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Outsourcing Reverse Logistics for E-Commerce Retailers: A Two-Stage Fuzzy Optimization Approach

Chia-Nan Wang
Thanh-Tuan Dang
1,2,* and
Ngoc-Ai-Thy Nguyen
Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Department of Logistics and Supply Chain Management, Hong Bang International University, Ho Chi Minh 723000, Vietnam
Authors to whom correspondence should be addressed.
Axioms 2021, 10(1), 34;
Submission received: 10 February 2021 / Revised: 10 March 2021 / Accepted: 11 March 2021 / Published: 14 March 2021
(This article belongs to the Special Issue Multiple-Criteria Decision Making)


On the heels of the online shopping boom during the Covid-19 pandemic, the electronic commerce (e-commerce) surge has many businesses facing an influx in product returns. Thus, relevant companies must implement robust reverse logistics strategies to reflect the increased importance of the capability. Reverse logistics also plays a radical role in any business’s sustainable development as a process of reusing, remanufacturing, and redistributing products. Within this context, outsourcing to a third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies for today’s organizations, especially e-commerce players. The objective of this study is to develop a decision support system to assist businesses in the selection and evaluation of different 3PRLPs by a hybrid fuzzy multicriteria decision-making (MCDM) approach. Relevant criteria concerning the economic, environmental, social, and risk factors are incorporated and taken into the models. For obtaining more scientific and accurate ranking results, linguistic terms are adopted to reduce fuzziness and uncertainties of criteria weights in the natural decision-making process. The fuzzy analytic hierarchy process (FAHP) is applied to measure the criteria’s relative significance over the evaluation process. The fuzzy technique for order preference by similarity to an ideal solution (FTOPSIS) is then used to rank the alternatives. The prescribed method was adopted for solving a case study on the 3PRLP selection for an online merchant in Vietnam. As a result, the most compatible 3PRLP was determined. The study also indicated that “lead time,” “customer’s voice,” “cost,” “delivery and service,” and “quality” are the most dominant drivers when selecting 3PLRLs. This study aims to provide a more complete and robust evaluation process to e-commerce businesses and any organization that deals with supply chain management in determining the optimized reverse logistics partners.

