# Sustainable and Resilient Garment Supply Chain Network Design with Fuzzy Multi-Objectives under Uncertainty

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## Abstract

**:**

## 1. Introduction and Literature Review

## 2. Mathematical Model

#### 2.1. Model Notations

Indices | |

i | Index of potential location for manufacturing facilities i = 1, 2, …, I |

j | Index of existing customer zones j = 1, 2, …, J |

k | Index of potential locations for collection centers k = 1, 2, …, K |

l | Index of existing markets to sale reusable textile l = 1, 2, …, L |

m | Index of existing fiber reclamation mills m = 1, 2, …, M |

n | Index of existing flocking industry n = 1, 2, …, N |

s | Index of existing suppliers s = 1, 2, …, S |

t | Index of time period t = 1, 2, …, T |

Parameters | |

${\tilde{d}}_{jt}$ | Demand for new product at customer zone j in period t (units/period) |

${\tilde{p}}_{t}$ | Price of new product in period t ($/unit/period) |

${\tilde{\psi}}_{jt}$ | Percentage of products recovered from customer zone j in period t |

${\tilde{a}}_{i}$ | Cost of installing manufacturing facility i ($) |

${\tilde{b}}_{k}$ | Cost of installing collection center k ($) |

${\tilde{c}}_{sit}$ | Transportation cost per unit from supplier s to manufacturing facility i in period t ($/unit/period) |

${\tilde{e}}_{ijt}$ | Transportation cost per unit from manufacturing facility i to customer zone j in period t ($/unit/period) |

${\tilde{f}}_{jkt}$ | Transportation cost per unit from customer zone j to collection center k in period t ($/unit/period) |

${\tilde{g}}_{klt}$ | Transportation cost per unit from collection center k to resale market l in period t ($/unit/period) |

${\tilde{h}}_{kmt}$ | Transportation cost per unit from collection center k to fiber reclamation mill m in period t ($/unit/period) |

${\tilde{o}}_{knt}$ | Transportation cost per unit from collection center k to flocking industry n in period t ($/unit/period) |

$c{e}_{st}^{emf}$ | Embodied carbon footprints of material coming from supplier s in period t (KgCO_{2}/unit/period) |

$c{e}_{it}^{man}$ | Carbon emission during production of unit product at manufacturing facility i in period t (KgCO_{2}/unit/period) |

$c{e}_{sit}^{tsm}$ | Carbon emission for shipping unit product from supplier s to manufacturing facility i in period t (KgCO_{2}/unit/period) |

$c{e}_{ijt}^{tmc}$ | Carbon emission for shipping unit product from manufacturing facility i to customer zone j in period t (KgCO_{2}/unit/period) |

$c{e}_{jkt}^{tcc}$ | Carbon emission for shipping unit product from customer zone j to collection center k in period t (KgCO_{2}/unit/period) |

$c{e}_{klt}^{tcm}$ | Carbon emission for shipping unit product from collection center k to resale market l in period t (KgCO_{2}/unit/period) |

$c{e}_{kmt}^{tcr}$ | Carbon emission for shipping unit product from collection center k to fiber reclamation mill m in period t (KgCO_{2}/unit/period) |

$c{e}_{knt}^{tcf}$ | Carbon emission for shipping unit product from collection center k to flocking industry n in period t (KgCO_{2}/unit/period) |

$c{e}_{kt}^{pcc}$ | Carbon emission during processing unit product at collection center k in period t (KgCO_{2}/unit/period) |

$c{e}_{mt}^{prm}$ | Carbon emission during processing unit product at fiber reclamation mill m in period t (KgCO_{2}/unit/period) |

$c{e}_{nt}^{pfi}$ | Carbon emission during processing unit product at flocking industry n in period t (KgCO_{2}/unit/period) |

${\tilde{\pi}}_{st}$ | Unit purchase cost of material from supplier s in period t ($/unit/period) |

