# A Smart Decision Support Framework for Sustainable and Resilient Supplier Selection and Order Allocation in the Pharmaceutical Industry

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. Sustainable Supplier Selection

#### 2.2. Sustainable Order Allocation

#### 2.3. Sustainable and Resilient Supplier Selection and Order Allocation

## 3. Problem Description

## 4. Methodology

- Phase 1: Sustainable and Resilient Supplier Selection

- ▪
- Step-1: Potential suppliers are evaluated based on TBL sustainability (economic, environment, social) and resilience criteria.
- ▪
- Step-2: Selection of sub-criteria for each sustainability and resilience criterion. (The sub-criteria have been included in Table 2.)
- ▪
- Step-3: Application of FE-AHP method to evaluate the relative weights of each supplier’s selection criteria.
- ▪
- Step-4: Application of FTOPSIS to rank the suppliers. (The linguistic variables used for calculating criteria weights and developing rankings of the suppliers are included in Table 3.)
- ▪
- Step-5: Preliminary selection of suppliers on the basis of defined thresholds of the closeness coefficient.
- ▪
- Step-6: A sensitivity analysis is performed to evaluate the robustness of the selected suppliers using the method identified by Forghani et al. [69].

- Phase 2: Order Allocation

- ▪
- Step-7: Identification of objectives for allocating optimal quantities to the potential suppliers. (The objectives are total cost, total travel time, environmental impact, acceptable quality limit, total value of sustainable purchasing.)
- ▪
- Step-8: Development of MOMINLP mathematical model along with demand, resource, and capacity constraints.
- ▪
- Step-9: Include uncertainty by developing the fuzzy MOMINLP mathematical model.
- ▪
- Step-10: Solve the model using nonlinear solver to determine minimum and maximum values of objective functions.
- ▪
- Step-11: Solve the model using AUGMECON2 and extract Pareto solutions.

- Phase 3: Best Solution Selection

- ▪
- Step-12: Use CRITIC method for determining objective functions’ weights for extracting best 10 Pareto solutions.
- ▪
- Step-13: Apply TOPSIS for the ranking of the best solutions for the time period considered.

Criteria | Sub-Criteria |
---|---|

Economic | Product Price |

Payment Terms | |

Product Quality | |

Use of Technology | |

Volume Flexibility | |

Vendor’s Reputation | |

Responsiveness | |

Product Mix | |

Past Business | |

Environment | Environmental Management System |

Energy Consumption | |

Waste Management System | |

Innovative Capability | |

Social | Employee Health & Safety |

Staff Personal & Technical Development | |

Information Disclosure | |

Resilience | Robustness |

Agility | |

Leanness | |

Flexibility |

**Table 3.**Linguistic variables used for FE-AHP and FTOPSIS (Adapted from [70]).

Performance Ranking of Alternatives | Importance of Criteria | ||
---|---|---|---|

Linguistic Variable | Fuzzy Number | Linguistic Variable | Fuzzy Number |

Very Low (VL) | (1, 1, 3) | Weakly Important (WI) | (0.1, 0.1, 0.3) |

Low (L) | (1, 3, 5) | Moderately Important (MI) | (0.1, 0.3, 0.5) |

Medium (M) | (3, 5, 7) | Important (I) | (0.3, 0.5, 0.7) |

High (H) | (5, 7, 9) | Strongly Important (SI) | (0.5, 0.7, 0.9) |

Very High (VH) | (7, 9, 10) | Extremely Important (EI) | (0.7, 0.9, 1) |

#### 4.1. Development of Fuzzified Mathematical Model for Order Allocation

- Assumptions

- ▪
- The model is a multi-period model.
- ▪
- The shipments are considered as less than container load (LCL) shipments.
- ▪
- The transfer cost and transfer time can only be applied at the nodes.
- ▪
- The custom clearance cost and time can only be applied while moving through port.
- ▪
- Custom clearance can only take place at one port for a shipment, i.e., either at seaport or dry port.

_{b}represents the value of the bth objective function, and Max

_{b}and Min

_{b}represent the maximum and minimum values of the bth objective function, respectively.

#### 4.2. Solving Algorithm for Order Allocation

_{2}, ε

_{3}, …, ε

_{n}are the right-hand side values for each objective function, S

_{2}, S

_{3}, …, S

_{n}are the slack variables, r

_{2}, r

_{3}, …, r

_{n}are the ranges of n objective functions, and eps$\in \left[{10}^{-6},{10}^{-3}\right].$

_{2}, f

_{3}, …, f

_{n}. The mathematical model is transformed as presented below to generate the Pareto solutions. For the purpose of this research work, TC has been considered as the main objective function.