1. Introduction

In recent years, reverse logistics has become a key component of any successful streamlined supply chain. Today’s global value chains require greater resilience and efficiencies in the flow of goods between and within countries. Sustainability in the supply chain has become a strategic intent for almost all businesses, and reverse logistics practice is key to the effort. In maximizing the value recovery and safe disposal of waste, reverse logistics expands products at the end of their life cycles through some activities such as resell, refurbish, remanufacture, and recycle, to name a few [1]. Besides optimizing value and sustainability for businesses, the reverse logistics issue that directly impacts supply chains the most is to manage the return of products from the end consumer back to the manufacturer. With online shopping becoming increasingly prevalent, the reverse loop has never been so prominent in global supply chains. In an e-commerce-focused era in which customers are returning products at an increasing rate in various industry sectors, e-merchants and other retailers are at a critical juncture, as any continued lack of focus on reverse logistics will be unsustainable. Online purchases are being returned three times more often than store purchases [2]. This statistic means reverse logistics supply chain management has become a necessity for online merchants to maintain a balanced inventory turn and operating expenses. Additionally, the unprecedented crisis by the Covid-19 pandemic has brought inexorable growth to the e-commerce sector, which is expected to lead to an even higher volume of returned goods. Undoubtedly, this trend will endure in a post-Covid-19 world. Accordingly, reverse logistics has a significant effect on customer relationships and, most significantly, leads to sustainability and long-term profitability of business operations.
Expected to reach 603.9 billion USD by 2025 [3], reverse logistics, when optimized, can increase customer satisfaction and return on investment (ROI). Simply put, reverse logistics in e-commerce refers to the return process, which is the collection of all activities of goods that move in the reverse direction, i.e., from their point of consumption back to the business. The most critical processes are customer support, physical movement of goods, warehousing, triage, repairs, and after-sales support [4]. An easy and hassle-free return process can gain customer engagement and loyalty. As a result, functional and efficient reverse logistics has become a pivotal element for e-commerce businesses. However, most companies are not able to manage their reverse logistics networks. Due to the complicated process and resource constraints, more and more businesses choose to outsource third-party companies to handle their reverse e-commerce services and optimize the return process. Third-party reverse logistics providers (3PRLP) are delegated to help companies productively manage returned currents of products at optimal cost. Hence, evaluating and selecting the best 3PRLP is an imperative and complicated task that must be undertaken prudently [5] for businesses, especially e-commerce merchants and other retailers, to facilitate an effective reverse logistics process and retain customers.
Given the abovementioned importance of reverse logistics outsourcing to the industries, the relevant literature on potential sectors in developing countries is sparse, taking the e-commerce sector in Vietnam as a good example. This gap has formed our research motivation. The current study aims to address this gap by investigating reverse logistics outsourcing practices in Vietnam. According to Google, Vietnam is ranked the second-fastest-growing e-commerce market in Southeast Asia, following Indonesia [6]. Vietnam’s e-commerce has been growing from about 28 percent in 2017 to nearly half of the population in 2020 [7]. It is also forecast that the country’s e-commerce market will hit 15 billion USD by 2025 [8]. While e-commerce giants in Vietnam such as Tiki, Lazada, and Grab have already developed their own logistics sector (warehousing, packing, shipping, and reverse logistics), most other online merchants (including some major players like Shopee and Sendo) are not able to operate an in-house logistics, therefore opt to delegate these activities to third-party logistics providers. Thus, it creates great opportunities and a promising market for e-commerce logistics. According to the 2017 logistics report of the Ministry of Industry and Trade, Vietnam has about 50 enterprises providing e-logistics services [9]. With the need for fast, immediate, and on-demand delivery from customers who shop online, more and more e-logistics startups have entered this market. Providing the volume of e-commerce transactions increasing, logistics providers have to handle large volumes of returned goods. In Vietnam, wrong shipping addresses (especially in rural areas) and unsuccessful orders are daunting challenges faced by both e-commerce retailers and e-logistics businesses. Moreover, competition in terms of delivery speed and costs are factors that can distinguish the best third-party logistics providers.
Moreover, the literature on outsourcing 3PRLP is still limited because of its recent emergence and demand from stakeholders. This lack, therefore, attracted our attention. In this paper, the authors aim to efficiently assist the decision-makers in evaluating and selecting the most sustainable 3PRLP by proposing a robust hybrid multicriteria decision-making (MCDM) approach. Among MCDM methods, the analytic hierarchy process (AHP) and the technique for order preference by similarity to an ideal solution (TOPSIS) are two classical and most commonly used techniques. Conventional AHP and TOPSIS have been combined in different ways and investigated in many studies. In these methods, the criteria weights are often determined by AHP, and TOPSIS ranks the alternatives. Some exemplary studies are as follows. In the basic combination of the AHP-TOPSIS approach, the evaluations about criteria and alternatives are all supposed to be deterministic numbers [10]. In the integration of AHP and the fuzzy TOPSIS method applied in [11], the criteria weights determined by AHP are real numbers, and the evaluations of alternatives with respect to different criteria are in linguistic terms. Meanwhile, linguistic terms are adopted to evaluate criteria, and real numbers are used to assess alternatives in [12]. As a further improvement to the existing literature of reverse logistics outsourcing in terms of methodologies, this paper combines fuzzy AHP (FAHP) and fuzzy TOPSIS (FTOPSIS), by which linguistic terms in evaluations of both criteria and alternatives are adopted. This adoption’s motive is that experts are often reluctant or unable to assign accurate values during the decision-making process. Thus, they prefer to provide their evaluations in linguistic terms, reflecting their uncertain, ambiguous, and vague judgment. In light of this, fuzzy set theory is a useful method for dealing with uncertainty. The decision model may include unknown, incomplete, and inaccessible information and partially ignorant data. During the whole evaluation process, the linguistic terms are converted into triangular fuzzy numbers.
The research procedure is described as follows. In the first stage, FAHP is applied for determining the fuzzy preference weights of the criteria. The evaluation process’s criteria concern economic factors (quality, cost, lead time, delivery and service, R&D capability), environmental factors (recycle, disposal, reproduction, and reuse, green technology, CO2 emissions), social factors (health and safety, customer’s voice, reputation), and risk factors (operation risk, financial risk). In the next stage, FTOPSIS is used to rank all alternatives, offering the optimized 3PRLPs. An application of selecting 3PRLPS for an e-commerce business in Vietnam is presented, simultaneously demonstrating the suggested method. In doing so, this study makes novel contributions to the field by providing a complete and robust evaluation process for 3PRLP selection in the e-commerce sector for a developing country in Asia which is almost neglected in the literature. As a result, this paper provides a holistic framework for businesses with theoretical and managerial implications as a robust decision support system in solving the reverse logistics outsourcing problem.
The paper unfolds as follows. In the next section, a literature review on reverse logistics outsourcing is reviewed. Section 3 summarizes the materials and methods of FAHP and FTOPSIS used in the paper. Result analysis of a case study in Vietnam is shown in Section 4. Finally, Section 5 offers the managerial insights and conclusions of the paper.

