Leisure Agricultural Park Selection for Traveler Groups Amid the COVID19 Pandemic
Abstract
:1. Introduction
 (1)
 After the outbreak of the COVID19 pandemic, the factors affecting travelers visiting a leisure agricultural park have been different to the previous factors. In addition to subjective personal preferences, there is also objective information related to the COVID19 pandemic. This study is one of the first studies to explore the influence of these factors on travelers’ decisions in choosing suitable leisure agricultural parks.
 (2)
 The acFGM method is proposed to enhance the precision of deriving the priorities of factors critical to the selection of a suitable leisure agricultural park.
2. Methodology
2.1. acFGM for Deriving the Fuzzy Priorities of Criteria
 Step 1. Approximate the value of the fuzzy priority of criterion i using FGM as in the following [18]:$${\tilde{w}}_{i}(k)\cong ({w}_{i1}(k),\text{}{w}_{i2}(k),\text{}{w}_{i3}(k))$$$${w}_{i1}(k)=\frac{1}{1+{\displaystyle \sum _{m\ne i}\frac{\sqrt[n]{{\displaystyle \prod _{j=1}^{n}{a}_{mj3}(k)}}}{\sqrt[n]{{\displaystyle \prod _{j=1}^{n}{a}_{ij1}(k)}}}}}$$$${w}_{i2}(k)\cong \frac{1}{1+{\displaystyle \sum _{m\ne i}\frac{\sqrt[n]{{\displaystyle \prod _{j=1}^{n}{a}_{mj2}(k)}}}{\sqrt[n]{{\displaystyle \prod _{j=1}^{n}{a}_{ij2}(k)}}}}}$$$${w}_{i3}(k)\cong \frac{1}{1+{\displaystyle \sum _{m\ne i}\frac{\sqrt[n]{{\displaystyle \prod _{j=1}^{n}{a}_{mj1}(k)}}}{\sqrt[n]{{\displaystyle \prod _{j=1}^{n}{a}_{ij3}(k)}}}}}$$
 Step 2. Derive the priority of criterion i from the crisp judgment matrix ${A}^{c}(k)=[{a}_{ij2}(k)]$ using an eigen analysis, as in the following [17]:$$\mathrm{det}({A}^{c}(k){\lambda}^{c}(k)I)=0$$$$({A}^{c}(k){\lambda}^{c}(k)I){x}^{c}(k)=0$$$${w}_{i}^{c}(k)=\frac{{x}_{i}^{c}}{{\displaystyle \sum _{j=1}^{n}{x}_{j}^{c}}}$$
 Step 3. Calibrate the fuzzy priority of criterion i in the following way:$${w}_{i1}(k)\to \text{}{w}_{i1}(k)+{w}_{i}^{c}(k){w}_{i2}(k)$$$${w}_{i2}(k)\to \text{}{w}_{i}^{c}(k)$$$${w}_{i3}(k)\to \text{}{w}_{i3}(k)+{w}_{i}^{c}(k){w}_{i2}(k)$$
 (1)
 After calibration, ${w}_{i1}(k)$ may be negative, which is infeasible.
 (2)
 The range of a fuzzy priority approximated using FGM is usually wider than that of the actual value, which is not considered in the calibration process.
2.2. FWI for Aggregating the Fuzzy Priorities of Criteria Derived by All Decision Makers
 (1)
 $\tilde{FWI}(\{{\tilde{w}}_{i}(k)\})={\tilde{w}}_{i}(l)$ if ${\omega}_{l}=1$ and ${\omega}_{k}=0$ ∀ k ≠ l
 (2)
 $\tilde{FWI}(\{{\tilde{w}}_{i}(k)\})=\tilde{FI}(\{{\tilde{w}}_{i}(k)\})$ if ${\omega}_{k}=\frac{1}{K}$ ∀ k; $\tilde{FI}$ is the fuzzy intersection operator (i.e., the tnorm).
 (3)
 $\underset{l}{\mathrm{min}}{\mu}_{{\tilde{w}}_{i}(l)}(x)\le {\mu}_{\tilde{FWI}(\{{\tilde{w}}_{i}(k)\})}(x)\le \underset{l}{\mathrm{max}}{\mu}_{{\tilde{w}}_{i}(l)}(x)$
 (4)
 $\frac{\partial {\mu}_{\tilde{FWI}(\{{\tilde{w}}_{i}(k)\})}(x)}{\partial {\mu}_{{\tilde{w}}_{i}(l)}(x)}\propto {\omega}_{l}$
2.3. Fuzzy VIKOR for Evaluating Alternatives
 Step 1. Determine the best and worst values of each criterion, as in the following:
 Step 2. Compute normalized fuzzy distances, as in the following:
 Step 3. Compute the values of ${\tilde{S}}_{h}$ and ${\tilde{R}}_{h}$ [28], as in the following:$${\tilde{S}}_{h}={\displaystyle \sum _{i=1}^{n}({\tilde{w}}_{i}}(all)(\times ){\tilde{d}}_{hi})$$$${\tilde{R}}_{h}=\underset{i}{\mathrm{max}}({\tilde{w}}_{i}(all)(\times ){\tilde{d}}_{hi})$$
 Step 4. Compute the value of ${\tilde{Q}}_{h}$ [28], as in the following:$${\tilde{Q}}_{h}=\xi \cdot \frac{{\tilde{S}}_{h}()\underset{r}{\mathrm{min}}{\tilde{S}}_{r}}{\mathrm{max}(\underset{r}{\mathrm{max}}{\tilde{S}}_{r})\mathrm{min}(\underset{r}{\mathrm{min}}{\tilde{S}}_{r})}(+)(1\xi )\cdot \frac{{\tilde{R}}_{h}()\underset{r}{\mathrm{min}}{\tilde{R}}_{r}}{\mathrm{max}(\underset{r}{\mathrm{max}}{\tilde{R}}_{r})\mathrm{min}(\underset{r}{\mathrm{min}}{\tilde{R}}_{r})}$$
 Step 5. Defuzzify ${\tilde{S}}_{h}$, ${\tilde{R}}_{h}$, ${\tilde{Q}}_{h}$ using the COG method, as in the following:$$COG({\tilde{S}}_{h})=\frac{{\displaystyle \underset{all\text{}x}{\int}x{\mu}_{{\tilde{S}}_{h}}(x)dx}}{{\displaystyle \underset{all\text{}x}{\int}{\mu}_{{\tilde{S}}_{h}}(x)dx}}$$$$COG({\tilde{R}}_{h})=\frac{{\displaystyle \underset{all\text{}x}{\int}x{\mu}_{{\tilde{R}}_{h}}(x)dx}}{{\displaystyle \underset{all\text{}x}{\int}{\mu}_{{\tilde{R}}_{h}}(x)dx}}$$$$COG({\tilde{Q}}_{h})=\frac{{\displaystyle \underset{all\text{}x}{\int}x{\mu}_{{\tilde{Q}}_{h}}(x)dx}}{{\displaystyle \underset{all\text{}x}{\int}{\mu}_{{\tilde{Q}}_{h}}(x)dx}}$$
