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SUMMARY
In the petrochemical industry, the choice of contractors will be relevant to the company's production equipment, utilization rate and availability rate, the production schedule of preparation and arrangements. In addition, contractors of industrial accidents will affect the company's operational risk. How to properly and effectively choose the best contractor is the focus of the research.
In the field of supply chain management, supplier selection, there are many choices of criteria and research methods, mostly which use Multi-Criteria Decision Making (MCDM) methods to construct the appropriate decision-making model. To select using the contractor selection criteria and MCDM method, a comparative analysis to explore the literature is described. When coupled with group decision (Group Decision Making, GDM) and Fuzzy Set Theory (FST), the analysis method and decision- making process are more complex, but also can improve the quality of decision-making.
In this research, by using AHP and Fuzzy TOPSIS evaluation methods among MCDM, it uses AHP crisp value as decision makers for “Rating” steps, and to reduce criteria for the number of pairwise comparisons and fuzzy complex calculations. After the project supervisor evaluates the contractor sub-criteria semantic score, and Positive Ideal Solution (PIS) and Negative Ideal Solution (NIS) are calculated, and then based on each contractor’s PIS and NIS distance, the contractors are ranked. A contractor is selected to provide the best advice.
Key word：Contractor selection、FMCDM、Fuzzy TOPSIS、AHP
INTRODUCTION
The petrochemical industry is a capital- and technology-intensive, economies of scale and market oligopoly industry, but also consumes large amounts of energy, produces environmental pollution, and is considered a high-risk industry. Because petrochemical raw materials are mostly combustible, flammable, explosive and toxic substances, industrial safety and accident risks are higher, compared to other industries. Therefore, the choice of contractors in the petrochemical manufacturing contractor management should be at the highest consideration.
This research focuses on important engineering / project engineering before awarding procurement contracts, for the selection of contractors. First, according to the group decision makers, selection criteria and weights, followed by using the fuzzy MCDM methods, to provide the priority for evaluating and sorting contractors properly, also provide bargain reference for the purchasing department.
Literature reviews focused on three topics. First, the supplier selection and the contractor selection of literature of evaluation choice criteria analyze the differences and importance of their selection of criteria. Second, a description and discussion on multi-criteria decision-making (MCDM) methods have methodology and the application of the methodology to perform MCDM to collate and analyze relevant literature for supplier selection and contractor selection. Third, the MCDM method adds Fuzzy Set Theory (FST), combined with the relevant methodology described in the literature, to investigate contractor selection.
Research methods explain contractor selection, decisions to use criteria and weights of settings, and the use of AHP and Fuzzy TOPSIS methodology to introduce principles and applications for the selection of contractors of priority selection. Example of verification is when the company will verify the case of a project contracting, the practical application of research methods, and the methodology used in this research. From the questionnaires, individual and group decision-makers provide data and decision criteria weights to obtain and calculate the optimal solution. Sensitivity analysis verifies the stability and consistency of research methods, in line with expectations decision maker. Finally, conclusion reconfirmed the results of this research.
RESEARCH METHODS
First, experts were selected in various fields as a group decision-making team, according to literature about the supplier selection and contractor selection of choice according to evaluation criteria, by (pre-test) questionnaire to assess the integration of experts team, determine main criteria and sub-criteria to choose contractor, the use of AHP, perform pairwise comparisons between the criteria, then a (post-test) questionnaire group decision by expert team, determine the main criteria and sub-criteria weights, rating completion of the first phase.
Furthermore, each of the important engineering or project engineering supervisor, uses Fuzzy TOPSIS method to evaluate each contractor. The contractors were selected through a nine-scale linguistic variables to measure the value of sub-criteria semantic score and can be obtained after each consolidated a contractor's overall score (performance).
RESULTS AND DISCUSSION
Figure 1、the main criteria and sub-criteria framework of contractor selection
AHP calculated criteria weights
In this research, according to the case’s Engineering Department, 11 senior executives are composed of as expert team to do the pre-test questionnaire, selection of criteria for main and sub-criteria. According to Dickson's 23 selection criteria and those the supplier selection and contractor selection from the literature of the final evaluation structure includes six main criteria and ten sub-criteria. Figure 1 shows the main criteria and sub-criteria framework of contractor selection.