2. Literature Review

Concerning the well-developed status of research on outsourcing 3PRLP, multiple criteria decision-making (MCDM) techniques that simultaneously consider various desired selection criteria in different dimensions have appeared to be promising for this task. Literature and practice show that economic, environmental, and social factors are dominant decision-making variables in selecting a sustainable provider of reverse logistics services [13,14,15]. Sustainable development (economic, environmental, and social aspects) lead organizations to reverse logistics practices (Figure 1) [16]. To determine a qualified provider in the outsourcing process, the proposed evaluation approach and the set of criteria are two quintessential parts [17]. Many notable studies have applied various MCDM techniques that consider different criteria to evaluate and select the best 3PRLP. For example, Tavana et al. [18] proposed an integrated intuitionistic fuzzy AHP and SWOT method for solving the reverse logistics outsourcing problem faced by a company. Their findings indicated that when delegating reverse logistics activities to 3PRLPs, it is the most significant priority for a firm to focus on its core business; meanwhile, reducing costs constitutes one of its least important priorities. In the study of Zarbakhshnia et al. [5], a multiple attribute decision making (MADM) model to rank and select the sustainable third-party reverse logistics providers in the presence of risk factors was proposed, and a realistic case study in the automotive industry was applied to demonstrate the model’s effectiveness. Bai and Sarkis [19] first introduced the use of neighborhood rough set, TOPSIS, and VIKOR as a proper and realistic modeling approach for 3PRLPs evaluation and selection using economic/business, environmental, and social (sustainability) factors.
In the management science and decision-making literature, discussing reverse logistics outsourcing issues has become an increasingly important topic. Various criteria and approaches have been considered in the literature. In terms of criteria, businesses traditionally examine cost, quality, and flexibility [19]. For organizations that seek long-term resilience of reverse supply chains, social and environmental concerns are considered sustainability factors [20,21,22]. Regarding methodologies, numerous evaluation models based on MCDM techniques for outsourcing 3PRLP have been introduced ranging from analytical hierarchy process (AHP) [23,24,25,26,27], fuzzy AHP [28,29], analytic network process (ANP) [30,31,32], fuzzy ANP [33], technique for order preference by similarity to ideal solution (TOPSIS) [19,27,34,35], fuzzy TOPSIS [36], visekriterijumska optimizacija i kompromisno resenje (VIKOR) method [19,31,33,37], step-wise weight assessment ratio analysis (SWARA) [38,39], quality function deployment (QFD) model [28,40], data envelopment analysis (DEA) [26,35,41,42], other MCDM methods [30,31,32,33,34,38,39,43], exact methods (mathematical programming) [26,37,43,44,45], and statistical approaches [40,46,47,48]. Table 1 presents a summary of the literature review on proposed approaches for 3PRLP selection and evaluation problems.
Given several methodologies used in the evaluation and selection of 3PRLPs, it can be observed that AHP and TOPSIS methods are the two typical and most commonly used due to their applicability. The foundation of the AHP is a set of axioms that carefully delimits the scope of the problem environment [49]. Among mathematical weighting methods, the pairwise comparison method in the AHP is an effective procedure to determine the importance of different attributes to the objective. Its understandability in theory, simplicity in application, and robustness of its outcomes have been proven in practice and validated by a diverse range of decision-making problems. The TOPSIS method, first introduced in [50], is one of the most well-known classical MCDM methods. Simply stated, in a geometrical sense, the TOPSIS method simultaneously considers the distance to the ideal solution and negative-ideal solution of each alternative and choosing the closest relative to the ideal solution as the best alternative [27]. In this paper, the authors aim to solve the fuzzy information during the whole evaluation and selection process by considering linguistic terms in both criteria and alternatives, which thereby are converted into triangular fuzzy numbers using the fuzzy set theory. Thus, the gaps in the existing literature are addressed in this paper as follows: (1) methodologically, this is the first study to suggest a hybrid fuzzy decision support system that combines the fuzzy AHP (FAHP) and the fuzzy TOPSIS (FTOPSIS) for the field of reverse logistics outsourcing; (2) in terms of applications, the prescribed approach is used for a case study in Vietnam to support an e-commerce business determining their compatible and sustainable partners for reverse logistics among eight candidates; (3) the managerial implications of this paper provide a comprehensive insight that enables decision analysts to better understand the complete evaluation and selection process of 3PRLPs considering well-rounded aspects. From a broader standpoint, this study can assist e-commerce businesses or any organization to expedite their reverse logistics strategies in this era.

3. Materials and Methods

3.1. Research Process

Decision-making in real-world problems, especially in the evaluation and selection of third-party reverse logistics providers (3PRLP), involves not only quantitative criteria (i.e., cost, lead time) but also quantitative criteria (i.e., the voice of customers, reputation). Fuzzy set theory is useful for handling complex decision-making problems with numerous associated factors. In this paper, among many MCDM models, FAHP and FTOSPS are chosen to select the most efficient providers among alternatives because they are available in decision-making software and allow decision-makers to practice effectively. The process of the paper includes two stages, as can be seen in Figure 2.
In the first stage, FAHP is applied to identify fuzzy preference weights of criteria in the outsourcing reverse logistics problem. The list of criteria used in this paper is selected from the relevant literature review (Table 2) and industrial experts’ survey. There are four main criteria and 15 criteria including economic (quality, cost, lead time, delivery and service, R&D capability), environment (recycle, disposal, reproduction and reuse, green technology, CO2 emissions), social (health and safety, customer’s voice, reputation), and risk (operational risk, financial risk). In the second stage, FTOPSIS is applied to rank all alternatives. This paper proposed a hybrid approach by combining two MCDM models that can improve the decision-making process. Besides, the problem of outsourcing reverse logistics for e-commerce retailers is addressed under uncertainty environment using fuzzy theory that can enhance the robust results.

3.2. Triangular Fuzzy Number (TFN)

Fuzzy set theory was introduced by Zadeh [59] to deal with uncertainty problems. A triangular fuzzy number (TFN) is defined as ( a , b , c ) which denotes pessimistic, most likely, and optimistic values, as shown in Equation (1), and as can be seen in Figure 3.
μ ( x F ˜ ) = { ( x a ) / ( b a ) , a x b ( c x ) / ( c b ) , b x c 0 , o t h e r w i s e
The representative of each level of membership function is denoted, as can be seen in Equation (2).
F ˜ = ( F l ( y ) , F r ( y ) ) = [ a + ( b a ) y ,   c + ( b c ) y ] , y   [ 0 ,   1 ]
where F l ( y ) ,   F r ( y ) denote two sides of the fuzzy number.

3.3. Fuzzy Analytical Hierarchy Process (FAHP)

FAHP is an extension of AHP, which overcomes the drawbacks of AHP, and solves such problems under fuzzy environment. The linguistic expression for fuzzy scale and allocated TFN is shown in Table 3. The procedure of FAHP includes six steps as follows [60].
Step 1: Build a fuzzy pairwise comparison matrix, as can be seen in Equation (3).
M k ˜ = ( e 11 k ˜ e 12 k ˜ e 1 n k ˜ e 21 k ˜ e 2 n k ˜ e n 1 k ˜ e n 2 k ˜ e n n k ˜ )
where e i j k ˜ denotes the important level from kth decision-maker with respect to the ith criterion over the jth criterion using TFN membership function.
Step 2: Assume that a decision group has K experts. An integrated fuzzy pairwise comparison matrix can be calculated as Equation (4).
M k ˜ = ( e 11 ˜ e 12 ˜ e 1 n ˜ e 21 ˜ e 2 n ˜ e n 1 ˜ e n 2 ˜ e n n ˜ )   s u c h   t h a t   e i j ˜ = k = 1 K e i j k ˜   K
Step 3: The fuzzy geometric mean of each criterion is calculated as Equation (5), as follows.
r i ˜ = ( j = 1 n e i j ˜ ) 1 / n s u c h   t h a t   i = 1 ,   2 ,   , n
where r i ˜ denotes the fuzzy geometric mean and e i j ˜ denotes the important level from group of decision-maker with respect to the ith criterion over the jth criterion.
Step 4: Calculate the fuzzy weights of each criterion using Equation (6).
w i ˜ = r i ˜   ( × )   ( r 1 ˜   ( + )   r 2 ˜   ( + ) ( + )   r n ˜ ) 1
Step 5: Defuzzify the fuzzy weight using the average weight criteria M i , as can be seen in Equation (7).
M i = w 1 ˜   ( + )   w 2 ˜   ( + ) ( + )   w n ˜ n
Step 6: Calculate the normalized weight criteria N i as Equation (8).
N i = M i i = 1 n M i