${\tilde{\beta}}_{it}$ | Manufacturing cost per product at manufacturing facility i in period t ($/unit/period) |

${\tilde{\chi}}_{kt}$ | Processing cost per product at collection center k in period t ($/unit/period) |

${\tilde{\epsilon}}_{mt}$ | Processing cost per product at fiber reclamation mill m in period t ($/unit/period) |

${\tilde{\varphi}}_{nt}$ | Processing cost per product at flocking industry n in period t ($/unit/period) |

${\tilde{\phi}}_{it}$ | Capacity of manufacturing facility i in period t (units/period) |

${\tilde{\gamma}}_{kt}$ | Capacity of collection center k in period t (units/period) |

${\tilde{\eta}}_{mt}$ | Capacity of fiber reclamation mill m in period t (units/period) |

${\tilde{\lambda}}_{nt}$ | Capacity of flocking industry n in period t (units/period) |

${\tilde{\nu}}_{st}$ | Capacity of supplier s in period t (units/period) |

${\tilde{p}}_{st}^{sd}$ | Probability of disruption risk at supplier s in period t |

${\tilde{p}}_{it}^{md}$ | Probability of disruption risk at manufacturing facility i in period t |

${\tilde{p}}_{kt}^{cd}$ | Probability of disruption risk at collection center k in period t |

Decision Variables | |

${Q}_{sit}^{SM}$ | Transportation quantity from supplier s to manufacturing facility i in period t (units/period) |

${Q}_{ijt}^{MC}$ | Transportation quantity from manufacturing facility i to customer zone j in period t (units/period) |

${Q}_{jkt}^{cc}$ | Transportation quantity from customer zone j to collection center k in period t (units/period) |

${Q}_{klt}^{cm}$ | Transportation quantity from collection center k to resale market l in period t (units/period) |

${Q}_{kmt}^{crm}$ | Transportation quantity from collection center k to fiber reclamation mill m in period t (units/period) |

${Q}_{knt}^{cfi}$ | Transportation quantity from collection center k to flocking industry n in period t (units/period) |

${x}_{i}=\{\begin{array}{c}1\\ 0\end{array}$ | If a manufacturing facility i is open 1, otherwise 0 |

${y}_{k}=\{\begin{array}{c}1\\ 0\end{array}$ | If a collection center k is open 1, otherwise 0 |

#### 2.2. Formulation of Objective Functions

_{cost}in Equation (1) minimizes the total cost of supply chain. Second, objective function f

_{sus}in Equation (2) minimizes the total carbon emission. Third, objective function f

_{edc}in Equation (3) minimizes expected disruption cost. Various estimations related to these objectives are discussed in below section.