 Step 6. Rank alternatives according to their $D({\tilde{S}}_{h})$, $D({\tilde{R}}_{h})$, and $D({\tilde{Q}}_{h})$ values from the smallest to the largest. The decision maker will have three ranking results, giving him/her a high degree of flexibility, which is an advantage of fuzzy VIKOR over fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) [29,30]; for example, when $D({\tilde{Q}}_{h})$ is considered, the top two alternatives are indicated with alternatives ${h}_{(1)}$ and ${h}_{(2)}$, respectively. Then, in the view of Opricovic [28], alternative ${h}_{(1)}$ can be recommended to the decision maker if the following two conditions are met:$$D({\tilde{Q}}_{{h}_{(2)}})D({\tilde{Q}}_{{h}_{(1)}})\ge \frac{1}{H1}$$$$D({\tilde{S}}_{{h}_{(1)}})=\underset{r}{\mathrm{min}}D({\tilde{S}}_{r})\mathrm{or}D({\tilde{R}}_{{h}_{(1)}})=\underset{r}{\mathrm{min}}D({\tilde{R}}_{r})$$
3. Case Study
3.1. Background
3.2. Application of the Proposed Methodology
3.3. Discussion
 (1)
 The most suitable leisure agricultural park for the family was leisure agricultural park #1; it had the best image and was the easiest to maintain social distance.
 (2)
 However, the superiority of leisure agricultural park #1 over leisure agricultural park #4 only met the second condition. Therefore, both leisure agricultural parks could be recommended to the family for their consideration.
 (3)
 In contrast, leisure agricultural park #3 ranked last because the family showed the lowest preference for this leisure agricultural park.
 (4)
 A parametric analysis has been conducted to examine the effect of ξ on the ranking result. The results are summarized in Table 9. The superiority of leisure agricultural park #1 over the others was not affected by the value of ξ. In addition, when ξ was set to zero, there was a tie between leisure agricultural parks #2 and #3.
 (1)
 The recommendation results to ten traveler groups and their choices are summarized in Table 10. As a result, the successful recommendation rate was 90%, high enough to support the effectiveness of the proposed methodology.
 (2)
 Among the ten traveler groups, seven rated the easiness to maintain social distance as the most important criterion. In contrast, the distance to a leisure agricultural park was considered the least important criterion by most traveler groups.
 (1)
 Three existing fuzzy group decisionmaking methods were also applied to this case for comparison. The first was the FGM–FGM–fuzzy weighted average (FWA) method, in which the decision makers’ fuzzy judgement matrixes were aggregated using FGM. Then, the fuzzy priorities of criteria were derived using FGM. Finally, the overall performance of each leisure agricultural park was evaluated using FWA. The second method was the FGM–FEA–FWA method, wherein FEA [31] was applied to derive the priorities of criteria in place of the FGM method. The third method was the FGM–FGM–FTOPSIS method, which was similar to the FGM–FGM–FWA method, except that fuzzy TOPSIS was employed to compare the overall performances of leisure agricultural parks. The results obtained using these methods are summarized in Table 11. It can be observed that the ranking results of leisure agricultural parks using existing methods were different from those using the proposed methodology, which is due to the imprecision of these existing methods in deriving the fuzzy priorities of criteria; for example, the fuzzy priorities of criterion ${\tilde{w}}_{5}$ derived by decision maker #1 using various methods are compared in Table 12, showing a significant difference between these results.
4. Conclusions
 (1)
 During the COVID19 pandemic, the willingness of travelers (especially traveler groups) to go to a leisure agricultural park was quite high.
 (2)
 In choosing a suitable leisure agricultural park, the most important criterion was the easiness to maintain social distance, while the least important criterion was the distance to a leisure agricultural park.
 (3)
 Nine of ten traveler groups followed the recommendations, resulting in a successful recommendation rate of 90%.
 (1)
 The easiness to maintain social distance is directly proportional to the area of a leisure agricultural park. Although such an evaluation method is simple, it may not be practical because in a leisure agricultural park, travelers will only go to part of the area.
 (2)
 Although it is not difficult to write a program to implement the proposed methodology, the proposed methodology is slightly more complicated than some multicriteria decisionmaking methods for similar purposes.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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i  ${\tilde{\mathit{w}}}_{\mathit{i}}(1)$  ${\tilde{\mathit{w}}}_{\mathit{i}}(2)$  ${\tilde{\mathit{w}}}_{\mathit{i}}(3)$ 

