# Multi-Objective Sustainable Closed-Loop Supply Chain Network Design Considering Multiple Products with Different Quality Levels

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## Abstract

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## 1. Introduction

- Designing a robust multi-objective mathematical programming model for a sustainable closed-loop supply chain that can integrate the design of forward and reverse networks (horizontal integration) and optimize strategic and tactical decisions simultaneously (vertical integration). It should be mentioned that the strategic decisions are mainly related to the structure of the supply chain in the upcoming years; how the supply chain is configured, how resources are allocated, and what processes take place at each level of the supply chain. Strategic decisions in this study are to determine the location of facilities such as factories, distribution centers, and collection centers (long-term decisions). The tactical decisions deal with satisfying demands, binding contracts, inventory management and control policies, and so on. In the planning phase, organizations need to consider demand uncertainty within the planning time horizon. Therefore, in this research, it is determined how many products should be sent to each warehouse and which warehouses should satisfy the demand of which market segment under uncertainty conditions.
- Considering several products with various quality levels (green/non-green) for different types of customers with different consumption trends.
- Taking the uncertainty of supply chain parameters such as cost and demand into account. Nowadays, costs are swiftly increasing, leading to changes in customer demands. This fact must be considered in decision-making so that more reliable and accurate decisions can be made.

## 2. Literature Review

_{2}emissions from the transportation sector by establishing hubs on the paths of suppliers and customers [37]. Uncertainty has affected the purchase, processing, markets, and other levels of the closed-loop supply chain and significantly enhanced the complexity of rebuilding and reduced the efficiency of the process. Uncertainty has also hindered the sustainable development of industries [38]. Supply chain planning at the strategic level as well as the dynamic nature of industries generally lead to environmental and systemic uncertainties; thus, strategic planning appears to be a difficult task and can be associated with a high rate of potential error [6]. Uncertainty in manufacturing systems in the real world is categorized into two classes: environmental and systemic uncertainties. Environmental uncertainty includes uncertainty in the amounts of demand and supply, originating from the behavior of consumers and suppliers. Systemic uncertainty also addresses issues such as uncertainty in manufacturing products, distribution, collection, and recycling. For example, uncertainties found in the capacity of facilities, production costs, and inventory control fall into this category. Vahdani et al. provided a multi-layered, multi-period model to seamlessly model the forward and reverse supply chain under uncertainty. The uncertainty in the parameters is considered to be fuzzy in this model [39]. Sahebjamnia et al. proposed a model to evaluate the design of a sustainable supply chain network under uncertainty conditions. In their research, a multi-objective model is provided designed to optimize the costs, environmental effects, and social impacts, which has been solved using metaheuristic algorithms [4]. Zhen et al. provided a two-objective model for planning a sustainable forward and reverse supply chain network under uncertainty conditions aimed at minimizing the costs and greenhouse gas emissions. They used a scenario-based method in the study to deal with uncertainty [12]. Dehghan et al. considered uncertainty conditions as a combination of possibility, probability, and stability [40]. A multi-period, multi-product model has been developed in this research. The uncertainty in the parameters has been examined in two scenario-based and fuzzy types. A case study has also been provided in this study for testing the model [40]. Fathollahi-Fard et al. suggested an integrated Water Supply and Wastewater Collection System (WSWCS) under stochastic uncertainty [41]. Khorshidvand et al. proposed a new hybrid method where SCC decisions and CLSCND goals are involved simultaneously. This decision-making approach is first implemented on price, greenness, and advertising and is then focused on maximizing profits and minimizing CO

_{2}emissions under demand uncertainty conditions [42]. Tehrani and Gupta proposed a sustainable closed-loop supply chain network for the tire industry under environmental uncertainty conditions by considering several recovery options, including energy, recycling, and recovery. The study was set to design and develop a multi-objective, multi-product, multi-layered, and multi-capacity supply chain network under fuzzy random programming uncertainty [16]. Sazvar et al. presented a scenario-based multi-objective mixed integer linear programming model to design a sustainable CLPSC, which evaluates the reverse flow of expired drugs as three classes (should be discarded, can be reproduced, and can be recycled) [43]. Gholizadeh et al. presented an environmentally friendly multi-objective model for a supply chain. The objectives of this study were to minimize costs, maximize vehicle productivity, and minimize information fraud in the process of sharing information in supply chain elements [44]. A summary of the related studies is presented in Table 1.