_{cost}(x) = Total supply chain cost

_{sus}(x) = Total carbon emission

_{edc}(x) = Expected disruption cost

#### a. Total supply chain cost (Economic objective)

#### b. Total Carbon emission (Sustainable objective)

#### c. Expected disruption cost (Resilience objective)

#### 2.3. Formulation of Constraints

## 3. Proposed Solution Methodology

- (1)
- Set the linguistic variables for the importance of objectives.
- (2)
- Evaluate the importance of objectives based on linguistic variables from Table 1.
- (3)
- Aggregate each of the fuzzy number using$$AF{N}_{q}=\left(\frac{{\omega}_{1}^{pes}+{\omega}_{2}^{pes}+\mathrm{...}+{\omega}_{n}^{pes}}{n},\frac{{\omega}_{1}^{mos}+{\omega}_{2}^{mos}+\mathrm{...}+{\omega}_{n}^{mos}}{n},\frac{{\omega}_{1}^{opt}+{\omega}_{2}^{opt}+\mathrm{...}+{\omega}_{n}^{opt}}{n}\right)$$
_{q}is aggregate fuzzy number of q^{th}objective and (${\omega}_{n}^{pes},{\omega}_{n}^{mos},{\omega}_{n}^{opt}$) is n^{th}decision maker perception of importance for q^{th}objective. - (4)
- Estimate the fuzzy weight of q
^{th}objective as shown below.$${\varpi}_{q}=\frac{{\omega}_{q}^{pes}+2{\omega}_{q}^{mos}+{\omega}_{q}^{opt}}{4}$$_{q}, q = 1, 2, 3 number of objectives. - (5)
- Calculate the normalized fuzzy weights of objectives by:$${\varpi}_{q}^{\prime}=\frac{{\varpi}_{q}}{{\displaystyle \sum _{q}{\varpi}_{q}}}$$
- (6)
- Finally, the single objective model using improved Werner’s method can be formed as below.$$\begin{array}{ll}maximize& \theta {\zeta}_{0}+(1-\theta )\left({\varpi}_{1}^{\prime}{\zeta}_{1}+{\varpi}_{2}^{\prime}{\zeta}_{2}+{\varpi}_{3}^{\prime}{\zeta}_{3}\right)\\ subject\text{}to& {\mu}_{cos\mathrm{t}}(x)\ge {\zeta}_{0}+{\zeta}_{1}\\ & {\mu}_{sus}(x)\ge {\zeta}_{0}+{\zeta}_{2}\\ & {\mu}_{res}(x)\ge {\zeta}_{0}+{\zeta}_{3}\\ & {\zeta}_{0},{\zeta}_{1},{\zeta}_{2},{\zeta}_{3}\in [0,1]\end{array}\phantom{\rule{0ex}{0ex}}\text{System constraints (26\u201338)}$$

_{1}, ζ

_{2}, and ζ

_{3}are the difference between satisfaction level of objectives with their minimum satisfaction level. That is, ζ

_{1}= µ

_{cost}– ζ

_{0}, ζ

_{2}= µ

_{sus}– ζ

_{0}, and ζ

_{3}= µ

_{res}– ζ

_{0}. ${\varpi}_{1}^{\prime},\text{}{\varpi}_{2}^{\prime},$ and ${\varpi}_{3}^{\prime}$ are the normalized weights for cost, sustainability, and resilience objectives, respectively.

## 4. Numerical Example

- A number of manufacturing facilities required to open and their locations.
- The amount of products produced at each opened facility and which customer zones satisfied from each opened facility.
- Suppliers selected for supplying material to each opened manufacturing facility.
- Quantity of material to be purchased from each selected supplier
- Number and locations of collection centers opened.
- Location of recycling facilities (i.e., flocking industry and reclamation mill) preferred for recycling used products.
- Location of resale market and quantity of products that can be resale to these markets.

#### 4.1. Data population

_{2}emissions) some reasonable assumptions were made, and all required calculations were done beforehand. Thereafter, two random numbers (n

_{1}, n

_{2}) are generated between 0.2 and 0.8 using uniform distribution and the pessimistic ${\vartheta}^{pes}$ and optimistic ${\vartheta}^{opt}$ values of fuzzy number $\tilde{\vartheta}$ are estimated as follows.

#### 4.2. Result and Discussion

#### 4.3. Sensitivity Analysis

## 5. Conclusions

_{2}emission; and (3) economic losses.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix A

Facility | Karachi | Hyderabad (India) | Kolkata |
---|---|---|---|

Manufacturing facility | 900,000 | 800,000 | 600,000 |

Facility | Karachi | Delhi | Hyderabad (India) |
---|---|---|---|

Collection center | 300,000 | 400,000 | 250,000 |

Facility | Karachi | Hyderabad (India) | Kolkata | |||
---|---|---|---|---|---|---|

Period | 1 | 2 | 1 | 2 | 1 | 2 |

Manufacturing facility | 15 | 15.44 | 12 | 12.28 | 10 | 9.74 |

Supplier Location | Karachi | Faisalabad | Hyderabad (India) | Dhaka | Shaoxing | |||||
---|---|---|---|---|---|---|---|---|---|---|