3.4. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS)

FTOPSIS is a very useful MCDM model, which is applied to rank different alternatives based on the distance between the fuzzy positive ideal solution (FPIS) and the fuzzy negative ideal solution (FNIS). The process of FTOPSIS includes seven steps below [61].
Step 1: Identify the fuzzy weights of criteria.
In FTOPSIS model, the fuzzy weights of criteria are obtained from FAHP model.
Step 2: Based on the linguistic rating value in Table 4, develop the fuzzy decision matrix, as can be seen in Equations (9) and (10).
M ˜ = [ x ˜ 11 x ˜ 11 x ˜ 21 x ˜ 22 x ˜ 11 x ˜ 2 n x ˜ m 1 x ˜ m 2 x ˜ m n ]   s u c h   t h a t   i = 1 ,   2 , , m ;   j = 1 ,   2 , , n
x ˜ i j = 1 k ( x ˜ i j 1   ( + )   x ˜ i j 2   ( + ) ( + )   x ˜ i j k )
where x ˜ i j k denotes the fuzzy rating of alternative A i with respect to criteria C j by kth expert, and x ˜ i j k = ( a i j k , b i j k , c i j k ) .
Step 3: In this step, the fuzzy decision matrix is normalized, as shown in Equations (11)–(13).
S ˜ = [ s ˜ i j ] m × n   s u c h   t h a t   i = 1 ,   2 , , m ; j = 1 ,   2 , , n
s ˜ i j = ( a i j c j * , b i j c j * , c i j c j * ) , c j * = m a x i { c i j | i = 1 ,   2 , , m }   f o r   b e n e f i t   c r i t e r i a
s ˜ i j = ( a j c i j , a j b i j , a j a i j ) ,   a j = m i n i { a i j | i = 1 ,   2 , , m }   f o r   c o s t   c r i t e r i a
Step 4: Construct the weighted normalized fuzzy decision matrix u ˜ i j , Equations (14) and (15).
U ˜ = [ u ˜ i j ] m × n   s u c h   t h a t   i = 1 ,   2 , , m ; j = 1 ,   2 , , n
u ˜ i j = s ˜ i j   ( × )   w ˜ j
Step 5: Determine fuzzy positive ideal solution (FPIS) Q * and fuzzy negative ideal solution (FNIS) Q based on Equations (16) and (17).
Q * = ( u ˜ 1 * , , u ˜ j * , , u ˜ n * )
Q = ( u ˜ 1 , , u ˜ j , , u ˜ n )
where u ˜ j * = ( 1 ,   1 ,   1 ) , u ˜ j = ( 0 ,   0 ,   0 ) , j = 1 ,   2 , , n .
Step 6: Calculate the distance ( D ˜ i * and D ˜ i ) of each alternative from FPIS and FNIS, as can be seen in Equations (18) and (19).
D ˜ i * = j = 1 n d ( u ˜ i j , u ˜ j * ) , i = 1 ,   2 , , m
D ˜ i = j = 1 n d ( u ˜ i j , u ˜ j ) , i = 1 ,   2 , , m
Step 7: Calculate the closeness coefficient (i.e., relative gaps-degree) of each alternative, Equation (20). The optimal alternative is closer to the FPIS and farther from the FNIS as C C ˜ i approaches to 1.
C C ˜ i = D ˜ i D ˜ i * + D ˜ i = 1 D ˜ i * D ˜ i * + D ˜ i , i = 1 ,   2 , , m
where D ˜ i D ˜ i * + D ˜ i is fuzzy satisfaction degree, D ˜ i * D ˜ i * + D ˜ i is fuzzy gap degree which presents how fuzzy gaps can be improved to obtain the aspiration levels of decision-makers.

4. Numerical Application and Results Analysis

4.1. Problem Description

Amid the Covid-19 online sales boom, the e-commerce industry has accelerated in every corner of the world [62], including in Vietnam. For this country, the e-commerce industry is rapidly expanding, thanks to increasing foreign investments, a favorable regulatory environment, and enhanced internet access. Investment and development of logistics are among the premises for the expansion and resilience of e-commerce. Among key strategies, outsourcing logistics has been the first choice. Selecting the most suitable partner is the most convenient and economical solution for any e-commerce merchant in Vietnam.
In this paper, a numerical application of an e-commerce business in Vietnam is used to test the effectiveness of the proposed fuzzy MCDM model. After preliminary evaluation, eight potential third-party reverse logistics providers (3PRLP) were selected based on the requirements of the company in order to select a compatible 3PRLP and to ensure a long-term healthy relationship between the two firms. The list of criteria was selected from the relevant literature review and industrial experts’ survey. In the numerical application, a total of 10 experts (head of logistics and relevant departments in this e-commerce business) were consulted by interviews to assess the effect of these criteria on the 3PRLP selection. All experts had more than 10 years’ working experiences in the area of logistics and supply chain. Figure 4 presents the decision hierarchy for outsourcing reverse logistics including all criteria and the list of eight potential 3PRLP. There are four main criteria and 15 criteria including economic (quality, cost, lead time, delivery and service, R&D capability), environment (recycle, disposal, reproduction and reuse, green technology, CO2 emissions), social (health and safety, customer’s voice, reputation), and risk (operational risk, financial risk).