## 3. Problem Statement and the Mathematical Programming Model

- New goods, whether brand new or remanufactured, are transported to the forward path of the network (factory to warehouse and warehouse to customer) to meet the customer’s demand.
- The products to be disposed of are collected from the customers and sent to the DCs for the disassembly process. In the disassembly centers, if the product is reconstructed, then it will enter the production cycle, and if it is non-recyclable, it is sent to disposal centers.
- The disassembled product is delivered from the DCs to the factory for the reproduction process.
- Factories have two production processes. One is the assembly of products sent from DCs and another is the supply of raw materials from suppliers and manufacturing of new products.

- The demands of all customers are defined as fuzzy numbers.
- All products that are produced in a factory and stored in the warehouse are always free of defects.
- All customer demands are supplied by factories or through warehouses.
- All disposed of products that are entered into DCs will be recovered with a specified probability of θ and disposed of with a probability of 1-θ.
- The fixed and variable costs of constructing supply chain institutions are pre-determined and fuzzy in nature.
- The model is examined in the multi-product mode with green and non-green quality levels.
- The customers are divided into two categories of green customers and non-green customers according to their attitude towards product consumption (green/non-green) and their demands for green and non-green products are analyzed under uncertainty conditions (green products are products made of environmentally friendly material).
- Products can be produced in both green and non-green modes according to the customers’ demands.

_{2}emissions along the supply chain, and maximizing economic benefits (Social Indicator (SI)). The proposed mathematical model is presented as follows:

Sets | |

P | Products index |

Q | Products quality level index (green/non-green) |

F | Potential factories index |

W | Potential warehouses index |

C | Customers’ index |

I | Index of potential DCs |

N | Potential centers index |

TF | Transportation options for factories |

TW | Transportation options for warehouses |

TK | Transportation options for customers |

TN | Transportation options for disposal centers |

Parameters | |

${\tilde{d}}_{pc}^{q}$ | The demand for product $p$ with quality level $q$ for customer c |

${\tilde{ta}}_{fw}^{tf}$ | The transfer cost per unit from factory f to warehouse $w$ with transportation mode $tf$ |

${\tilde{tb}}_{wc}^{tw}$ | The transfer cost per unit from warehouse $w$ with transportation mode $tw$ to customer c |

${\tilde{tc}}_{ci}^{tk}$ | The transfer cost per unit from customer $c$ with transportation mode $tk$ to collection/disassembly site $i$ |

${\tilde{td}}_{if}^{ti}$ | The transfer cost per unit from disassembly center $i$ with transportation mode $ti$ to the factory site $f$ |

${\tilde{tn}}_{in}^{tn}$ | The transfer cost per unit from disassembly center $i$ with transportation mode tn to landfill $n$ |

${\tau}_{fw}^{tf}$ | The transportation rate from factory $f$ to warehouse w with transportation mode tf |

${\tau}_{wc}^{tw}$ | The transportation rate from warehouse w with transportation mode $tw$ to customer $c$ |

${\tau}_{ci}^{tk}$ | The transportation rate from customer $c$ with transportation mode $tk$ to collection/disassembly site $i$ |

${\tau}_{if}^{ti}$ | The transportation rate from disassembly center $i$ with transportation mode ti to factory site $f$ |

${\tau}_{in}^{tn}$ | The transportation rate from disassembly center $i$ with transportation mode $tn$ to landfill $n$ |

${\tilde{va}}_{f}^{pq}$ | The variable cost per producing $a$ unit of product p with quality level $q$ in factory $f$ |

${\tilde{vb}}_{w}^{.}$ | The variable cost per maintaining $a$ unit of product in the warehouse w |

${\tilde{vc}}_{c}^{.}$ | The variable cost per collecting $a$ unit of product from customer c for the recovery operation |

${\tilde{vd}}_{i}^{pq}$ | The variable cost per disassembling a unit of product p with quality level $q$ at the DC site $i$ |

${\tilde{vr}}_{f}^{pq}$ | The variable cost per reproduction of a unit of product p with quality level q assembled at factory $f$ |