1  (0.155, 0.347, 0.491)  (0.195, 0.442, 0.655)  (0.12, 0.278, 0.532) 
2  (0.034, 0.059, 0.147)  (0.044, 0.092, 0.238)  (0.036, 0.087, 0.243) 
3  (0.229, 0.369, 0.596)  (0.112, 0.264, 0.511)  (0.175, 0.423, 0.643) 
4  (0.027, 0.06, 0.127)  (0.024, 0.051, 0.151)  (0.024, 0.052, 0.156) 
5  (0.087, 0.165, 0.316)  (0.057, 0.15, 0.375)  (0.065, 0.159, 0.391) 
h  Area (m^{2})  Major Agricultural Products  City  Number of Confirmed COVID19 Cases *  Distance (min)  Image 

1  2,120,000  Shiitake mushrooms, flowers  Taichung  202  57 

2  23,000  Strawberry  Miaoli  549  64 

3  145,000  Orange, pitaya  Yunlin  22  54 

4  500,000  Milk, dairy products, malt  Miaoli  549  57 

Criterion  Rule 

Image of the leisure agricultural park  ${\tilde{p}}_{h1}({x}_{h1})=\{\begin{array}{ccc}(0,\text{}0,\text{}1)& \mathrm{if}& {x}_{h1}=\u201c\mathrm{not}\mathrm{very}\mathrm{interesting}(\mathrm{just}\mathrm{for}\mathrm{killing}\mathrm{time})\u201d\\ (0,\text{}1,\text{}2)& \mathrm{if}& {x}_{h1}=\u201c\mathrm{somewhat}\mathrm{interesting}\u201d\\ (1.5,\text{}2.5,\text{}3.5)& \mathrm{if}& {x}_{h1}=\u201c\mathrm{interesting}\mathrm{and}\mathrm{somewhat}\mathrm{healthy}\u201d\\ (3,\text{}4,\text{}5)& \mathrm{if}& {x}_{h1}=\u201c\mathrm{interesting}\mathrm{and}\mathrm{healthy}\u201d\\ (4,\text{}5,\text{}5)& \mathrm{if}& {x}_{h1}=\u201c\mathrm{very}\mathrm{interesting}\mathrm{and}\mathrm{healthy}\left(\mathrm{enjoyable}\right)\u201d\end{array}$ $\mathrm{where}{x}_{h1}$ is the image of the leisure agricultural park. 
Number of confirmed COVID19 cases in the city  ${\tilde{p}}_{h2}({x}_{h2})=\{\begin{array}{ccc}(0,\text{}0,\text{}1)& \mathrm{if}& 0.1\cdot \underset{r}{\mathrm{min}}{x}_{r2}+0.9\cdot \underset{r}{\mathrm{max}}{x}_{r2}{x}_{h2}\\ (0,\text{}1,\text{}2)& \mathrm{if}& 0.35\cdot \underset{r}{\mathrm{min}}{x}_{r2}+0.65\cdot \underset{r}{\mathrm{max}}{x}_{r2}\le {x}_{h2}0.1\cdot \underset{r}{\mathrm{min}}{x}_{r2}+0.9\cdot \underset{r}{\mathrm{max}}{x}_{r2}\\ (1.5,\text{}2.5,\text{}3.5)& \mathrm{if}& 0.65\cdot \underset{r}{\mathrm{min}}{x}_{r2}+0.35\cdot \underset{r}{\mathrm{max}}{x}_{r2}\le {x}_{h2}0.35\cdot \underset{r}{\mathrm{min}}{x}_{r2}+0.65\cdot \underset{r}{\mathrm{max}}{x}_{r2}\\ (3,\text{}4,\text{}5)& \mathrm{if}& 0.9\cdot \underset{r}{\mathrm{min}}{x}_{r2}+0.1\cdot \underset{r}{\mathrm{max}}{x}_{r2}\le {x}_{h2}0.65\cdot \underset{r}{\mathrm{min}}{x}_{r2}+0.35\cdot \underset{r}{\mathrm{max}}{x}_{r2}\\ (4,\text{}5,\text{}5)& \mathrm{if}& {x}_{h2}\le 0.9\cdot \underset{r}{\mathrm{min}}{x}_{r2}+0.1\cdot \underset{r}{\mathrm{max}}{x}_{r2}\end{array}$ $\mathrm{where}{x}_{h2}$ is the number of confirmed COVID19 cases in the city. 