4.2. Results of Fuzzy AHP

The following FAHP procedure shows an example of the calculation of the four main criteria (economic, environment, social, risk). Other criteria are calculated using the same procedures. The initial and integrated fuzzy comparison matrix of the main criteria are presented in Table 5 and Table 6, respectively.
In order to check the consistency ratio (CR) of the rating score, the fuzzy number is converted to a real number using lower bound (pessimistic value) and upper bound (optimistic value) values of the fuzzy comparison matrix [60,63,64]. Table 7 shows the non-fuzzy comparison matrix of the main criteria.
To get the priority vector of the main criteria, the normalized matrix of pairwise comparison is calculated by dividing each number in a column of the comparison matrix by its column sum. In addition, the priority vector is determined by averaging the row entries in the normalized matrix, which are shown in Table 8.
In this step, the largest eigenvector ( λ m a x ) is calculated to determine the consistency index (CI), the random index (RI), and the consistency ratio (CR), as follows.
[ 0.5088 0.4608 0.4106 0.5943 0.1799 0.1629 0.1836 0.1213 0.1314 0.0941 0.1060 0.0743 0.1799 0.2822 0.2998 0.2101 ] × [ 0.4936 0.1619 0.1014 0.2430 ] = [ 2.0318 0.6524 0.4083 0.9849 ]
[ 2.0318 0.6524 0.4083 0.9849 ] / [ 0.4936 0.1619 0.1014 0.2430 ] = [ 4.1161 4.0291 4.0253 4.0530 ]
This paper considers four main criteria. Hence, we get n = 4 . Consequently, λ m a x and CI are calculated as follows.
λ m a x = 4.1161 + 4.0291 + 4.0253 + 4.0530 4 = 4.0559
C I = λ m a x n n 1 = 4.0559 4 4 1 = 0.0186
where n = 4 , we get R I = 0.9 , and the consistency ratio (CR) is calculated as follows:
C R = C I R I = 0.0186 0.9 = 0.0207
From the result, C R = 0.0207 < 0.1 , therefore, the pairwise comparison matrix is consistent, and the results are satisfactory. Consequently, other criteria are calculated using the same methodology. The integrated fuzzy comparison matrix of 15 criteria is presented in Table A1 (Appendix A).
The results of the fuzzy weights of all criteria from the FAHP model are calculated based on the fuzzy geometric mean concept, shown in Table 9. Each fuzzy weight includes three values, which are pessimistic value (the lowest weight), most likely value (the middle weight), and optimistic value (the highest weight). For example, the fuzzy weight of criteria quality (C11), has the pessimistic weight at 0.0357, the most likely weight of 0.0750, and the most optimistic weight of 0.1545. The remaining criteria have the same demonstration. These fuzzy preference weights will be used in the next stage of the FTOPSIS model.
Figure 5 depicts the influence levels of criteria. As can be seen, “lead time”, “customer’s voice”, “cost”, “delivery and service”, and “quality” criteria have the most influence percentages, at 8.4559%, 8.3087%, and 7.9628%, 7.6185%, and 7.58%, respectively. Regarding economic factors in choosing 3PRLPs in the e-commerce sector, the results recommend that “lead time” is more critical in experts’ evaluation than other cost and quality issues. For e-commerce businesses, lead times are a significant measure when figuring out inventory management strategy, which is the primary estimator for when the managers reorder stock. It is also an especially concerning factor when adding new product lines to the online store. The new products will have their lead times that may be separate from regularly scheduled deliveries. “Customer’s voice” (as a social criterion) is also positioned second in the expert ranking. This result shows that information sharing, and customer engagement are key determinants of reverse logistics in the e-commerce industry [5]. In the booming e-commerce market of Vietnam, e-commerce businesses are attempting to survive and thrive by focusing more on economic aspects. However, green and resilient strategies towards sustainable development have come into focus. In order to enhance the competitiveness of Vietnamese businesses, the government encourages enterprises to participate effectively in the global value chain by embracing and integrating sustainable business strategies [65]. Thus, the criteria ranking results indicate that economic criteria were ranked high, but the other criteria from the three pillars of sustainable development (social and environmental factors) were also noteworthy. Among environmental factors, “reproduction and reuse” and “green technology” ranked sixth (6.9908%) and seventh (6.9550%) among 15 criteria. These figures elaborate that social and environmental drivers are of tremendous importance in reverse logistics systems besides economic aspects.

4.3. Results of Fuzzy TOPSIS

In FTOPSIS model, the fuzzy preference weights of criteria are obtained from FAHP model. According to the FTOPSIS process in Section 3.4, fuzzy normalized decision matrix and fuzzy weighted normalized decision matrix are shown in Table A2 and Table A3 (Appendix A). Table 10 and Figure 6 show the top three potential third-party reverse logistics providers, which are 3PRLP-05, 3PRLP-07, and 3PRLP-01, ranked first, second, and third with scores of 0.0590, 0.0506, and 0.0513, respectively.