$h{a}_{f}^{pq}$ | The maximum production capacity of product p with quality level $q$ in factory $f$ |

$h{r}_{f}^{pq}$ | The maximum capacity for the reproduction of product p with quality level q in factory $f$ |

$q{d}_{p}^{q}$ | The minimum percentage of product $p$ with quality level $q$ that is collected for recycling from the customer. |

$\theta $ | A percentage of products that are shipped intactly from the $ith$ DC to the factory. |

$m{a}_{fw}$ | The distance from factory f to warehouse $w$ |

$m{b}_{wc}$ | The distance from warehouse $w$ to customer $c$ |

$m{c}_{ci}$ | The distance from customer c to collection/DC site $i$ |

$m{d}_{if}$ | The distance of DC $i$ from factory site f |

$m{n}_{in}$ | The transportation rate from DC $i$ to landfill $n$ |

Environmental Parameters | |

$e{a}_{f}^{pq}$ | The CO_{2} emission rate per manufacturing of a unit product $p$ with quality level q in factory $f$ |

$e{a}_{f}^{pq}$ | The emission rate of CO_{2} per production of a unit product $p$ with quality level q in factory $f$ |

$e{b}_{w}^{pq}$ | The CO_{2} emission rate per processing of a unit product p with quality level $q$ in warehouse $w$ |

$e{d}_{i}^{pq}$ | The emission rate of CO_{2} per recovery of a unit product p with quality level q in DC $i$ |

$e{r}_{f}^{pq}$ | The CO_{2} emission rate per reproduction of a unit product p with quality level q in factory $f$ |

$et{a}^{tf}$ | The emission rate of CO_{2} with transportation mode tf to transfer a unit product p from factory to warehouse in unit of length |

$et{b}^{tw}$ | The CO_{2} emission rate corresponding with transportation mode tw to transfer a unit product $p$ from the warehouse to the customer in unit of length |

$et{k}^{tk}$ | The emission rate of CO_{2} with transportation mode tk to collect a unit product from customer to DC in unit of length |

${\mathrm{etd}}^{\mathrm{ti}}$ | The CO_{2} emission rate corresponding with transportation mode $ti$ to transfer a unit product from DC to factory in unit of length |

${\widehat{\mathrm{fa}}}_{\mathrm{f}}$ | The fixed cost of constructing factory $f$ |

${\widehat{\mathrm{fb}}}_{\mathrm{w}}$ | The fixed cost of constructing the warehouse $w$ |

${\widehat{\mathrm{fd}}}_{\mathrm{i}}$ | The fixed cost of constructing DC site $i$ |

${\mathrm{hb}}_{\mathrm{w}}$ | The maximum processing capacity in the warehouse |

${\mathrm{hd}}_{\mathrm{i}}$ | The maximum capacity for recovery operation in DC $i$ |

${\mathrm{hn}}_{\mathrm{n}}$ | The maximum capacity to dispose of products in $n$ |

$x{a}_{f}$ | is equal to one if factory $\mathrm{f}$ is open and otherwise equal to zero |

${\mathrm{xb}}_{\mathrm{w}}$ | is equal to one if warehouse w is open, otherwise equal to zero |

$x{d}_{i}$ | is equal to one if DC $\mathrm{i}$ is open, otherwise equal to zero |

$Y{a}_{fw,tf}^{pq}$ | The number of $p$-type products with quality level $q$ that are transferred from factory $f$ to warehouse $w$ with transportation option $tf$ |

$Y{b}_{wc,tw}^{pq}$ | The number of p-type products with quality level $q$ that are transferred from the warehouse $w$ and the customer $c$ with transportation option $tw$ |

$Y{c}_{ci,tk}^{pq}$ | The number of $p$-type products with quality level $q$ sent from customer $c$ to the DC site $i$ with transportation option $tk$ for recycling |

$Y{n}_{in,tn}^{pq}$ | The number of products p with quality level $q$ that are passed transferred from the DC $i$ to the disposal site n with transportation option $tn$ for disposal |

$Y{d}_{if,ti}^{pq}$ | The amount of product that is sent from DC $i$ to factory $f$ with transportation option $ti$ |

${X}_{f}^{pq}$ | The amount of product $p$ with the new quality level $q$ that should be produced in factory $f$ |

_{2}total emission was obtained by summing the phrases of the CO

_{2}total emission due to production (EP), the CO

_{2}total emission due to storage in the warehouse (EH), the CO

_{2}total emission due to product disassembly (ED), the CO

_{2}total emission due to product reproduction (ER), and the CO

_{2}total emission due to transfer (ET). A transportation rate described in the previous sections was used to calculate the emission in each transportation model.