Period | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |

Purchase cost | 4.0 | 4.10 | 3.50 | 3.57 | 4.50 | 4.62 | 4.10 | 4.20 | 3.60 | 3.70 |

Facility Location | Karachi | Faisalabad | Kolkata | Dhaka | ||||
---|---|---|---|---|---|---|---|---|

Period | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |

Reclamation mill | _ | 4.0 | 4.11 | 3.50 | 3.60 | _ | ||

Flocking industry | 3.50 | 3.58 | _ | _ | 2.50 | 2.56 |

Location | Karachi | New Delhi | Hyderabad (India) | |||
---|---|---|---|---|---|---|

Period | 1 | 2 | 1 | 2 | 1 | 2 |

Processing cost | 4.0 | 4.12 | 5.50 | 5.63 | 4.50 | 4.60 |

Region | Pakistan | India | Bangladesh | China | |||||
---|---|---|---|---|---|---|---|---|---|

Potential location | Karachi | Faisalabad | Hyderabad (India) | New Delhi | Kolkata | Dhaka | Shaoxing | ||

Period | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | |

Resilience Index (0–100) | 22.2 | 11.6 | 27.1 | 19.2 | 29.0 | 21.1 | 45.3 | 32.2 | |

Relative Probability of disruption risks | 0.2815 | 0.2798 | 0.2637 | 0.2558 | 0.2569 | 0.2498 | 0.1979 | 0.2146 |

Potential Location | Karachi | Faisalabad | Lahore | Hyderabad (India) | Kolkata | New Delhi | Mumbai | Dhaka | Shaoxing | Mali | Sabha |
---|---|---|---|---|---|---|---|---|---|---|---|

Karachi | _ | 0.1679 | 0.1808 | 0.2752 | 0.3511 | 0.2551 | 0.0891 | 0.1311 | 0.1608 | 0.2322 | 0.3043 |

Faisalabad | 0.1679 | _ | 0.0463 | 0.2807 | 0.2811 | 0.1030 | 0.2352 | 0.2704 | 0.2985 | _ | _ |

Lahore | 0.1808 | 0.0463 | _ | 0.1985 | 0.2543 | 0.0891 | 0.2241 | 0.2833 | 0.3115 | _ | _ |

Hyderabad (India) | 0.2752 | 0.2807 | 0.1985 | _ | 0.1985 | 0.2062 | 0.1072 | 0.1546 | 0.3039 | 0.3878 | 0.3868 |

Kolkata | 0.3511 | 0.2811 | 0.2543 | 0.1985 | _ | 0.1972 | 0.2585 | 0.0948 | 0.2129 | _ | _ |

New Delhi | 0.2551 | 0.1030 | 0.0891 | 0.2062 | 0.1972 | _ | 0.1886 | 0.2073 | 0.3567 | 0.4475 | 0.5221 |

Mumbai | 0.0891 | 0.2352 | 0.2241 | 0.1072 | 0.2585 | 0.1886 | _ | 0.0952 | 0.2133 | _ | _ |

Dhaka | 0.1311 | 0.2704 | 0.2833 | 0.1546 | 0.0948 | 0.2073 | 0.0952 | _ | 0.1425 | _ | _ |

Shaoxing | 0.1608 | 0.2985 | 0.3115 | 0.3039 | 0.2129 | 0.3567 | 0.2133 | 0.1425 | _ | _ | _ |

Potential Location | Karachi | Faisalabad | Lahore | Hyderabad (India) | Kolkata | New Delhi | Mumbai | Dhaka | Shaoxing | Mali | Sabha |
---|---|---|---|---|---|---|---|---|---|---|---|

Karachi | _ | 0.1714 | 0.1767 | 0.2675 | 0.3613 | 0.2486 | 0.0872 | 0.1349 | 0.1569 | 0.2275 | 0.2982 |