5. Discussion and Conclusions

As online shopping volumes grow, so do return volumes. Product return rates are rising in the e-commerce industry. The cost of doing e-commerce business is facilitating a simple and seamless return processes as part of the increase in customer expectations. This is where reverse logistics comes into its unique role, which is to handle many inevitable situations regarding e-commerce transactions, including deliveries of incorrect products, customer behaviors, damaged products, delays in order fulfillment, to name just a few [66]. It cannot be failed to mention that reverse logistics systems, in essence, provide businesses with numerous opportunities to integrate the three drivers of sustainable development, such as remanufacturing, repair, recycle, and disposal. There is no question that proficient reverse logistics not only handles the e-commerce returns problem but also enables any business to gain customer retention, reduced costs, and higher achievement of sustainability goals. However, managing reverse logistics in-house means the supply chain of moving goods must be amplified, which leads companies to significant issues. For this reason, more and more e-commerce retailers consider reverse logistics outsourcing as an inevitable part of their business. Thus, the evaluation and selection problem of 3PRLP has never been so prominent in this era.
Methodologically, the authors elaborate that in using the proposed hybrid MCDM approach (combining FAHP and FTOPSIS), this has so far been the first study to fill the gap of the existing literature that lacks 3PRLP evaluation and selection practices for several industries within a developing countries context, and especially to address the increased demand of reverse logistics outsourcing in the e-commerce sector. The use of linguistic expressions for the whole evaluation process will mitigate the risk of fuzzy and uncertain judgment when weighing criteria, as well as improving the robustness of the ranking results and the overall computation efficiency. Proper transformations of linguistic terms also ensures that the approach has a broad range of applications.
E-commerce has been on the rise in Vietnam. By 2025, e-commerce purchases are projected to be used by over 70 percent of the 100 million population. Regarding the e-commerce logistics sector, it has contributed to approximately 20–25% of the GDP of Vietnam, according to Vietnam Logistics Business Association [67]. This industry is also predicted to grow by roughly 12 percent every year. Thus, more and more logistics companies and investors are flooding into this untapped potential market. On the other hand, the dilemma of outsourcing logistics services and the decision to select a provider based on quality management and financial performance criteria are sure to be a headache facing e-commerce retailers. Within this context, our paper aims to provide significant insights for online merchants on the methods of evaluation and selection of 3PRLP. In doing so, the proposed approach is applied to illustrate a case study of the reverse logistics outsourcing problem in Vietnam. From the FAHP stage, results show that “lead time,” “cost,” “delivery and service,” and “quality” (economic factors) and “customer’s voice” (social factor) are the most impactful criteria according to expert evaluation. Environmental drivers such as “reproduction and reuse” and “green technology” were also ranked high, indicating the other aspect from the three pillars of sustainable development were noteworthy in reverse logistics outsourcing of e-commerce in Vietnam. In the next stage, FTOPSIS indicates that 3PRLP-05 was the optimized partner with the final ranking score of 0.059. For practical implications, these findings can help e-commerce businesses or any firms to gain a better understanding of the 3PRLP selection process. Thus, the companies can devise their strategies accordingly to better control their reverse logistics activities.
For future studies involving qualitative and quantitative criteria, new factors concerning today’s situation (i.e., post-Covid-19 world) should be upgraded to obtain well-rounded results. Regarding theoretical limitations, this paper calculated the consistency ratio using lower bound (pessimistic value) and upper bound (optimistic value) values of the fuzzy comparison matrix, hence, future studies should approach the procedure of defuzzification according to the derivate fuzzy AHP [68]. Moreover, future studies should target other MCDM techniques (i.e., VIKOR, PROMETHEE II) for ranking alternatives and compare the results using ranking similarity reference coefficients [69], and/or combine with the exact methods [70]. In terms of applications, further studies might therefore investigate reverse logistics outsourcing practices in other countries than Vietnam to improve the findings’ external validity. Moreover, the procedure and criteria presented in this paper can also be considered in other related industries, such as supplier selection or any decision-making problems.

Author Contributions

Conceptualization, C.-N.W. and T.-T.D.; Data curation, T.-T.D.; Formal analysis, T.-T.D. and N.-A.-T.N.; Funding acquisition, C.-N.W.; Investigation, T.-T.D. and N.-A.-T.N.; Methodology, C.-N.W. and T.-T.D.; Project administration, C.-N.W.; Software, T.-T.D. and N.-A.-T.N.; Validation, C.-N.W. and T.-T.D.; Writing—original draft, T.-T.D. and N.-A.-T.N.; Writing—review and editing, C.-N.W. and N.-A.-T.N. All authors have read and agreed to the published version of the manuscript.


This research was partly supported by the National Kaohsiung University of Science and Technology, and MOST 109-2622-E-992-026 from the Ministry of Sciences and Technology in Taiwan.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.


The authors appreciate the support from the National Kaohsiung University of Science and Technology, Ministry of Sciences and Technology in Taiwan.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Integrated fuzzy comparison matrix of 15 criteria (FAHP).
Table A1. Integrated fuzzy comparison matrix of 15 criteria (FAHP).
Note: calculated by the researchers.
Table A2. Fuzzy normalized decision matrix of all alternatives (FTOPSIS).
Table A2. Fuzzy normalized decision matrix of all alternatives (FTOPSIS).
3PRLPQualityCostLead TimeDelivery and Service
3PRLPR&D CapabilityRecycleDisposalReproduction and Reuse
3PRLPGreen TechnologyCO2 EmissionsHealth and SafetyCustomer’s Voice
3PRLPReputationOperational RiskFinancial Risk
Note: calculated by the researchers.
Table A3. Fuzzy weighted normalized decision matrix (FTOPSIS).
Table A3. Fuzzy weighted normalized decision matrix (FTOPSIS).
3PRLPQualityCostLead TimeDelivery and Service
3PRLPR&D CapabilityRecycleDisposalReproduction and Reuse
3PRLPGreen TechnologyCO2 EmissionsHealth and SafetyCustomer’s Voice
3PRLPReputationOperational RiskFinancial Risk
Note: calculated by the researchers.