#### 3.1. Robust Possibilistic Programming Model

#### 3.2. The Proposed Robust Model for the Sustainable Closed-Loop Supply Chain Problem

#### 3.3. Solving the Proposed Three-Objective Model with the AEC Approach

#### 3.3.1. Determining the Range of Epsilons’ Values

#### 3.3.2. The AEC Model

## 4. Numerical Example

#### 4.1. Solving the Problem under Uncertainty Conditions

#### 4.2. Analyzing the Results and Solving the Model Considering the Different Sizes of the Problem

#### 4.3. Analyzing the Results and Solving the Model Considering Various Problems with Different Sizes

## 5. Discussion

#### Managerial Discussions and Practical Implications

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 9.**Comparison of the robust possibilistic programming and possibilistic programming at a confidence level of 0.9.

**Figure 10.**Values of the economic objective functions of the robust possibilistic programming and possibilistic programming models.

**Figure 11.**Values of the environmental objective functions of the robust possibilistic programming and possibilistic programming models.

**Figure 12.**Solution time of the robust possibilistic programming and possibilistic programming methods.

Research | Network Structure | Sustainability Aspects | Multi-Product | Types of Customers | Solution Methods | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|

Forward | Reverse | Closed-Loop | Economic | Environmental | Social | Main | Variety of Quality | Recycled | |||