Easiness to maintain social distance  ${\tilde{p}}_{h3}({x}_{h3})=\{\begin{array}{ccc}(0,\text{}0,\text{}1)& \mathrm{if}& {x}_{h3}\le 0.9\cdot \underset{r}{\mathrm{min}}{x}_{r3}+0.1\cdot \underset{r}{\mathrm{max}}{x}_{r3}\\ (0,\text{}1,\text{}2)& \mathrm{if}& 0.9\cdot \underset{r}{\mathrm{min}}{x}_{r3}+0.1\cdot \underset{r}{\mathrm{max}}{x}_{r3}\le {x}_{h3}0.65\cdot \underset{r}{\mathrm{min}}{x}_{r3}+0.35\cdot \underset{r}{\mathrm{max}}{x}_{r3}\\ (1.5,\text{}2.5,\text{}3.5)& \mathrm{if}& 0.65\cdot \underset{r}{\mathrm{min}}{x}_{r3}+0.35\cdot \underset{r}{\mathrm{max}}{x}_{r3}\le {x}_{h3}0.35\cdot \underset{r}{\mathrm{min}}{x}_{r3}+0.65\cdot \underset{r}{\mathrm{max}}{x}_{r3}\\ (3,\text{}4,\text{}5)& \mathrm{if}& 0.35\cdot \underset{r}{\mathrm{min}}{x}_{r3}+0.65\cdot \underset{r}{\mathrm{max}}{x}_{r3}\le {x}_{h3}0.1\cdot \underset{r}{\mathrm{min}}{x}_{r3}+0.9\cdot \underset{r}{\mathrm{max}}{x}_{r3}\\ (4,\text{}5,\text{}5)& \mathrm{if}& 0.1\cdot \underset{r}{\mathrm{min}}{x}_{r3}+0.9\cdot \underset{r}{\mathrm{max}}{x}_{r3}{x}_{h3}\end{array}$ $\mathrm{where}{x}_{h3}$ is the area of the leisure agricultural park. 
Distance to the leisure agricultural park  ${\tilde{p}}_{h4}({x}_{h4})=\{\begin{array}{ccc}(0,\text{}0,\text{}1)& \mathrm{if}& 0.1\cdot \underset{r}{\mathrm{min}}{x}_{r4}+0.9\cdot \underset{r}{\mathrm{max}}{x}_{r4}{x}_{h4}\\ (0,\text{}1,\text{}2)& \mathrm{if}& 0.35\cdot \underset{r}{\mathrm{min}}{x}_{r4}+0.65\cdot \underset{r}{\mathrm{max}}{x}_{r4}\le {x}_{h4}0.1\cdot \underset{r}{\mathrm{min}}{x}_{r4}+0.9\cdot \underset{r}{\mathrm{max}}{x}_{r4}\\ (1.5,\text{}2.5,\text{}3.5)& \mathrm{if}& 0.65\cdot \underset{r}{\mathrm{min}}{x}_{r4}+0.35\cdot \underset{r}{\mathrm{max}}{x}_{r4}\le {x}_{h4}0.35\cdot \underset{r}{\mathrm{min}}{x}_{r4}+0.65\cdot \underset{r}{\mathrm{max}}{x}_{r4}\\ (3,\text{}4,\text{}5)& \mathrm{if}& 0.9\cdot \underset{r}{\mathrm{min}}{x}_{r4}+0.1\cdot \underset{r}{\mathrm{max}}{x}_{r4}\le {x}_{h4}0.65\cdot \underset{r}{\mathrm{min}}{x}_{r4}+0.35\cdot \underset{r}{\mathrm{max}}{x}_{r4}\\ (4,\text{}5,\text{}5)& \mathrm{if}& {x}_{h4}\le 0.9\cdot \underset{r}{\mathrm{min}}{x}_{r4}+0.1\cdot \underset{r}{\mathrm{max}}{x}_{r4}\end{array}$ $\mathrm{where}{x}_{h4}$ is the distance to the leisure agricultural park. 
Preference for the leisure agricultural park  ${\tilde{p}}_{h5}({x}_{h5})=\{\begin{array}{ccc}(0,\text{}0,\text{}1)& \mathrm{if}& {x}_{h1}=\u201c\mathrm{very}\mathrm{lowly}\mathrm{preferred}\u201d\\ (0,\text{}1,\text{}2)& \mathrm{if}& {x}_{h1}=\u201c\mathrm{lowly}\mathrm{preferred}\u201d\\ (1.5,\text{}2.5,\text{}3.5)& \mathrm{if}& {x}_{h1}=\u201c\mathrm{moderately}\mathrm{preferred}\u201d\\ (3,\text{}4,\text{}5)& \mathrm{if}& {x}_{h1}=\u201c\mathrm{highly}\mathrm{preferred}\u201d\\ (4,\text{}5,\text{}5)& \mathrm{if}& {x}_{h1}=\u201c\mathrm{very}\mathrm{highly}\mathrm{preferred}\u201d\end{array}$ $\mathrm{where}{x}_{h5}$ is the preference for the leisure agricultural park. 
h  ${\tilde{\mathit{p}}}_{\mathit{h}1}$  ${\tilde{\mathit{p}}}_{\mathit{h}2}$  ${\tilde{\mathit{p}}}_{\mathit{h}3}$  ${\tilde{\mathit{p}}}_{\mathit{h}4}$  ${\tilde{\mathit{p}}}_{\mathit{h}5}$ 