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Figure 1. Three pillars of sustainable development [16].
Figure 1. Three pillars of sustainable development [16].
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Figure 2. Research process.
Figure 2. Research process.
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Figure 3. The membership function of triangular fuzzy number (TFN).
Figure 3. The membership function of triangular fuzzy number (TFN).
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Figure 4. The decision hierarchy for outsourcing reverse logistics.
Figure 4. The decision hierarchy for outsourcing reverse logistics.
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Figure 5. Influence level of criteria from FAHP model.
Figure 5. Influence level of criteria from FAHP model.
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Figure 6. Final ranking of the FTOPSIS model.
Figure 6. Final ranking of the FTOPSIS model.
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Table 1. Summary of method approaches in reverse logistics provider’s selection.
Table 1. Summary of method approaches in reverse logistics provider’s selection.
No.Authors [Citation]YearMethodologiesProblems
AHPANPTOPSISVIKORSWARAQFDOther MCDMDEAMathematical ModelStatistical ApproachDeterministicUncertainReverse Logistics
1Korpela and Tuominen [23]1996x x
2Moberg and Speh [46]2004 xx
3Sinkovics and Roath [47]2004 xx
4Thakkar et al. [30]2005 x x x x
5So et al. [24]2006x x
6Bottani and Rizzi [36]2006 x x
7Sahay et al. [44]2006 x x
8Göl and Çatay [25]2007x x
9Yang et al. [48]2008 xx
10Zhou et al. [41]2008 x x
11Hamdan and Rogers [42]2008 x x
12Kannan et al. [34]2009 x x x
13Liu and Wang [45]2009 x x
14Liou et al. [31]2010 x x x x
15Sasikumar and Haq [37]2011 x x x x
16Ho et al. [28]2012x x x
17Falsini et al. [26]2012x xx x
18Hsu et al. [32]2013 x x x
19Perçin and Min [40]2013 x x x
20Jayant et al. [27]2014x x x x
21Tadić et al. [33]2014 x x x x
22Ilgin [29]2017x xx
23Zarbakhshnia et al. [38]2018 x x x
24Bai and Sarkis [19]2019 xx x x
25Govindan et al. [43]2019 x x x x
26Wang et al. [35]2019 x x x x
27Agarwal et al. [39]2020 x x xx
28This paper2021x x xx
Note: analytical hierarchy process (AHP), analytical network process (ANP), technique for order of preference by similarity to ideal solution (TOPSIS), visekriterijumska optimizacija i kompromisno resenje (VIKOR), step-wise weight assessment ratio analysis (SWARA), quality function deployment (QFD), multi-criteria decision making (MCDM), data envelopment analysis (DEA).
Table 2. List of criteria used in reverse logistics provider’s selection.
Table 2. List of criteria used in reverse logistics provider’s selection.
Main CriteriaCriteriaTargetAuthors [Citation]
Economic (C1)C11. QualityMaxBottani and Rizzi [36], Spencer et al. [51], Tsai et al. [52], Zarbakhshnia et al. [38], Mavi et al. [53], Govindan et al. [43], Sasikumar and Haq [37]
C12. CostMinBottani and Rizzi [36], Spencer et al. [51], Tsai et al. [52], Zarbakhshnia et al. [38], Mavi et al. [53], Govindan et al. [43], Sasikumar and Haq [37], Efendigil et al. [54]
C13. Lead timeMinZarbakhshnia et al. [38], Mavi et al. [53], Govindan et al. [43], Efendigil et al. [54]
C14. Delivery and serviceMaxBottani and Rizzi [36], Spencer et al. [51], Tsai et al. [52], Zarbakhshnia et al. [38], Mavi et al. [53], Govindan et al. [43], Sasikumar and Haq [37], Efendigil et al. [54]
C15. R&D capabilityMaxBottani and Rizzi [36], Goebel et al. [55], Ni et al. [56]
Environment (C2)C21. RecycleMaxZarbakhshnia et al. [38], Mavi et al. [53], Sasikumar and Haq [37], Guimarães and Salomon [57]
C22. DisposalMaxZarbakhshnia et al. [38], Mavi et al. [53], Sasikumar and Haq [37]
C23. Reproduction and reuseMaxGoebel et al. [55], Zarbakhshnia et al. [38], Mavi et al. [53], Sasikumar and Haq [37]
C24. Green technologyMaxZarbakhshnia et al. [38], Sasikumar and Haq [37], Guimarães and Salomon [57]
C25. CO2 emissionsMinZarbakhshnia et al. [38], Govindan et al. [43]
Social (C3)C31. Health and safetyMaxZarbakhshnia et al. [38], Govindan et al. [43], Mavi et al. [53]
C32. Customer’s voiceMaxZarbakhshnia et al. [38], Mavi et al. [53], Efendigil et al. [54]
C33. ReputationMaxSpencer et al. [51], Mavi et al. [53], Efendigil et al. [54]
Risk (C4)C41. Operational riskMinZarbakhshnia et al. [38], Mitra et al. [58], Mavi et al. [53]
C42. Financial riskMinBottani and Rizzi [36], Tsai et al. [52], Ni et al. [56], Mavi et al. [53]