[22] | * | * | * | * | * | * | Heuristic/LR | ||||

[24] | * | * | CPLEX | ||||||||

[25] | * | * | * | * | * | * | * | HCC&CF | |||

[27] | * | * | * | * | * | MOPSO/EC | |||||

[28] | * | * | * | Lexicograph | |||||||

[30] | * | * | * | * | AEC | ||||||

[31] | * | * | * | Heuristic | |||||||

[19] | * | * | * | * | * | CPLEX | |||||

[41] | * | * | * | * | * | * | Hybrid method | ||||

[42] | * | * | * | LR | |||||||

[43] | * | * | * | Heuristic | |||||||

Our research | * | * | * | * | * | * | * | * | * | * | Lexicograph/AEC |

${\widehat{\mathrm{fa}}}_{\mathrm{f}}=\mathrm{fa}{1}_{\mathrm{f}},\mathrm{fa}{2}_{\mathrm{f}},\mathrm{fa}{3}_{\mathrm{f}},\mathrm{fa}{4}_{\mathrm{f}}.$ ${\widehat{\mathrm{fb}}}_{\mathrm{w}}=\mathrm{fb}{1}_{\mathrm{w}},\mathrm{fb}{2}_{\mathrm{w}},\mathrm{fb}{3}_{\mathrm{w}},\mathrm{fb}{4}_{\mathrm{w}}$ ${\widehat{\mathrm{fd}}}_{\mathrm{i}}=\mathrm{fd}{1}_{\mathrm{i}},\mathrm{fd}{2}_{\mathrm{i}},\mathrm{fd}{3}_{\mathrm{i}},\mathrm{fd}{4}_{\mathrm{i}}$ ${\tilde{d}}_{pc}^{q}=\mathrm{d}{1}_{pc}^{q},\mathrm{d}{2}_{pc}^{q},\mathrm{d}{3}_{pc}^{q},\mathrm{d}{4}_{pc}^{q}$ ${\tilde{va}}_{fw}^{pq}=va{1}_{fw}^{pq},va{2}_{fw}^{pq},va{3}_{fw}^{pq},va{4}_{fw}^{pq}$ ${\tilde{vb}}_{wc}^{pq}=vb{1}_{wc}^{pq},vb{2}_{wc}^{pq},vb{3}_{wc}^{pq},vb{4}_{wc}^{pq}$ ${\tilde{vr}}_{f}^{pq}=vn{1}_{f}^{pq},vn{2}_{f}^{pq},vn{3}_{f}^{pq},vn{4}_{f}^{pq}$ | ${\tilde{ta}}_{fw}^{tf}=ta{1}_{fw}^{tf},ta{2}_{fw}^{tf},ta{3}_{fw}^{tf},ta{4}_{fw}^{tf}$ ${\tilde{tb}}_{wc}^{tw}=tb{1}_{wc}^{tw},tb{2}_{wc}^{tw},tb{3}_{wc}^{tw},tb{4}_{wc}^{tw}$ ${\tilde{tc}}_{ci}^{tk}=\mathrm{tc}{1}_{ci}^{tk},\mathrm{tc}{2}_{ci}^{tk},\mathrm{tc}{3}_{ci}^{tk},\mathrm{tc}{4}_{ci}^{tk}$ ${\tilde{td}}_{if}^{ti}=td{1}_{if}^{ti}$,$td{2}_{if}^{ti},td{3}_{if}^{ti},td{4}_{if}^{ti}$ ${\tilde{tn}}_{in}^{tn}=tn{1}_{in}^{tn},tn{2}_{in}^{tn},tn{3}_{in}^{tn},tn{4}_{in}^{tn}$ ${\tilde{vc}}_{ci}^{pq}=\mathrm{vc}{1}_{ci}^{pq},\mathrm{vc}{2}_{ci}^{pq},\mathrm{vc}{3}_{ci}^{pq},\mathrm{vc}{4}_{ci}^{pq}$ ${\tilde{vd}}_{if}^{pq}=vd{1}_{if}^{pq}$,$vd{2}_{if}^{pq},vd{3}_{if}^{pq},vd{4}_{if}^{pq}$ |

W = 2 | |||||||
---|---|---|---|---|---|---|---|

Customer | Product | Quality Level | TW = 1 | TW = 2 | Quality Level | TW = 1 | TW = 2 |