#### 4.3. Selection of Best Pareto Solution

## 5. Application Case Study

#### 5.1. Sustainability and Resilience Criteria Weighting

#### 5.2. Sustainable and Resilient Supplier Ranking

#### 5.3. Sustainable and Resilient Order Allocation

_{2}, ε

_{3}, ε

_{4}, and ε

_{5}with the step interval of 2 by using Equation (30). These values have been presented in Table 9.

_{1}-t

_{4}have been included in Table 11. The best solution for each time period has been presented in Table 12.

## 6. Discussion

## 7. Managerial Insight

- (a)
- A multi-phase, multi-period smart decision support framework has been proposed for sustainable and resilient supplier selection and order allocation. The proposed framework has been demonstrated using real-time data collected from the pharmaceutical industry.
- (b)
- A combination of TBL sustainability and resilience criteria has been employed for supplier selection and order allocation that leads to a more comprehensive and thorough evaluation of the sustainable and resilient supplier selection and order allocation problem.
- (c)
- A detailed fuzzified mathematical model has been developed for order allocation. As demonstrated by the results presented in the preceding sections, this mathematical model can successfully handle real-life uncertainty of decision variables during supply-chain operations.

## 8. Conclusions and Future Recommendations

- (a)
- Among the TBL sustainability criteria, product price, past business, innovative capability, and information disclosure rank as the most significant sub-criteria for the DMs in the pharmaceutical industry.
- (b)
- Robustness and flexibility are considered the most valued attributes in the potential suppliers as far as the resilience criterion is concerned.
- (c)
- The transfer cost and custom clearance cost comprise 69.4% of the total cost of the supply chain network. On the other hand, transfer time and custom clearance time comprise only 24.7% of the total transportation time.
- (d)
- Transportation by sea has the least impact on environment (8.2%) while transportation by rail has the highest rate of environmental impact (62.5%) followed by transportation by road (29.5%).
- (e)
- Inland transportation of goods is dominated by rail as the most preferred mode of transport. Transportation by rail is also preferred by potential suppliers located in geographically contiguous countries.

## Supplementary Materials

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AUGMECON2 | Augmented ε-Constraint 2 |

CRITIC | Criteria Importance through Intercriteria Correlation |

DM | Decision Maker |

EI | Environmental Impact |

FE-AHP | Fuzzy Enhanced Analytical Hierarchy Process |

FMOMINLP | Fuzzy Multi-objective Mixed Integer Nonlinear Programming |

FST | Fuzzy Set Theory |

FTOPSIS | Fuzzy Technique for Order of Preference by Similarity to Ideal Solution |

LCL | Less than Container Load |

MCDM | Multi-criteria Decision Making |

MILP | Mixed-integer Linear Programming |

MINLP | Mixed-integer Nonlinear Programming |

MIP | Mixed-integer Programming |

MOILP | Multi-objective Integer Linear Programming |

MOLP | Multi-objective Linear Programming |

SS-OA | Supplier Selection and Order Allocation |

SSS-OA | Sustainable Supplier Selection and Order Allocation |

SRSS-OA | Sustainable and Resilient Supplier Selection and Order Allocation |

TC | Total Cost |

TTT | Total Travel Time |

TVSP | Total Value of Sustainable Purchasing |

TBL | Triple Bottom Line |

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Research Publication | Sustainability Criteria | Resilience Criteria | SS Technique(s) | OA Technique(s) | Application Case Study | ||
---|---|---|---|---|---|---|---|

Economic | Environment | Social | |||||

Rajesh and Ravi [57] | ✓ | GRA, AHP, ANP | Electronics devices manufacturing industry | ||||

Sen et al. [63] | ✓ | ✓ | TODIM | Numerical problem | |||

Amindoust [64] | ✓ | ✓ | ✓ | ✓ | FIS, AR (Assurance Region) DEA | Alloy manufacturing industry | |

Jabbarzadeh et al. [65] | ✓ | ✓ | ✓ | ✓ | Stochastic bi-objective optimization | Plastic goods manufacturing industry | |

Mohammed et al. [58] | ✓ | ✓ | ✓ | Fuzzy AHP, TOPSIS | Fuzzy multi-objective programming | Food supply chain | |

Hosseini et al. [10] | ✓ | ✓ | Stochastic bi-objective mixed-integer programming | Numerical problem | |||

Yavari and Zaker [59] | ✓ | ✓ | ✓ | Mixed-integer linear programming | Dairy supply chain | ||