Faisalabad | 0.1714 | _ | 0.0453 | 0.2728 | 0.2893 | 0.1004 | 0.2302 | 0.2783 | 0.2913 | _ | _ |

Lahore | 0.1767 | 0.0453 | _ | 0.1929 | 0.2617 | 0.0868 | 0.2194 | 0.2916 | 0.3040 | _ | _ |

Hyderabad (India) | 0.2675 | 0.2728 | 0.1929 | _ | 0.2043 | 0.2009 | 0.1049 | 0.1591 | 0.2966 | 0.3769 | 0.3759 |

Kolkata | 0.3613 | 0.2893 | 0.2617 | 0.2043 | _ | 0.1922 | 0.2530 | 0.0976 | 0.2078 | _ | _ |

New Delhi | 0.2486 | 0.1004 | 0.0868 | 0.2009 | 0.1922 | _ | 0.1846 | 0.2133 | 0.3481 | 0.4361 | 0.5088 |

Mumbai | 0.0872 | 0.2302 | 0.2194 | 0.1049 | 0.2530 | 0.1846 | _ | 0.0980 | 0.2082 | _ | _ |

Dhaka | 0.1349 | 0.2783 | 0.2916 | 0.1591 | 0.0976 | 0.2133 | 0.0980 | _ | 0.1391 | _ | _ |

Shaoxing | 0.1569 | 0.2913 | 0.3040 | 0.2966 | 0.2078 | 0.3481 | 0.2082 | 0.1391 | _ | _ | _ |

_{2}emission during production, recycling, and transportation of product are estimated. It is estimated that a pair of Jeans produces 33.4 kg of CO

_{2}during its life cycle, out of which 40% accounts only for production and packaging process, 9% of raw material production, and 3% in recycling or landfill [21]. In this case, CO

_{2}emissions during production at different locations are set as shown in Table A10. The CO

_{2}emission per capita is used as a basis to differentiate the total emission at different locations. CO

_{2}emission per capita is collected from World Bank data [22].

_{2}emission during transportation is estimated from CO

_{2}emission index using cargo router calculator (CargoRouter [20]). CO

_{2}emission index defined as the amount of CO

_{2}released per unit of gaseous, liquid and solid fuels used [23], it is estimated in grams of CO

_{2}released. Table A11, Table A12 and Table A13 represent the embodied carbon footprints, CO

_{2}emission during processing at collection centers, and CO

_{2}emission during the recycling process. Table A14 shows the CO

_{2}emission during transportation between potential locations based on distance traveled and mode of transportation used.

Facility | Karachi | Hyderabad (India) | Kolkata | |||
---|---|---|---|---|---|---|

Period | 1 | 2 | 1 | 2 | 1 | 2 |

Emission during production | 12.66 | 12.92 | 13.65 | 14.05 | 13.96 | 13.55 |

Supplier Location | Karachi | Faisalabad | Hyderabad (India) | Dhaka | Shaoxing | |||||
---|---|---|---|---|---|---|---|---|---|---|

Period | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |

carbon footprints | 3.0 | 2.94 | 3.30 | 3.37 | 3.50 | 3.00 | 2.80 | 2.74 | 3.80 | 3.70 |

Location | Karachi | New Delhi | Hyderabad (India) | |||
---|---|---|---|---|---|---|

Period | 1 | 2 | 1 | 2 | 1 | 2 |

CO_{2} emission | 0.90 | 0.82 | 1.0 | 0.97 | 1.30 | 1.27 |

Location | Karachi | Faisalabad | Kolkata | Dhaka | ||||
---|---|---|---|---|---|---|---|---|

Period | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |

Reclamation mill | _ | 1.0 | 1.02 | 1.30 | 1.27 | _ | ||

Flocking industry | 0.90 | 0.82 | _ | _ | 1.0 | 1.02 |

Potential Location | Karachi | Faisalabad | Lahore | Hyderabad (India) | Kolkata | New Delhi | Mumbai | Dhaka | Shaoxing |
---|---|---|---|---|---|---|---|---|---|

