CHEMICAL ENGINEERING TRANSACTIONS VOL. 62, 2017 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Guest Editors: Fei Song, Haibo Wang, Fang He Copyright © 2017, AIDIC Servizi S.r.l. ISBN 978-88-95608- 60-0; ISSN 2283-9216 Evaluation of Logistics Resource Allocation Efficiency in Chemical Industry Base Yamei Pan Henan Industry and Trade Vocational College, Xinzheng 451191, China yameipan2008@163.com In order to verify the effect of logistics resource allocation efficiency on the production efficiency of chemical enterprises, the logistics resource allocation index system of chemical base is established by analyzing the logistics system and logistics resources of chemical base. Data envelopment analysis (DEA) is used to establish the evaluation model of resource allocation efficiency of logistics system in chemical base. Taking a large chemical industry base as the research object, the resource allocation efficiency of the chemical base is analyzed by using the evaluation system and model of the logistics resource allocation efficiency. The results show that the research on resource allocation efficiency of logistics system in chemical base can reduce resource redundancy. By adjusting the input structure of resources, the cost can be saved, and the benefit and industrial demonstration effect of chemical base can be improved. 1. Introduction Because the normal production of the project in the chemical base needs a lot of raw and auxiliary materials, the logistics cost accounts for a high proportion of the total production cost of the base (Thomas, et al., 2013). At the same time, the chemical base logistics system is a multi-input and multi-output system with dynamic, complex and specific. The rational allocation of logistics resources is of great significance to guarantee the efficient operation of the base production organization, optimize the industrial system, reduce the cost, and improve the demonstration effect of the chemical base (Kazuyo and Tetsuya, 2015). In the evaluation of chemical production logistics system, some scholars have applied AHP (Ozsakalli et al., 2014) and fuzzy mathematics methods (Thies et al., 2017), but these evaluation methods have strong subjectivity. The comparison and judgment process are rough and the precision is poor. However, the DEA method does not need the weight hypothesis. It can exclude the subjective factors with strong objectivity (Qiong et al., 2014). This method can solve the problems of diversification of input and output indexes, difficulty of homogenization and difficulty to be measured with unified standards. The evaluation model of resource allocation efficiency of logistics system in chemical base is established by using DEA method. Through empirical research, the effectiveness of resource allocation in different chemical bases is tested, which provides theoretical support for efficient resource allocation of logistics system in chemical base. 2. Construction of evaluation model of logistics resource allocation efficiency in chemical base 2.1 Analysis and construction of evaluation index Although the raw materials and auxiliary materials of chemical base are relatively single, the supply and logistics system have higher stability. However, the logistics process of chemical base is a flow with complex multi -link and multi-process, and the resources needed to be invested are more complex. Because of the large equipment used in the production of chemical projects, the cost is high and the operation is complicated. The process is closely connected, the operation continuity is strong, and the requirement of time condition is high. Therefore, the import of major raw materials and fuels is particularly important (Yong et al., 2017). Based on the full analysis for logistics resources of chemical bases, the transportation resources, storage resources, DOI: 10.3303/CET1762248 Please cite this article as: Yamei Pan, 2017, Evaluation of logistics resource allocation efficiency in chemical industry base, Chemical Engineering Transactions, 62, 1483-1488 DOI:10.3303/CET1762248 1483 loading and unloading resources and circulation processing resources of the chemical base logistics input link are taken as input indexes, while the demand for coal is taken as output index. The detailed division is shown in table 1. Table 1: Evaluation Index System of Logistics Resource Integration in Chemical Industry Base Indicators Data sources Resource input Transport resources Calculated Storage resources Calculated Loading and unloading resources Calculated Circulation processing resources Calculated Logistics output Resource demand Calculated 2.2 Application steps of