1 | P = 1 | Q = 1 | 117 | Q = 2 | 185 | ||

2 | 129 | 122 | |||||

3 | 106 | 150 | |||||

4 | 200 | 176 | |||||

5 | 116 | 125 | |||||

6 | 136 | 135 | |||||

7 | 159 | 183 | |||||

8 | 178 | 130 | |||||

1 | P = 2 | 155 | 130 | ||||

2 | 135 | 186 | |||||

3 | 200 | 158 | |||||

4 | 113 | 164 | |||||

5 | 167 | 143 | |||||

6 | 113 | 115 | |||||

7 | 123 | 167 | |||||

8 | 111 | 150 |

Optimal | Obj1 | Obj2 | Obj3 | Epsilon2 | Epsilon3 |
---|---|---|---|---|---|

1 | 110310.1 | 34298.26 | 17 | 34298.26 | 13.508 |

2 | 109848.8 | 38474.18 | 17 | 38865.33 | 16.731 |

3 | 111084.8 | 27381.6 | 17 | 27381.6 | 11.229 |

4 | 110140.2 | 38131.43 | 18 | 38131.43 | 17.838 |

5 | 198484.2 | 21043.82 | 18 | 21043.82 | 17.7 |

6 | 110581.8 | 34023.52 | 18 | 34023.52 | 17.994 |

7 | 110771 | 30186.08 | 17 | 30186.08 | 10.546 |

8 | 132514.5 | 22876.38 | 18 | 22876.38 | 16.438 |

9 | 110465.1 | 34909.98 | 18 | 34909.98 | 17.854 |

10 | 110393.2 | 35457.03 | 18 | 35457.03 | 17.743 |

11 | 151137.2 | 21206.96 | 16 | 21206.96 | 12.044 |

12 | 111650.8 | 26409.57 | 18 | 26409.57 | 17.808 |

**Table 5.**The outputs of the bi-objective model considering the first and second objective functions.

Optimal | Obj1 | Obj2 | Epsilon2 |
---|---|---|---|

1 | 257609.1 | 20858.02 | 20858.02 |

2 | 126651 | 23489.32 | 23489.32 |

3 | 111254.8 | 26120.62 | 26120.62 |

4 | 110846.1 | 28751.91 | 28751.91 |

5 | 110681 | 30897.68 | 31383.21 |

6 | 110316.6 | 34014.51 | 34014.51 |

7 | 109922.1 | 36645.81 | 36645.81 |

8 | 109803.8 | 38474.18 | 39277.11 |

Optimal | Obj1 | Obj3 | Epsilon3 |
---|---|---|---|

1 | 109956.5 | 18 | 18 |

2 | 109956.5 | 18 | 17.556 |

4 | 109686.7 | 17 | 16.667 |

5 | 109666.3 | 16 | 15.778 |

6 | 109662.1 | 15 | 14.889 |

7 | 109641.7 | 14 | 14 |

**Table 7.**Comparing the results of the robust possibilistic programming and possibilistic programming models.

Possibilistic Programming | Robust Possibilistic Programming | ||||||
---|---|---|---|---|---|---|---|

α, β, λ | Obj1 | Obj2 | Obj3 | Obj1 | Obj2 | Obj3 | |

1 | 0.7 | 112492.98 | 36736.85 | 17 | 149040.568 | 44036.731 | 17 |

2 | 0.8 | 115988.58 | 42318.86 | 17 |

Factory (Potential) | Warehouse (Potential) | Customers | Collection Centers | Disposal Centers | Transportation Mode for Factory | Transportation Mode for Warehouse | Transportation Mode for Customers | Transportation Mode for Collection Centers | Transportation Mode for Disposal Centers | Product | Quality | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Sample Problem | F | W | C | I | N | TF | TW | TK | TI | TN | P | Q |

1 | 3 | 2 | 8 | 3 | 3 | 2 | 4 | 3 | 4 | 2 | 2 | 2 |

2 | 3 | 5 | 12 | 3 | 3 | 2 | 4 | 3 | 4 | 2 | 2 | |

3 | 3 | 5 | 14 | 3 | 3 | 2 | 4 | 3 | 4 | 2 | 2 | |

4 | 3 | 9 | 16 | 4 | 4 | 2 | 4 | 3 | 4 | 2 | 3 | |

5 | 4 | 9 | 18 | 4 | 4 | 3 | 5 | 4 | 4 | 2 | 3 | |

6 | 4 | 9 | 20 | 4 | 4 | 3 | 5 | 4 | 5 | 3 | 3 | |

7 | 4 | 11 | 22 | 5 | 5 | 3 | 5 | 4 | 5 | 3 | 4 | |

8 | 5 | 11 | 24 | 5 | 5 | 3 | 5 | 4 | 5 | 3 | 4 | |

9 | 5 | 11 | 26 | 5 | 5 | 4 | 6 | 5 | 5 | 3 | 4 | |

10 | 5 | 13 | 28 | 6 | 6 | 4 | 6 | 5 | 5 | 3 | 5 | |

11 | 6 | 13 | 30 | 6 | 6 | 4 | 6 | 5 | 6 | 4 | 5 | |

12 | 6 | 13 | 32 | 6 | 6 | 4 | 6 | 5 | 6 | 4 | 5 | |

13 | 6 | 15 | 34 | 7 | 7 | 4 | 7 | 6 | 6 | 4 | 6 | |

14 | 8 | 15 | 36 | 7 | 7 | 5 | 7 | 6 | 6 | 4 | 6 | |

15 | 8 | 15 | 38 | 7 | 7 | 5 | 7 | 6 | 6 | 4 | 6 |

**Table 9.**Comparison of the results of the robust possibilistic programming and possibilistic programming models.

Possibilistic Programming | Solution Time | Robust Possibilistic Programming | Solution Time | |||||
---|---|---|---|---|---|---|---|---|