Fallahpour et al. [66] | ✓ | ✓ | ✓ | ✓ | Fuzzy DEMATEL, Fuzzy ANP, Fuzzy BWM, FIS | Palm oil industry | |

Fazlollahtabar and Kazemitash [67] | ✓ | ✓ | ✓ | ✓ | Authors’ custom technique, DEA | Electrical equipment manufacturing industry | |

Mahmoudi et al. [68] | ✓ | ✓ | Fuzzy Ordinal Priority Approach (OPA) | Numerical problem | |||

This research work | ✓ | ✓ | ✓ | ✓ | Fuzzy AHP, Fuzzy TOPSIS | Fuzzy multi-objective mixed-integer nonlinear programming | Pharmaceutical industry |

Criteria | Global Weights | Sub-Criteria | Local Weights | Ranking |
---|---|---|---|---|

Economic | 0.66 | Product Price | 0.02 | 5 |

Payment Terms | 0.02 | 5 | ||

Product Quality | 0.03 | 4 | ||

Use of Technology | 0.03 | 4 | ||

Volume Flexibility | 0.07 | 3 | ||

Vendor’s Reputation | 0.07 | 3 | ||

Responsiveness | 0.12 | 2 | ||

Product Mix | 0.15 | 1 | ||

Past Business | 0.15 | 1 | ||

Environment | 0.08 | Environmental Management System | 0.01 | 3 |

Energy Consumption | 0.02 | 2 | ||

Waste Management System | 0.02 | 2 | ||

Innovative Capability | 0.03 | 1 | ||

Social | 0.16 | Employee Health & Safety | 0.01 | 3 |

Staff Personal & Technical Development | 0.02 | 2 | ||

Information Disclosure | 0.13 | 1 | ||

Resilience | 0.1 | Robustness | 0.03 | 1 |

Agility | 0.02 | 2 | ||

Leanness | 0.02 | 2 | ||

Flexibility | 0.03 | 1 |

Supplier | Economic Criteria | Environment Criteria | Social Criteria | Resilience Criteria | Overall Closeness Coefficient | Ranking |
---|---|---|---|---|---|---|

Supplier 1 | 0.69 | 0.12 | 0.36 | 0.15 | 0.68 | 1 |

Supplier 2 | 0.68 | 0.16 | 0.14 | 0.15 | 0.648 | 2 |

Supplier 3 | 0.64 | 0.23 | 0.54 | 0.92 | 0.567 | 3 |

Supplier 4 | 0.5 | 0.3 | 0.68 | 0.61 | 0.421 | 4 |

Supplier 5 | 0.2 | 0.87 | 0.86 | 0.42 | 0.268 | 5 |

Time Period | Objective Function | Ideal Solution |
---|---|---|

t_{1} | TC | $91,136,983.38 |

TTT | 621.6 h | |

EI | 750,915.35 g | |

AQL | 18,680.02 kg | |

TVSP | 117,241.22 | |

t_{2} | TC | $93,435,628.21 |

TTT | 645.3 h | |

EI | 892,574.29 g | |

AQL | 18,721.19 kg | |

TVSP | 116,272.51 | |

t_{3} | TC | $95,265,437.07 |

TTT | 634.2 h | |

EI | 957,297.42 g | |

AQL | 19,938.52 kg | |

TVSP | 117,252.96 | |

t_{4} | TC | $96,521,876.81 |

TTT | 655.7 h | |

EI | 869,374.61 g | |

AQL | 19,132.52 kg | |

TVSP | 119,935.22 |

Time Period | Objective Function | TC | TTT | EI | AQL | TVSP |
---|---|---|---|---|---|---|