The numbers in Figure 1 show the main criteria and sub-criteria weight values. Five experts from the term are selected to fill post-test questionnaire. The Expert Choice software is used to help analyze the consistency test. As shown in Table 1, five experts evaluation CI and CR values as well as CIH and CRH values of the overall level are less than 0.1 which meet the requirement of consistency.
Table 1、CI and CR values of individual experts Versus CIH and CRH values of the overall level
Fuzzy TOPSIS translated composite score
Using a power engineering company's example as an empirical case. Fuzzy TOPSIS analysis is performed in a step-by-step way as follows：
Step1：Set 9-scale fuzzy linguistic measure value
Two kinds of linguistic variable sets are constructed, as listed in Table 2.
Table 2、Linguistic variables and fuzzy number
Step2：Construct fuzzy decision matrix ─score of contractors
In this example, there are five contractors to be evaluated. Table 3 shows the linguistic ratings, which are then translated as fuzzy numbers based on linguistic variable in Table 2.
Table3、Contractors basic information, quotes, and supervisor score sheet
Table4、Fuzzy number score sheet (Decision Matrix )
Step3：Calculate the weighted decision matrix ─ sub-criteria weights, and composite score
Based on weights of sub-criteria and Table 4, the weighted decision matrix is obtained using , as shown in Table 5.
Table5、The weighted decision matrix
Step4：Determine FPIS and FNIS
According to the characteristic of each sub-criteria, the FPIS( ) and FNIS( ) are shown below：
[(1,1,1), (1,1,1), (1,1,1), (1,1,1), (1,1,1), (1,1,1), (1,1,1), (1,1,1), (0,0,0), (1,1,1)]
=[(0,0,0), (0,0,0), (0,0,0), (0,0,0), (0,0,0), (0,0,0), (0,0,0), (0,0,0), (1,1,1), (0,0,0)]
Step5：Compute the distance of each contractor from FPIS and FNIS using vertex method
According to the formula： , calculate the alternatives (contractors) distance from FPIS and FNIS.
Step6：Calculate each alternative (contractor) relative closeness degree CCi * with FPIS
Calculate each alternative (contractor) relative closeness degree CCi * with FPIS by the formula： , as shown in Table 6.
Step7：Sort alternatives (contractors)
Based on the closeness degrees, the priority of contractors is ranked as B〉 D〉 A〉 C〉 E.
Table 6、Each alternative (contractor) relative approximation with FPIS
Analysis of sensitivity
By exchanging weight values of 10 sub-criteria pairwisely, we can have 45 kinds of combinations. The relative closeness degree (CCi *) is calculated from each combination, the numerical results are shown in Table 7, Figure 2 shows the numerical analysis.
Table 7 and Figure 2 show that a combination 5 (w1 and w6 are exchanged) and combination 7 (w1 and w8 are exchanged), “A” contractor can get the highest CCi * value, while “B” contractor is the best in the remaining 43 combinations. Showing the results with the stability and consistency.
Figure 2、Sensitivity analysis curve (CCi*=0.13~0.19)
Table 7、Sensitivity analysis table
Research results
1. Construct the model of engineering contractor selection mechanism for petrochemical industry, which is different from other industries supplier selection and contractor selection literature.
2. Combine AHP and Fuzzy TOPSIS to propose a new framework to select contractor, making the appraisal objective.
3. The results from demonstrated example are consistent with the expectation of the professionals in the real world, confirming the approach effective.
4. Sensitivity analysis, shows the stability and consistency of the validation methodology.
CONCLUSIONS
This research considers the processes for making decision of the contractor selection in petrochemical industry. The characteristics of this research are to integrate expert opinions, AHP, Fuzzy TOPSIS and considered as effective solution to the problem on industry practices.
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中文部分：
網站
工業技術研究院-產業經濟與趨勢研究中心(IEK)，https://www.itri.org.tw/chi/iek/
六輕工安事件資訊網，http://fpcc.yunlin.gov.tw/index.aspx
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經濟部統計處，http://www.moea.gov.tw/Mns/dos/home/Home.aspx
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范振誠(2013)，2013石化產業年鑑，經濟部技術處 產業技術知識服務計畫(ITIS)，台北
張紹勳(2012)，模糊多準則評估法及統計，五南，台北
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研討會論文
陳振東、黃淑芬、林宜慶(2006)，以語意計算為基礎的模糊決策分析模式建構之研究，2006全球化暨國際企業研討會論文集，pp.93-111
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馮正民(2006)，多準則決策分析方法簡報，交通大學交通運輸研究所，新竹
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