1  (4, 5, 5)  (3, 4, 5)  (4, 5, 5)  (0, 1, 2)  (1.5, 2.5, 3.5) 
2  (3, 4, 5)  (0, 0, 1)  (0, 0, 1)  (4, 5, 5)  (3, 4, 5) 
3  (1.5, 2.5, 3.5)  (4, 5, 5)  (0, 0, 1)  (0, 0, 1)  (0, 1, 2) 
4  (3, 4, 5)  (0, 0, 1)  (0, 1, 2)  (0, 1, 2)  (4, 5, 5) 
i  1  2  3  4  5 

${\tilde{p}}_{i}^{\ast}$  (4, 5, 5)  (4, 5, 5)  (4, 5, 5)  (4, 5, 5)  (4, 5, 5) 
${\tilde{p}}_{i}^{}$  (1.5, 2.5, 3.5)  (0, 0, 1)  (0, 1, 2)  (0, 0, 1)  (0, 1, 2) 
h  ${\tilde{\mathit{d}}}_{\mathit{h}1}$  ${\tilde{\mathit{d}}}_{\mathit{h}2}$  ${\tilde{\mathit{d}}}_{\mathit{h}3}$  ${\tilde{\mathit{d}}}_{\mathit{h}4}$  ${\tilde{\mathit{d}}}_{\mathit{h}5}$ 