Karachi | _ | 21.0 × 10^{-5} | 23.1 × 10^{-5} | 33.0 × 10^{-5} | 49.1 × 10^{-5} | 24.4 × 10^{-5} | 2.7 × 10^{-5} | 10.1 × 10^{-5} | 29.5 × 10^{-5} |

Faisalabad | 21.0 × 10^{-5} | _ | 2.7 × 10^{-5} | 37.3 × 10^{-5} | 40.6 × 10^{-5} | 11.3 × 10^{-5} | 31.2 × 10^{-5} | 30.6 × 10^{-5} | 49.7 × 10^{-5} |

Lahore | 23.1 × 10^{-5} | 2.7 × 10^{-5} | _ | 36.7 × 10^{-5} | 38.5 × 10^{-5} | 9.5 × 10^{-5} | 31.7 × 10^{-5} | 32.6 × 10^{-5} | 51.8 × 10^{-5} |

Hyderabad (India) | 33.0 × 10^{-5} | 37.3 × 10^{-5} | 36.7 × 10^{-5} | _ | 26.7 × 10^{-5} | 28.4 × 10^{-5} | 14.0 × 10^{-5} | 17.4 × 10^{-5} | 29.8 × 10^{-5} |

Kolkata | 49.1 × 10^{-5} | 40.6 × 10^{-5} | 26.7 × 10^{-5} | 26.7 × 10^{-5} | _ | 29.4 × 10^{-5} | 37.5 × 10^{-5} | 5.5 × 10^{-5} | 22.3 × 10^{-5} |

New Delhi | 24.4 × 10^{-5} | 11.3 × 10^{-5} | 9.5 × 10^{-5} | 28.4 × 10^{-5} | 29.4 × 10^{-5} | _ | 26.3 × 10^{-5} | 32.1 × 10^{-5} | 49.3 × 10^{-5} |

Mumbai | 2.7 × 10^{-5} | 31.2 × 10^{-5} | 11.3 × 10^{-5} | 14.0 × 10^{-5} | 37.5 × 10^{-5} | 26.3 × 10^{-5} | _ | 8.4 × 10^{-5} | 27.8 × 10^{-5} |

Dhaka | 10.1 × 10^{-5} | 30.6 × 10^{-5} | 32.6 × 10^{-5} | 17.4 × 10^{-5} | 5.5 × 10^{-5} | 32.1 × 10^{-5} | 8.4 × 10^{-5} | _ | 23.0 × 10^{-5} |

Shaoxing | 29.5 × 10^{-5} | 49.7 × 10^{-5} | 51.8 × 10^{-5} | 29.8 × 10^{-5} | 22.3 × 10^{-5} | 49.3 × 10^{-5} | 27.8 × 10^{-5} | 23.0 × 10^{-5} | _ |

Objective Functions | Decision Maker Preferences | Fuzzy Calculations | |||||
---|---|---|---|---|---|---|---|

DM1 | DM2 | DM3 | DM4 | AFN | FW | NFW | |

Cost | ML | L | MH | L | (0.200,0.350,0.500) | 0.350 | 0.199 |

Sustainability | MH | H | MH | H | (0.575,0.725,0.875) | 0.725 | 0.411 |

Resilience | H | M | MH | H | (0.538,0.688,0.838) | 0.688 | 0.390 |

Location | Karachi | Lahore | New Delhi | Hyderabad (India) | ||||
---|---|---|---|---|---|---|---|---|

Period | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |

Demands (units) | 4000 | 4105 | 3000 | 3086 | 2500 | 2425 | 2000 | 2056 |

Percent of recovered used products | 57% | 59% | 51% | 52% | 55% | 57% | 52% | 53% |

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Importance Level | Abbreviation | Fuzzy Number |
---|---|---|

