracy. While in determining the bid price, accuracy model shows 94.48% with error value of 5.52%. From table 4 it can be seen also that the model resulting the lowest bid 14 times of 16 times tests, meaning the model has a 75% probability of winning the tender. 4. CONCLUSIONS The factors that influence the decision in determining the bid/no bid the most are expected profit, project size, contractor financial ability, historical data of profit / loss on similar projects, and experience on similar projects. The factors that influence the decision in determining the bid price the most are expected profits, project size, project cost, project location, and historical data of profit/loss on similar projects. Model achieves 100% accuracy for bid/no bid decission and 94.48% accuracy for bid price decission. Modeling has a rules combination with a winning probability is 75%. The limitation of the model is the selection of methods used to determine the five main factors that affect the contractor's decision very simply. This allows for other factors that may have the same effect but not became a major factor. So that factor analysis can be used as the method for get the factors that influence in accordance with the more appropriate category (strengt of firm, project, risk, competition, client, etc.) editor@iaeme.com

9 Estimated Model for Contractor Using Neuro-Fuzzy The propose model still require MATLAB software assistance in its application so it is considered less practical to be utilized by the contractor. So this modeling can be used as framework in more practical application development. REFERENCES [1] I. Ahmad and I. Minakarah, Questionare survey on bidding in construction, Jurnal of Management in Engineering, 4(3), 1988, [2] M. Egemen and A. N. Mohamed, A framework for contractors to reach strategically correct bid/no bid and mark-up size decisions, Building and Environment, 42(3), 2007, [3] L. Friedman, A competitive bidding strategy, Operational Research, 4, 1656, [4] S. Christodoulou, Optimum bid markup calculation using neurofuzzy system and multidimensional risk analysis algorithm, 18(4), 2004, [5] T. Hegazy, Practical bid preparation with emphasis on risk assessment using neural networks, doctoral diss, Concordia University, Montreal, Canada, [6] Y. Tan, L. Shen, C. Laangston, and Y. Liu, Construction project selection using fuzzy TOPSIS approach, 5(3), 2010, [7] J. R. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on System, Man, and Cybernetics, 23(3), 1993, [8] T. Takagi and M. Sugeno, Derivation of fuzzy control rules from human operator s control actions, Proc. IFAC Symposium On Fuzzy Information, Knowledge Representation and Decission Analysis, Marseille, France, 1983, [9] Pradeep Singh and Krishan Arora, Improvement of Power Quality (PQ) by UPQC (Unified Power Quality Conditioner) in Power System Using Adaptive Neuro Fuzzy (ANFIS). International Journal of Electrical Engineering & Technology, 7 (2), 2016, pp editor@iaeme.com