1  (0, 0, 0.29)  (0, 0.2, 0.4)  (0, 0, 0.2)  (0.4, 0.8, 1)  (0.1, 0.5, 0.7) 
2  (0, 0.29, 0.57)  (0.6, 1, 1)  (0.6, 1, 1)  (0, 0, 0.2)  (0, 0.2, 0.4) 
3  (0.14, 0.71, 1)  (0, 0, 0.2)  (0.6, 1, 1)  (0.6, 1, 1)  (0.4, 0.8, 1) 
4  (0, 0.29, 0.57)  (0.6, 1, 1)  (0.4, 0.8, 1)  (0.4, 0.8, 1)  (0, 0, 0.2) 
h  ${\tilde{\mathit{S}}}_{\mathit{h}}$  ${\tilde{\mathit{R}}}_{\mathit{h}}$  ${\tilde{\mathit{Q}}}_{\mathit{h}}$ 

1  (0.01, 0.17, 0.82)  (0.01, 0.1, 0.28)  (0, 0, 0.44) 
2  (0.12, 0.61, 1.4)  (0.1, 0.38, 0.65)  (0, 0.35, 0.89) 
3  (0.16, 0.85, 1.8)  (0.1, 0.38, 0.65)  (0, 0.41, 1) 
4  (0.09, 0.55, 1.45)  (0.07, 0.31, 0.65)  (0, 0.27, 0.9) 
H  $\mathit{D}({\tilde{\mathit{S}}}_{\mathit{h}})$  $\mathit{D}({\tilde{\mathit{R}}}_{\mathit{h}})$  $\mathit{D}({\tilde{\mathit{Q}}}_{\mathit{h}})$  Rank 

1  0.296  0.120  0.110  1 
2  0.686  0.380  0.395  3 
3  0.914  0.380  0.457  4 
4  0.663  0.333  0.361  2 
ξ  Ranking Result 

0  1→4→2, 3 
0.1  1→4→2→3 
0.2  1→4→2→3 
0.3  1→4→2→3 
0.4  1→4→2→3 
0.5  1→4→2→3 
0.6  1→4→2→3 
0.7  1→4→2→3 
0.8  1→4→2→3 
0.9  1→4→2→3 
1.0  1→4→2→3 
Group #  Recommendation  Choice 

1  Leisure agricultural park #1  Leisure agricultural park #1 
2  Leisure agricultural park #5  Leisure agricultural park #5 
3  Leisure agricultural park #4  Leisure agricultural park #4 
4  Leisure agricultural park #6  Leisure agricultural park #6 
5  Leisure agricultural park #1  Leisure agricultural park #1 
6  Leisure agricultural park #11  Leisure agricultural park #11 
7  Leisure agricultural park #2  Leisure agricultural park #3 
8  Leisure agricultural park #9  Leisure agricultural park #9 
9  Leisure agricultural park #1  Leisure agricultural park #1 
10  Leisure agricultural park #11  Leisure agricultural park #11 
h  Rank (FGMFGMFWA)  Rank (FGMFEAFWA)  Rank (FGMFGMFTOPSIS)  Rank (Proposed Methodology) 

1  1  1  1  1 
2  2  2  2  3 
3  4  4  4  4 
4  3  3  3  2 
Method  ${\tilde{\mathit{w}}}_{5}$ 

FGM  (0.079, 0.157, 0.309) 
FEA  0.257 
acFGM  (0.087, 0.165, 0.316) 
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Wu, H.C.; Lin, Y.C.; Chen, T.C.T. Leisure Agricultural Park Selection for Traveler Groups Amid the COVID19 Pandemic. Agriculture 2022, 12, 111. https://doi.org/10.3390/agriculture12010111
Wu HC, Lin YC, Chen TCT. Leisure Agricultural Park Selection for Traveler Groups Amid the COVID19 Pandemic. Agriculture. 2022; 12(1):111. https://doi.org/10.3390/agriculture12010111
Chicago/Turabian StyleWu, HsinChieh, YuCheng Lin, and TinChih Toly Chen. 2022. "Leisure Agricultural Park Selection for Traveler Groups Amid the COVID19 Pandemic" Agriculture 12, no. 1: 111. https://doi.org/10.3390/agriculture12010111