Obj1 | Obj2 | Obj3 | Obj1 | Obj2 | Obj3 | |||

1 | 112493 | 36736.85 | 17 | 32.2 | 149040.6 | 44036.73 | 17 | 31.8 |

2 | 187169 | 29267.91 | 29 | 35.1 | 226106 | 31864.99 | 29 | 32.3 |

3 | 226508.6 | 51318.52 | 30 | 34.6 | 280990.5 | 56885.9 | 30 | 34 |

4 | 221298.8 | 37652.39 | 35 | 43.95 | 275637.5 | 42646.97 | 35 | 38.5 |

5 | 495118.2 | 79996.83 | 44 | 52.3 | 614204.3 | 88622.48 | 41 | 58.3 |

6 | 371434.2 | 105694.7 | 44 | 58.9 | 459974.3 | 116882 | 44 | 59.7 |

7 | 645098.4 | 119017.6 | 45 | 86.7 | 802963.3 | 134888 | 44 | 82.4 |

8 | 623957.8 | 116261.9 | 57 | 82.8 | 776619 | 131765.5 | 57 | 85.3 |

9 | 598870.6 | 125125.3 | 56 | 10.2 | 744473.2 | 144066.5 | 55 | 15.65 |

10 | 819076.4 | 222814.6 | 61 | 14.28 | 1017886 | 246844 | 61 | 17.32 |

11 | 895641.9 | 295736.8 | 83 | 14.78 | 1113248 | 330478.7 | 83 | 18.2 |

12 | 1031021 | 406372.7 | 57 | 15.6 | 1282136 | 438746.5 | 57 | 18.8 |

13 | 1202309 | 379571.3 | 83 | 27 | 1494380 | 444382.5 | 82 | 31 |

14 | 1226793 | 346850.2 | 75 | 28.7 | 1524536 | 385002.7 | 75 | 34.6 |

15 | 1265524 | 438293.4 | 73 | 35.2 | 1572802 | 496758.6 | 73 | 46.7 |

**Table 10.**Comparing the results of changing the consumption attitudes of green and non-green customers.

Non-Green Customers | Green Customers | |||||||||
---|---|---|---|---|---|---|---|---|---|---|

Scenario | Obj1 | Obj2 | Obj3 | Epsilon2 | Epsilon3 | Obj1 | Obj2 | Obj3 | Epsilon2 | Epsilon3 |

0 | 95502.610 | 18508.589 | 17 | 18508.589 | 17 | 95502.610 | 18508.589 | 17 | 18508.589 | 17 |

0/2 | 104548.366 | 20015.360 | 17 | 20015.360 | 18 | 105380.050 | 20116.889 | 17 | 20116.889 | 18 |

0/3 | 109068.938 | 20903.887 | 17 | 20903.887 | 17 | 110324.145 | 21042.220 | 17 | 21042.220 | 17 |

0/4 | 113590.680 | 21565.449 | 17 | 21776.655 | 18 | 115270.113 | 21801.261 | 17 | 21976.472 | 18 |

0/5 | 118091.996 | 22440.993 | 17 | 22664.224 | 17 | 120206.676 | 22727.276 | 17 | 22908.389 | 17 |

0/6 | 122613.476 | 22735.056 | 17 | 22135.056 | 18 | 125164.781 | 22800.815 | 17 | 22400.815 | 18 |

0/7 | 127115.501 | 22970.552 | 17 | 22970.552 | 17 | 130409.262 | 23284.226 | 17 | 23284.226 | 17 |

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**MDPI and ACS Style**

Soon, A.; Heidari, A.; Khalilzadeh, M.; Antucheviciene, J.; Zavadskas, E.K.; Zahedi, F.
Multi-Objective Sustainable Closed-Loop Supply Chain Network Design Considering Multiple Products with Different Quality Levels. *Systems* **2022**, *10*, 94.
https://doi.org/10.3390/systems10040094

**AMA Style**

Soon A, Heidari A, Khalilzadeh M, Antucheviciene J, Zavadskas EK, Zahedi F.
Multi-Objective Sustainable Closed-Loop Supply Chain Network Design Considering Multiple Products with Different Quality Levels. *Systems*. 2022; 10(4):94.
https://doi.org/10.3390/systems10040094

**Chicago/Turabian Style**

Soon, Amirhossein, Ali Heidari, Mohammad Khalilzadeh, Jurgita Antucheviciene, Edmundas Kazimieras Zavadskas, and Farbod Zahedi.
2022. "Multi-Objective Sustainable Closed-Loop Supply Chain Network Design Considering Multiple Products with Different Quality Levels" *Systems* 10, no. 4: 94.
https://doi.org/10.3390/systems10040094