t_{1} | TC | 92,804,282.45 | 823.33 | 1,755,576.8 | 18,475 | 105,930.56 |

TTT | 93,631,359.24 | 608.86 | 849,670.38 | 19,475 | 119,085.76 | |

EI | 93,631,359.24 | 608.86 | 849,670.38 | 19,475 | 119,085.76 | |

AQL | 93,631,340.95 | 608.85 | 849,652.81 | 19,475 | 119,085.76 | |

TVSP | 95,162,050.42 | 611.33 | 850,116.42 | 18,975 | 119,485.76 | |

t_{2} | TC | 91,254,980.23 | 797.25 | 1,564,291.6 | 18,356 | 143,597.22 |

TTT | 93,473,964.17 | 657.61 | 873,693.43 | 18,929 | 116,293.91 | |

EI | 93,912,579.05 | 629.43 | 865,298.66 | 19,574 | 116,369.32 | |

AQL | 93,624,579.66 | 602.43 | 869,764.34 | 19,578 | 116,297.49 | |

TVSP | 97,253,427.49 | 638.29 | 868,265.55 | 19,649 | 115,679.64 | |

t_{3} | TC | 92,673,456.22 | 643.53 | 1,427,941.3 | 19,246 | 152,457.87 |

TTT | 95,876,521.09 | 687.47 | 942,654.39 | 19,925 | 117,562.82 | |

EI | 97,648,761.22 | 678.32 | 956,482.21 | 19,643 | 117,465.97 | |

AQL | 95,790,352.62 | 654.91 | 954,278.23 | 19,587 | 117,790.11 | |

TVSP | 96,542,752.79 | 667.01 | 957,267.25 | 19,647 | 117,025.09 | |

t_{4} | TC | 97,825,790.25 | 686.38 | 1,374,825.2 | 19,835 | 137,923.34 |

TTT | 96,257,860.29 | 652.71 | 868,734.56 | 19,897 | 116,432.66 | |

EI | 96,843,789.34 | 652.82 | 873,484.43 | 19,642 | 119,843.21 | |

AQL | 96,894,392.55 | 655.72 | 862,564.52 | 19,528 | 119,532.62 | |

TVSP | 96,654,872.46 | 679.31 | 865,782.37 | 19,874 | 119,376.01 |

Objective Function | t_{1} | t_{2} | t_{3} | t_{4} | ||||
---|---|---|---|---|---|---|---|---|

Max | Min | Max | Min | Max | Min | Max | Min | |

TC | 95,162,050.42 | 92,804,282.45 | 97,253,427.49 | 91,254,980.23 | 9,7648,761.22 | 92,673,456.22 | 97,825,790.25 | 962,57,860.29 |