Very Low | VL | (0,0,0.2) |

Low | L | (0.05,0.2,0.35) |

Medium–Low | ML | (0.2,0.35,0.5) |

Medium | M | (0.35,0.5,0.65) |

Medium–High | MH | (0.5,0.65,0.8) |

High | H | (0.65,0.8,0.95) |

Very High | VH | (0.8,1,1) |

Objective Functions | Total Supply Chain Cost ($) | Sustainability (KgCO_{2}) | Expected Disruption Cost ($) |
---|---|---|---|

Minimize total supply chain cost | 1,084,865.00 | 305,416.60 | 307,182.30 |

Minimize sustainability | 1,486,758.00 | 280,782.30 | 331,000.70 |

Minimize expected disruption cost | 1,296,953.00 | 307,944.90 | 287,975.10 |

Method | µ_{cost} | µ_{sus} | µ_{res} | Total Supply Chain Cost ($) | Sustainability (KgCO_{2}) | Resilience ($) |
---|---|---|---|---|---|---|

Werner’s Method | 0.9918 | 0.1173 | 1.0000 | 1,088,161.00 | 304,759.50 | 287,975.10 |

Proposed Method | 0.1018 | 0.9029 | 0.7561 | 1,445,828.00 | 283,419.90 | 298,468.50 |

α | θ | µ_{Cost} | µ_{sus} | µ_{res} | Operational cost ($) | Sustainability (KgCO_{2}) | Resilience ($) |
---|---|---|---|---|---|---|---|

0.4 | 0.0–0.5 | 0.0868 | 0.8746 | 0.7640 | 1,588,538.00 | 384,441.10 | 422,511.00 |

0.6–0.9 | 0.8955 | 0.2389 | 0.8710 | 1,244,495.00 | 408,274.30 | 416,152.80 | |

1.00 | 0.8965 | 0.2389 | 0.6891 | 1,244,040.00 | 408,272.20 | 426,960.30 | |

0.6 | 0.0–0.5 | 0.09 | 0.8857 | 0.7609 | 1,530,208.00 | 343,699.70 | 371,470.40 |

0.6–0.9 | 0.8917 | 0.2292 | 0.8824 | 1,197,843.00 | 365,553.50 | 365,062.20 | |

1.00 | 0.8928 | 0.2292 | 0.6982 | 1,197,395.00 | 365,551.70 | 374,779.50 | |

0.9 | 0.0–0.5 | 0.1018 | 0.9029 | 0.7561 | 1,445,828.00 | 283,419.90 | 298,468.50 |

0.60 | 0.1065 | 0.8964 | 0.7561 | 1,443,965.00 | 283,595.30 | 298,468.50 | |

0.7–0.9 | 0.8860 | 0.2142 | 0.9000 | 1,130,670.00 | 302,126.20 | 292,278.00 | |

1.00 | 0.8871 | 0.2143 | 0.7121 | 1,336,783.00 | 301,128.10 | 311,485.30 |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Mari, S.I.; Lee, Y.H.; Memon, M.S.
Sustainable and Resilient Garment Supply Chain Network Design with Fuzzy Multi-Objectives under Uncertainty. *Sustainability* **2016**, *8*, 1038.
https://doi.org/10.3390/su8101038

**AMA Style**

Mari SI, Lee YH, Memon MS.
Sustainable and Resilient Garment Supply Chain Network Design with Fuzzy Multi-Objectives under Uncertainty. *Sustainability*. 2016; 8(10):1038.
https://doi.org/10.3390/su8101038

**Chicago/Turabian Style**

Mari, Sonia Irshad, Young Hae Lee, and Muhammad Saad Memon.
2016. "Sustainable and Resilient Garment Supply Chain Network Design with Fuzzy Multi-Objectives under Uncertainty" *Sustainability* 8, no. 10: 1038.
https://doi.org/10.3390/su8101038