Applying analytic network process to evaluate the optimal recycling strategy in upstream of solar energy industry
Highlights
► We developed a novel model to evaluate the optimal recycling strategy. ► We find the optimal strategy is “In-House”. ► We identified the critical cluster and a criterion for the case is “Benefits” and “Economic/Financial Benefits”. ► We combined the BOCR and BSC model to establish the network model, and then determine the weight by using ANP method. ► We proposed a reality case study to test the ANP model, and then the results provide the administrators reference.
Introduction
In recent years, the solar energy industry (SEI) is one of the most booming and cleanest resource industries in the world. The main advantage of using solar energy is its ability to access clean and environmentally friendly electricity without consuming fossil fuels [1], [2]. Solar energy does not deplete natural resources, does not, cause CO2 or other gaseous emissions to diffuse into the air, and does not generates liquid or solid waste products [2]. Concerning sustainable development, the advantages of using solar energy, whether directly or indirectly derived, include following [2], [3], [4], [5]:
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No emissions of greenhouse (mainly CO2, NOx) or toxic gasses (SO2, particulates);
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Reclamation of degraded land;
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Reduction of transmission lines from electricity grids;
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Improvement of quality of water resources;
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Increase of regional/national energy independence;
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Diversification and security of energy supply;
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Acceleration of rural electrification in developing countries.
Consequently, literatures approved that SEI is vital to the trend of global environmental concerns. Many countries are actively developing, enhancing and installing Photovoltaic (PV) electricity [2] (see Table 1); scholars also forecast that the quantity of PV installations will generate 70,000 Megawatts (MW) in 2020 and 140,000 MW in 2030.
The SEI has a highly complicated business structure. The supply chain of the SEI requires processing certain materials and includes at least the following five steps: (1) raw silicon material, (2) wafers/ingots, (3) PV cells, (4) PV modules and (5) PV system. The upstream is step 1 and step 2; midstream is step 3 and downstream is step 4 and step 5 as shown in Fig. 1 [1], [6], [7], [8]. Research papers published by the European Photovoltaic Industry Association (EPIA) [9] showed the trend that the numbers of companies in the PV supply chain are decreasing moving from downstream to upstream (Table 2). At the same time, more customers in the PV supply chain are moving from upstream to downstream. According to Table 1, the installations of the PV systems reached 70,000 MW in 2020 and 140,000 MW in 2030. Pavlović et al. [10] showed that about 20,000 m2 of space is required to install a fixed 1 MW PV solar plant. Thus, the installation of PV plants will occupy very large parcels of land. Additionally, Solar PV modules contain hazardous materials such as cadmium, tellurium, lead and selenium. Cadmium compounds are, for example, currently regulated in many countries because of their toxicity to fish and wildlife and because they can pass to humans through the food chain [11]. Cadmium has also been associated with numerous human illnesses particularly lung, kidney and bone damage and once absorbed in the body; cadmium can remain for decades [12]. These chemicals materials or waste are very dangerous in human future life and global environment. That may become a big environmental problem when these plants are broken. Nowadays, more and more countries are paying attention to the environmental issues such as global warming, the greenhouse effect, etc. Environmental responsibility plays a significant role in the agendas of every corporation [13]. Government regulations have forced corporations to take into account environmental concerns when modifying their supply chain operations [14], [15], Some countries have enacted strict legislations with strong the environmental consciousness, such as waste electrical and electronic equipment (WEEE), Restriction of the use of certain Hazardous Substances (RoHS) and eco-design requirements for energy using products (EUP) and so forth. Therefore, a supply chain network combining the green concepts is becoming increasingly more important in every company. Nativi and Lee [16] discussed how GSCM can provide environmental benefits to businesses. In 2007 Srivastava proposed that green supply chain management (GSCM) be divided into three areas (1) importance of GSCM, (2) green design and (3) green operations; and, pointed out that the key challenge of GSCM is in green operations [17]. Three groups are involved in the green operations: (1) green manufacturing and remanufacturing, (2) reverse logistics (RL) and network design, and (3) waste management (Fig. 2). Thus, this study focused on the group of reverse logistics. In reverse logistics, three fundamental stages of recycling procedures have been established: (1) collection (2) inspection, and (3) processing as shown in Fig. 3 [18], [19]. Therefore, we can see combining the forward/reverse logistics in the SEI (Fig. 4). Other studies have also discussed the problem of collection procedures in RL, which are both important and complex [20], [21], [22]. They indicated that have high uncertainty and complexity in collection of the used products. Because of the client of used products are widely distributed over the country, the collection costs cannot control efficiently. For this reason, some of the articles suggest the best of collecting strategy is outsourcing because of outsourcing logistics functions to third-party reverse logistics (3PL) providers have been a resource of competitive benefit for most companies [23], [24], [25]. On the other hand, some of studies proposed the best collecting strategy should keep collection In-house (IH) because of In-house might provide a certain comfort level to service customer [26], and the most prominent reason for companies to prefer In-house logistics is the risks associated with outsourcing in terms of loss of competencies [27] and hidden costs [28]. Many companies are lack of evaluation of the optimal collecting strategy in their RL processes. Therefore, the purpose of this study focuses on the evaluation of collection strategies in the upstream of the SEI.