DEA DEA is used to build evaluation model of logistics resource allocation efficiency in coal chemical industry base. First, the application steps of DEA method need to be defined, and the general application process is shown in figure 1. Determine the purpose of evaluation Filter and identify decision units Establish input and output index system Create DEA model DEA analysis Result analysis Determine the result Re-adjust the input and output indicators Y N Figure 1: DEA general application flow chart Through the research on the general application framework of DEA, the systematic analysis steps for evaluating the efficiency of logistics resource allocation in coal chemical industry base by using DEA are obtained: Clear analysis purpose: DEA analysis technology is used to analyze the allocation of logistics resources among the various projects in the coal chemical industry base, and the overall re planning of the logistics resources of the base is carried out according to the analysis results (Martha, et al., 2016). DEA is used to reevaluate the efficiency of logistics resource allocation after overall planning and the optimal allocation strategy of logistics resources in the base are summarized. Determine the decision-making unit: According to the different purposes of each evaluation, the decision- making units including common external conditions, common input and output indicators are selected, such as each coal chemical project, different time periods. Establish an input / output index system: The input/output index system relies on DEA technology, so the definition of input and output should be defined. A specific analysis is carried out according to the logistics resources involved in the base logistics circulation. Combined with the particularity of the base logistics, the 1484 key resources are screened so that the evaluation can be more meaningful. Usually, the scarce resources are the input, and the measure of the actual product or the achievement of the target is the output. Determine the DEA model: Because there are many models in DEA, different problems should be chosen according to the purpose of the research and the characteristics of each model. Analyze the result: According to the analysis results, the improvement direction and degree of non-priority DMU are obtained, so as to provide decision-making reference for base management. 2.3 Construction of DEA evaluation model The basic idea of establishing the resource allocation evaluation model of the logistics input system in the coal chemical industry base is: The logistics input system of each coal chemical project is regarded as a decision- making unit. Through the comprehensive analysis of input and output indicators with different dimensions, the model is established with the input and output index weights as variables, and then the validity of the allocation of resources is tested by comparison. Assuming that there are n coal chemical projects, the logistics input system of each coal chemical project has m types inputs and s types output. Among them, the i-th input of item j is represented by xij, and the r-th output is represented by yrj. vi denotes the weight coefficient of the input quantity of the i-th resource, and ur denotes the weight coefficient of the r-th output. The efficiency evaluation index hj of the j-th DMU is: nj xv yu h m i iji s r rjr j ,...,2,1, 1 1      (1) Efficiency evaluation (1≤j0≤n) is carried out for the j0-th logistics input system of coal chemical project. The weight coefficient v and u are taken as the variable, and the efficiency of the j0-th decision-making unit is taken as the main goal. The efficiency coefficient of all DMUs is less than 1 for the constraint. An optimization model of relative efficiency evaluation is as follow: 0 0 1 1 2 1 . . 1 max ( ) 1 1, 2,..., ; 0; 0 o s r rj r j m i ij i s r rj r m s t i ij i u y h v y u y C R v y j n u v = = = = ìï ï ï ï ï =ï ï ï ï ï ï ï ï ï ï ï ï ï £í ï ï ï ï ï ï ï = ³ ³ï ï ï ï ï ï ï ï ï ï ïî å å å å (2) The model of formula (2) is a fractional programming problem. In order to solve the problem conveniently, the Charnes-Cooper transform is carried out (Luisa, et al., 2017), and the input relaxation variable s- and the output relaxation variable s+ (Cui and Song, 2017) are introduced respectively according to the dual theory of linear programming. The inequality constraints of formula (2) are transformed into equality constraints, and the following models are obtained: The logistics input system of n project in a chemical base is selected. vi is the input of i logistics resources of chemical project logistics system. ur is the r output (Ewa and Joanicjusz, 2017) of the logistics system of chemical projects. According to table 1, the DEA model of logistics resource allocation in coal chemical industry base is set up, and the specific division is shown in table 2. 