Note: identified by the researchers.
Table 3. Linguistic rating level and allocated TFN in the fuzzy AHP (FAHP) model.
Table 3. Linguistic rating level and allocated TFN in the fuzzy AHP (FAHP) model.
Fuzzy NumberLinguistics Rating LevelAllocated TFN
1Equal importance (1, 1, 1)
2Weak importance (1, 2, 3)
3Not bad (2, 3, 4)
4Preferable(3, 4, 5)
5Importance (4, 5, 6)
6Fairly importance (5, 6, 7)
7Very important (6, 7, 8)
8Absolute (7, 8, 9)
9Perfect (8, 9, 10)
Table 4. Linguistics rating level of alternatives in the fuzzy TOPSIS (FTOPSIS) model.
Table 4. Linguistics rating level of alternatives in the fuzzy TOPSIS (FTOPSIS) model.
Linguistics Rating LevelAllocated TFN
Very poor (VP) (0, 0, 1)
Poor (P) (0, 1, 3)
Medium poor (MP) (1, 3, 5)
Fair (F) (3, 5, 7)
Medium good (MG) (5, 7, 9)
Good (G)(7, 9, 10)
Very good (VG)(9, 10, 10)
Table 5. Initial comparison matrix of the main criteria.
Table 5. Initial comparison matrix of the main criteria.
CriteriaLinguistics Rating LevelCriteria
(8, 9, 10)(7, 8, 9)(6, 7, 8)(5, 6, 7)(4, 5, 6)(3, 4, 5)(2, 3, 4)(1, 2, 3)(1, 1, 1)(1, 2, 3)(2, 3, 4)(3, 4, 5)(4, 5, 6)(5, 6, 7)(6, 7, 8)(7, 8, 9)(8, 9, 10)
C1 x C2
C1 x C3
C1 x C4
C2 x C3
C2 x C4
C3 x C4
Note: economic (C1), environment (C2), social (C3), risk (C4).
Table 6. Integrated fuzzy comparison matrix of the main criteria.
Table 6. Integrated fuzzy comparison matrix of the main criteria.
CriteriaEconomic (C1)Environment (C2)Social (C3)Risk (C4)
Economic (C1)(1, 1, 1)(2, 3, 4)(3, 4, 5)(2, 3, 4)
Environment (C2)(1/4, 1/3, 1/2)(1, 1, 1)(1, 2, 3)(1/3, 1/2, 1)
Social (C3)(1/5, 1/4, 1/3)(1/3, 1/2, 1)(1, 1, 1)(1/4, 1/3, 1/2)
Risk (C4)(1/4, 1/3, 1/2)(1, 2, 3)(2, 3, 4)(1, 1, 1)
Note: identified by the researchers.
Table 7. Non-fuzzy comparison matrix of the main criteria.
Table 7. Non-fuzzy comparison matrix of the main criteria.
CriteriaEconomic (C1)Environment (C2)Social (C3)Risk (C4)
Economic (C1)12.82843.87302.8284
Environment (C2)0.353611.73210.5774
Social (C3)0.25820.577410.3536
Risk (C4)0.35361.73212.82841
Note: calculated by the researchers.
Table 8. Normalized comparison matrix of the main criteria.
Table 8. Normalized comparison matrix of the main criteria.
CriteriaEconomic (C1)Environment (C2)Social (C3)Risk (C4)Priority Vector
Economic (C1)0.50880.46080.41060.59430.4936
Environment (C2)0.17990.16290.18360.12130.1619
Social (C3)0.13140.09410.10600.07430.1014
Risk (C4)0.17990.28220.29980.21010.2430
Note: calculated by the researchers.
Table 9. Results of fuzzy weights from the FAHP model.
Table 9. Results of fuzzy weights from the FAHP model.
Main CriteriaCriteriaTargetFuzzy Geometric MeanFuzzy Weights
C11. QualityMax0.76541.14091.65970.03570.07500.1545
C12. CostMin0.84791.22471.70310.03950.08050.1585
C13. Lead timeMin0.88341.29011.82450.04120.08480.1698
C14. Delivery and serviceMax0.77851.15021.66110.03630.07560.1546
C15. R&D capabilityMax0.69990.97441.35490.03260.06400.1261
C21. RecycleMax0.63290.89851.29830.02950.05910.1208
C22. DisposalMax0.57950.83181.22410.02700.05470.1139
C23. Reproduction and reuseMax0.78011.08971.46720.03640.07160.1366
C24. Green technologyMax0.78691.08651.45250.03670.07140.1352
C25. CO2 emissionsMin0.61960.85591.22180.02890.05630.1137
C31. Health and safetyMax0.56800.76611.08380.02650.05040.1009
C32. Customer’s voiceMax0.90291.27841.76770.04210.08400.1645
C33. ReputationMax0.62390.86311.22840.02910.05670.1143
C41. Operational riskMin0.59170.81531.17280.02760.05360.1092
C42. Financial riskMin0.68310.94831.33870.03180.06230.1246
Note: calculated by the researchers.
Table 10. Closeness coefficient of each alternative.
Table 10. Closeness coefficient of each alternative.
3PRLP D i * D i Gap DegreeSatisfaction DegreeRank
Note: calculated by the researchers.
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Wang, C.-N.; Dang, T.-T.; Nguyen, N.-A.-T. Outsourcing Reverse Logistics for E-Commerce Retailers: A Two-Stage Fuzzy Optimization Approach. Axioms 2021, 10, 34.

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Wang C-N, Dang T-T, Nguyen N-A-T. Outsourcing Reverse Logistics for E-Commerce Retailers: A Two-Stage Fuzzy Optimization Approach. Axioms. 2021; 10(1):34.

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Wang, Chia-Nan, Thanh-Tuan Dang, and Ngoc-Ai-Thy Nguyen. 2021. "Outsourcing Reverse Logistics for E-Commerce Retailers: A Two-Stage Fuzzy Optimization Approach" Axioms 10, no. 1: 34.

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