The main recycling processes can be divided into collection, sort-test/inspection and processing. The collecting strategies include four categories: outsourcing (OS), IH, purchasing from government organization (PFGO) and joint recycling in business (JRIB). The evaluation of optimal solution is a multi-criteria problem and an ideal model requires suitable criteria and strict screening [29]. In the past, there have been few research studies pertaining to the collection process that uses multi-criteria decision-making methodology to evaluate the optimal collection strategies of recycling processes in the upstream of the SEI. This study focused on the collection process that evaluates optimal strategy for upstream in SEI as shown in Fig. 4.
The benefits, opportunities, cost, and risks (BOCR) model created by Saaty and Ozdemir in 2003 has been widely adopted [30]. Saaty and Ozdemir suggested four ways to obtain the overall rankings of the alternatives: additive, probabilistic additive, subtractive and multiplicative in BOCR. Saaty [31] presented a model to synthesize the priorities of alternatives by using the two formulas, multiplicative and additive subtractive, to combine the priorities of alternatives under B, O, C, R. Therefore, the BOCR model includes four perspectives to measure the various areas. Erdogmus et al. [32], for example, presented an analytic network process (ANP) with the BOCR model to select a renewal transaction processing system for enhanced efficiency and operation quality. Erdogmus et al. [33] applied a similar model as in Erdogmus et al. [32] to evaluate alternative fuels for residential heating in Turkey, and considered the political, economic, social and environmental issues. Saaty and Shang [34] proposed a framework to reshape the group decision-making process so several related issues were considered simultaneously by the group. Saaty and Shang used the BOCR to evaluate alternative options by considering the intensity of each preference to each decision-maker. Ustun and Demirtas [35] proposed a BOCR model to select a suitable supplier. Lee et al. [36] combined the BOCR with the analytic hierarchy process (AHP) to help select a suitable wind farm project. Lee [37] presented a BOCR framework for suppliers in their selection of a TFT-LCD manufacturer. Chang and Liao [38] used the BOCR and the AHP to identify the key factors in the design of chiller system collaborative service. Chen [39] applied ANP to evaluate the intelligence of facilities. Chen et al. [40] employed ANP model for lifespan energy efficiency assessment of intelligent buildings.
This study combines the BOCR and the ANP to evaluate the synthetic utility values of criteria and sub-criteria of the recycling process in the upstream of the SEI, and then assigns a suitable relative weight to each criterion within the multi-attribute decision-making model to rank the optimal strategies. Academically, the ANP-based decision-making method can provide decision makers or administrators of the SEI with valuable guidance for measuring the optimal collection strategies of recycling activities. Commercially, the proposed model can provide administrators with a useful tool to assess the optimal collection strategies in the upstream of the SEI.
Section snippets
ANP methodology
The ANP is the generic form of the AHP that allows for more complex interdependent relationships among elements [41]. It is also known as the systems-with-feedback approach [42]. By incorporating interdependencies (i.e., addition of the feedback loops in the model), a supermatrix is created. The super-matrix adjusts the relative weights in individual matrices to form a new overall matrix with the eigenvectors of the adjusted relative weights [42]. In fact, the ANP uses a network without a need
Case study
This study constructed indicators to evaluate the optimal collection strategies in the upstream of SEI and the processes as shown in Fig. 5. An evaluation model is constructed based on the BOCR/BSC model to assess the optimal collection strategy by using super decision software. This model for evaluating the optimal collection strategy comprises the following steps: Step 1 Construct and define the evaluative criteria based on BOCR/BSC and establish an ANP model
Saaty and Ozdemir [30] presented the BOCR
Conclusion
Nowadays, the environmental problem is an important issue in the international community. The development of natural energy has become imperative for every country. Even though solar energy is by far the cleanest method to generate electricity, the waste and used products generated by the PV plants cause environmental pollution. To make SEI the cleanest industry, the general public is working together with the government to enforce strict measures to ensure that their quality of life is not
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