1485                                  nj ss ysyts xsx Vsese D j n j jj n j jj D TT ,...,2,1 0,, .. ˆmin )( 1 0 1 0       (3) Table 2: Coal chemical industry logistics input system resource integration evaluation DEA model Project 1 Project 2 …… Project n Weights x11 x12 …… x1n Transport resources v1 x21 x22 …… x2n Storage resources v2 x31 x32 …… x3n Loading and unloading resources v3 x41 x42 …… x4n Circulation processing resources v4 y11 y12 …… y1n Resource demand u1 To sum up, firstly, through analyzing the logistics input system of each project of coal chemical industry base, it is abstracted as the research object (decision-making unit), and then the C2R model of DEA is transformed through linear programming. According to the dual theory of linear programming, the input relaxation variables s- and the output relaxation variable s+ are introduced respectively. The evaluation model of logistics resource allocation suitable for coal chemical industry base is constructed. 3. Case study and analysis Taking a large chemical base in Northwest China as an example, the base was started construction in 2004 and covers an area of 25 square kilometers. By 2020, the base plan has invested 260 billion yuan. 3.1 Analysis of existing input logistics resources capability in chemical industry base Transportation resource capacity. The railway transportation of the logistics system in the coal chemical industry base is based on the local railway of a local professional railway company and the special line in the base. It is responsible for transporting the coal from the mining area to the coal chemical industry base. At present, the comprehensive transportation capacity of the professional railway company is 36 million tons / year, and the daily transportation capacity is 110 thousand tons, amounting to 1818 vehicles. There are 17 diesel locomotives and 581 owned open-wagons operated by the company. Storage resource capability. At present, the coal chemical base has built 3 coal bunkers with 60 thousand tons capacity, and the coal storage capacity is 180 thousand tons. Loading and unloading resource capacity. The coal chemical base uses the double tandem dumper system to unload the coal. The system is equipped with dumper platform and electronic control system. At present, the coal chemical base has 2 sets of double tandem dumper system, and the unloading capacity is 5000 tons per hour. If it works 16 hours a day, the average daily unloading capacity can reach 80000 tons. Table 3: Analysis of Input Logistics Resource Capability in Coal Chemical Industry Day capacity Logistics equipment Transport resources 110000 Gondola Storage resources 180000 Warehouse Loading and unloading resources 80000 Tipping machine Circulation processing resources 110000 Coal washing plant 1486 Circulation and processing resource capability. At present, the coal chemical base has been built into a coal preparation plant with a design capacity of 36 million tons per year. The plant can produce 330 days a year and 16 hours a day. Its daily handling capacity is 110 thousand tons, and the processing capacity is 6818.18 tons per hour. As shown in table 3, through the analysis of the existing input logistics resources in the coal chemical industry base, the analysis table of the input logistics resources capacity in the coal chemical industry base is obtained. 3.2 Evaluation and analysis of input logistics resource allocation efficiency of base projects According to the input logistics traffic statistics data and the current logistics resources in the base, the basic data of the logistics resources and the daily coal demand of each project are obtained in table 4 (Holden, et al., 2016). According to the model established by (3), MATLAB software programming is used to analyze the logistics resource allocation efficiency of the logistics input system of each project planning. The results of analysis and calculation are shown in table 5: Table 4: Item Input / Output Indicator Raw data Decision unit Input resources Output Transport resources Storage resources (%) Loading and unloading resources(min) Circulation processing resources(min) Resource output (t) DMU1 37 1.24 27 21 2234.85 DMU2 103 3.44 75 56 6199.3 DMU3 264 8.80 190 144 15848.49 DMU4 32 1.05 23 17 1890.91 DMU5 1028 34.27 740 558 61688.18 DMU6 44 1.46 31 24 2623.03 DMU7 709 23.65 511 385 42570.91 DMU8 1028 34.27 740 558 61688.18 DMU9 44 1.46 31 24 2623.03 Table 