TTT | 823.33 | 608.85 | 797.25 | 602.43 | 687.47 | 643.53 | 686.38 | 652.71 |

EI | 1,755,576.8 | 849,652.81 | 1,564,291.6 | 865,298.66 | 1,427,941.3 | 942,654.39 | 1,374,825.2 | 862,564.52 |

AQL | 19,475 | 18475 | 19,649 | 18,356 | 19,647 | 19,246 | 19,897 | 19,528 |

TVSP | 119,485.76 | 105,930.56 | 143,597.22 | 116,293.91 | 152,457.87 | 117,025.09 | 137,923.34 | 116,432.66 |

Time Period | ε-Values | ||||
---|---|---|---|---|---|

ε_{2} | ε_{3} | ε_{4} | ε_{5} | ||

1 | t_{1} | 608.85 | 849,852.81 | 18,475 | 105,930.56 |

t_{2} | 716.09 | 849,652.71 | 18,425 | 109,831.37 | |

t_{3} | 823.33 | 849,652.21 | 18,375 | 110,718.26 | |

t_{4} | 608.85 | 1,302,614.8 | 18,475 | 115,701.11 | |

2 | t_{1} | 608.85 | 1,302,614.8 | 18,375 | 105,930.16 |

t_{2} | 716.09 | 849,652.81 | 19,575 | 108,921.84 | |

t_{3} | 823.33 | 849,652.81 | 17,965 | 112,222.01 | |

t_{4} | 608.85 | 849,752.81 | 17,985 | 117,728.19 | |

3 | t_{1} | 608.85 | 1,302,614.8 | 18,355 | 105,930.06 |

t_{2} | 716.09 | 859,252.81 | 18,925 | 111,392.11 | |

t_{3} | 823.33 | 889,652.61 | 18,765 | 112,421.17 | |

t_{4} | 608.85 | 819,652.01 | 19,971 | 116,133.15 | |

4 | t_{1} | 608.85 | 859,652.81 | 19,975 | 105,930.06 |

t_{2} | 823.33 | 846,652.91 | 19,673 | 111,929.72 | |

t_{3} | 608.85 | 849,752.81 | 18,455 | 114,441.06 | |

t_{4} | 823.33 | 1,302,614.8 | 19,415 | 115,792.19 | |

5 | t_{1} | 823.33 | 1,302,614.8 | 19,815 | 105,930.72 |

t_{2} | 608.85 | 849,652.33 | 18,415 | 109,431.82 | |

t_{3} | 823.33 | 847,652.61 | 19,121 | 112,416.15 | |

t_{4} | 608.85 | 845,652.09 | 18,415 | 115,719.82 | |

6 | t_{1} | 608.85 | 849,652.81 | 19,415 | 105,930.28 |

t_{2} | 608.85 | 849,752.91 | 19,411 | 106,931.65 | |

t_{3} | 716.09 | 848,652.29 | 18,471 | 114,563.12 | |

t_{4} | 823.33 | 849,752.81 | 19,471 | 115,708.11 | |

7 | t_{1} | 608.85 | 889,652.86 | 19,915 | 105,930.52 |

t_{2} | 716.09 | 848,652.81 | 19,915 | 105,930.86 | |

t_{3} | 823.33 | 889,652.88 | 19,915 | 112,518.15 | |

t_{4} | 608.85 | 1,302,614.8 | 19,915 | 114,401.26 | |

8 | t_{1} | 823.33 | 1,755,576.8 | 18,915 | 105,930.26 |

t_{2} | 608.85 | 849,652.86 | 19,975 | 107,531.52 | |

t_{3} | 823.33 | 848,652.81 | 19,915 | 114,478.12 | |

t_{4} | 608.85 | 889,652.97 | 19,414 | 117,719.11 | |

9 | t_{1} | 823.33 | 849,672.81 | 18,425 | 105,930.23 |

t_{2} | 608.85 | 848,652.91 | 18,415 | 104,948.35 | |

t_{3} | 823.33 | 859,652.88 | 19,371 | 114,545.11 | |

t_{4} | 608.85 | 1,755,576.8 | 19,275 | 118,715.16 | |

10 | t_{1} | 823.33 | 1,755,576.8 | 18,415 | 105,930.15 |

t_{2} | 608.85 | 849,652.86 | 18,585 | 110,943.54 | |

t_{3} | 823.33 | 848,652.81 | 19,471 | 111,927.14 | |

t_{4} | 608.85 | 839,672.69 | 19,475 | 117,934.29 |

Objective Function | Weight | |||
---|---|---|---|---|

t_{1} | t_{2} | t_{3} | t_{4} | |

TC | 0.21 | 0.21 | 0.2 | 0.25 |

TTT | 0.1 | 0.1 | 0.15 | 0.15 |

EI | 0.1 | 0.1 | 0.17 | 0.12 |

AQL | 0.25 | 0.22 | 0.2 | 0.18 |

TVSP | 0.34 | 0.37 | 0.28 | 0.3 |

**Table 11.**Relative closeness coefficient (CC) matrix for Pareto solutions of AUGMECON2 for t

_{1}–t

_{4}.

Time Period | |||||
---|---|---|---|---|---|

t_{1} | t_{2} | t_{3} | t_{4} | ||

CC | 1 | 0.944 | 0.742 | 0.961 | 0.958 |

2 | 0.885 | 0.692 | 0.838 | 0.974 | |

3 | 0.851 | 0.852 | 0.659 | 0.862 | |

4 | 0.788 | 0.952 | 0.271 | 0.681 |

CC | TC | TTT | EI | AQL | TVSP | |
---|---|---|---|---|---|---|

t_{1} | 0.944 | 93,224,195.91 | 736.22 | 849,670.38 | 18,564 | 108,357.88 |

t_{2} | 0.952 | 94,678,327.81 | 699.13 | 887,235.25 | 19,741 | 112,319.02 |

t_{3} | 0.961 | 92,675,287.52 | 647.02 | 957,162.45 | 19,623 | 115,791.21 |

t_{4} | 0.974 | 96,589,852.26 | 691.72 | 878,519.31 | 19,738 | 117,301.06 |

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## Share and Cite

**MDPI and ACS Style**

Kayani, S.A.; Warsi, S.S.; Liaqait, R.A.
A Smart Decision Support Framework for Sustainable and Resilient Supplier Selection and Order Allocation in the Pharmaceutical Industry. *Sustainability* **2023**, *15*, 5962.
https://doi.org/10.3390/su15075962

**AMA Style**

Kayani SA, Warsi SS, Liaqait RA.
A Smart Decision Support Framework for Sustainable and Resilient Supplier Selection and Order Allocation in the Pharmaceutical Industry. *Sustainability*. 2023; 15(7):5962.
https://doi.org/10.3390/su15075962

**Chicago/Turabian Style**

Kayani, Saheeb Ahmed, Salman Sagheer Warsi, and Raja Awais Liaqait.
2023. "A Smart Decision Support Framework for Sustainable and Resilient Supplier Selection and Order Allocation in the Pharmaceutical Industry" *Sustainability* 15, no. 7: 5962.
https://doi.org/10.3390/su15075962