5: The results of resource allocation evaluation of logistics input system of chemical industry Decision unit θ* S1-* S2-* S3-* S4-* S1+* DMU1 1.000 0.000 0.000 0.000 0.000 0.000 DMU2 1.000 0.000 0.000 0.000 0.000 0.000 DMU3 1.000 0.000 0.000 0.000 0.000 0.000 DMU4 1.000 0.000 0.000 0.000 0.000 0.000 DMU5 1.000 0.000 0.000 0.000 0.000 0.000 DMU6 1.000 0.000 0.000 0.000 0.000 0.000 DMU7 1.000 0.000 0.000 0.000 0.000 0.000 DMU8 1.000 0.000 0.000 0.000 0.000 0.000 DMU9 1.000 0.000 0.000 0.000 0.000 0.000 According to the DEA calculation results shown in table 5, the comprehensive efficiency of all decision units is θ*=1 and S-*=0, S+*=0. This shows that the logistics resources input of each project according to the existing logistics resources of the base is effective (Venus, et al., 2015). Its input resources have been more efficient use, that is, the effectiveness of the individual planning of logistics resources allocation is DEA. 4. Conclusion The DEA evaluation model for logistics resource allocation efficiency in chemical base is established, and the logistics input system of a large chemical industry base is taken as the research object to analyze the example. First of all, by analyzing the logistics input system of each project of coal chemical industry base and abstracting it as the research object, the evaluation model of logistics resource allocation efficiency suitable for coal chemical industry base is constructed. Then, the data of each project logistics input system are obtained by sorting out the planning data of each base project. Then, the logistics resources of each project logistics input system are analyzed and calculated by using MATLAB software. The calculation results show that the input of logistics resources and the logistics demand of each project is matching when each project is planned separately. 1487 Reference Cui Y., Song B., 2017, Logistics Agglomeration and Its Impacts in China, Transportation Research Procedia, 25, 3875-3885, DOI: 10.1016/j.trpro.2017.05.302 Ewa C., Joanicjusz N., 2017, Network DEA Models for Evaluating Couriers and Messengers, Procedia Engineering, 182, 106-111, DOI: 10.1016/j.proeng.2017.03.130 Holden R., Xu B., Greening P., Piecykc M., Dadhich P., 2016, Towards a common measure of greenhouse gas related logistics activity using data envelopment analysis, Transportation Research Part A: Policy and Practice, 91, 105-119, DOI: 10.1016/j.tra.2016.06.001 Kazuyo M., Tetsuya N., 2015, Resource Logistics Analysis on Phosphorus and its Applications for Science, Technology and Innovation (STI) Policy, Topical Themes in Energy and Resources, 159-176, DOI: 10.1007/978-4-431-55309-0_9 Luisa M., Juan C.M., Rosa P., 2017, A DEA-logistics performance index, Journal of Applied Economics, 20(1), 169-192, DOI: 10.1016/S1514-0326(17)30008-9 Martha G., Christopher R., Travis G., 2016, Data challenges in dynamic, large-scale resource allocation in remote regions, Safety Science, 87, 76-86, DOI: 10.1016/j.ssci.2016.03.021 Ozsakalli G., Ozdemir D., Ozcan S., Sarioglu B., Dincer A., 2014, Daily Logistics Planning With Multiple 3PLs: A Case Study in a Chemical Company, Journal of Applied Research and Technology, 12(5), 985-995, DOI: 10.1016/S1665-6423(14)70605-4 Qiong L., Chaoyong Z., Keren Z., Yunqing R., 2014, Novel multi-objective resource allocation and activity scheduling for fourth party logistics, Computers & Operations Research, 44, 42-51, DOI: 10.1016/j.cor.2013.10.010 Thies Beinke., Abderrahim A.A., Michael F., 2017, Resource Sharing in the Logistics of the Offshore Wind Farm Installation Process based on a Simulation Study, International Journal of e-Navigation and Maritime Economy, 7, 42-54, DOI: 10.1016/j.enavi.2017.06.005 Thomas L., Kai F., Carsten R., Evi H., 2013, Sustainability management beyond organizational boundaries– sustainable supplier relationship management in the chemical industry, Journal of Cleaner Production, 56, 94-102, DOI: 10.1016/j.jclepro.2011.10.011 Venus Y.H., Kee-hung L., Christina W.Y., Cheng T.C., 2015, Greening propensity and performance implications for logistics service providers, Transportation Research Part E: Logistics and Transportation Review, 74, 50-62, DOI: 10.1016/j.tre.2014.10.002 Yong W., Xiaolei M., Mingw L., Ke G., Yong L., 2017, Cooperation and profit allocation in two-echelon logistics joint distribution network optimization, Applied Soft Computing, 56, 143-157, DOI: 10.1016/j.asoc.2017.02.025 Yu W., Mahmood E., Shahab S., Erin W., Anthony L., 2017, Impact of the biorefinery size on the logistics of corn stover supply – A scenario analysis, Applied Energy, 198, 360-376, DOI: 10.1016/j.apenergy.2017.03.056 1488