# Multicriteria Optimization of Logistics Processes Using a Grey FUCOM-SWOT Model

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Preliminaries

#### 3.1. Grey Set Theory

#### 3.2. Grey Number and Its Extension

_{ϒ}, b

_{ϒ}], (a

_{ϒ}≤ b

_{ϒ}) representation, and has upper and lower boundaries, such as a number of grey numbers. One of the important concepts used in transferring the grey number to a real number is the whitening weight function, which is a quantitative method used to describe the interval grey number ⊗ϒ in comparison to different values of the preference degree in the range of [a

_{ϒ}, b

_{ϒ}]. The set ϒ, as a number set, can be the aggregation of a set of continuous intervals, a set of discrete values, or a mixture of both continuous intervals and discrete values. In particular, if the value set is represented by just one continuous set with upper and lower boundaries, the corresponding grey number is defined as an interval grey number.

**Definition**

**1**

**.**A grey number, denoted as ⊗ϒ, is defined as a number with an unknown exact value, but the range within which the value lies is known. The interval grey number is a grey number with known upper, $\overline{\mathsf{\Upsilon}}$, and the lower, $\underset{\xaf}{\mathsf{\Upsilon}}$, limits, but the unknown distribution information for $\mathsf{\Upsilon}$ is defined as:

**Definition**

**2**

**.**The length of the grey number is the distance between the bounds of an interval grey number; i.e., $l\left(\otimes \mathsf{\Upsilon}\right)=\underset{\xaf}{\mathsf{\Upsilon}}-\overline{\mathsf{\Upsilon}}$. When the length of an interval grey number increases and the upper and lower limit tend toward infinity,$\underset{\xaf}{\mathsf{\Upsilon}}\to \infty $and$\overline{\mathsf{\Upsilon}}\to \infty $, then the interval grey number tends toward the so-called black number. In contrast to this, when the length decreases, then the interval grey number tends to become a white number. Finally, when the upper and the lower limits are equal,$\underset{\xaf}{\mathsf{\Upsilon}}=\overline{\mathsf{\Upsilon}}$, such an interval grey number becomes a white (crisp) number.

**Definition**

**3**

**.**The whitened value of an interval grey number (${\mathsf{\Upsilon}}_{\lambda}$) is a crisp number with possible values lying between the upper and the lower limits of the interval grey number, $\otimes \mathsf{\Upsilon}$. For the given interval grey number, $\otimes \mathsf{\Upsilon}\in \left[\underset{\xaf}{\mathsf{\Upsilon}},\overline{\mathsf{\Upsilon}}\right]$, the corresponding whitened value, ${\mathsf{\Upsilon}}_{\lambda}$, is determined as ${\mathsf{\Upsilon}}_{\lambda}=\left(1-\lambda \right)\underset{\xaf}{\mathsf{\Upsilon}}+\lambda \overline{\mathsf{\Upsilon}}$, where $\lambda $ refers to the whitening coefficient and is in the interval $\lambda \in [0,1]$. For the special case, when $\lambda =0.5$, the whitened value becomes the mean of the interval grey number, ${\mathsf{\Upsilon}}_{0.5}=(\underset{\xaf}{\mathsf{\Upsilon}}+\overline{\mathsf{\Upsilon}})/2$.

**Example**

**1.**

## 4. Grey Hamy Mean Operators and Their Operations

**Definition**

**4**

**.**Assume that ${x}_{i}(i=1,2,\dots ,n)$represents a set of non-negative real numbers and a parameter$k=1,2,\dots ,n$, then HM is defined as:

**Definition**

**5.**

**Theorem**

**1.**

## 5. Grey Full-Consistency Method (FUCOM-G)

## 6. Multicriteria Optimization of Logistics Processes: Grey SWOT-FUCOM Model

#### 6.1. SWOT Analysis

#### 6.2. FUCOM-G: Evaluation of SWOT Factors

#### 6.2.1. Level I of the SWOT Matrix: Defining the Weights of the Main Dimensions

#### 6.2.2. Level II of the SWOT Matrix: Defining the Weights of S, W, O, and T Factors

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

Symbol | Meaning |
---|---|

$H{M}^{(k)}$ | Hamy operator |

$GNH{M}^{(k)}$ | Grey number Hamy operator |

$\otimes {\mathsf{\Upsilon}}_{i}$ | Grey number |

m | Number of experts |

$C$ | Set of criteria |

$\otimes {\mathsf{\varpi}}_{{C}_{j(k)}}^{e}$ | Importance of criterion j on kth rank |

$\otimes {\mathsf{\phi}}_{k/(k+1)}^{(e)}$ | Grey comparative importance of criteria on kth rank determined by eth expert |

${\Phi}^{(e)}$ | Vector of comparative importance determined by eth expert |

$\otimes {w}_{j}$ | Grey weight coefficients of criteria |

$\chi $ | A deviation from full consistency |

S_{j} | Strength factors |

W_{j} | Weaknesses factors |

O_{j} | Opportunities factors |

T_{j} | Threats factors |

## Appendix B

**Proof.**

## References

- Stević, Ž.; Stjepanović, Ž.; Božičković, Z.; Das, D.K.; Stanujkić, D. Assessment of Conditions for Implementing Information Technology in a Warehouse System: A Novel Fuzzy Piprecia Method. Symmetry
**2018**, 10, 586. [Google Scholar] [CrossRef] [Green Version] - Pupavac, D. Warehouse Logistics Management. Bus. Logist. Mod. Manag.
**2012**, 12, 87–98. [Google Scholar] - Stević, Ž.; Vasiljević, M.; Puška, A.; Tanackov, I.; Junevičius, R.; Vesković, S. Evaluation Of Suppliers under Uncertainty: A Multiphase Approach Based on Fuzzy Ahp and Fuzzy Edas. Transport
**2019**, 34, 52–66. [Google Scholar] [CrossRef] [Green Version] - Kosanke, L.; Schultz, M. Key Performance Indicators for Performance-Based Airport Management from the perspective of airport operations. In Proceedings of the CATO, Delft, The Netherlands, 20–23 July 2015. [Google Scholar]
- Rauch, P. SWOT analyses and SWOT strategy formulation for forest owner cooperations in Austria. Eur. J. For. Res.
**2007**, 126, 413–420. [Google Scholar] [CrossRef] - Chae, B. Developing key performance indicators for supply chain: An industry perspective. Supply Chain Manag. Int. J.
**2009**, 14, 422–428. [Google Scholar] [CrossRef] - Đalić, I.; Stević, Ž.; Ateljević, J.; Turskis, Z.; Zavadskas, E.K.; Mardani, A. A Novel Integrated Mcdm-Swot-Tows Model for The Strategic Decision Analysis In Transportation Company. Facta Univ. Ser. Mech. Eng.
**2021**, 19, 401–422. [Google Scholar] [CrossRef] - Kurttila, M.; Pesonen, M.; Kangas, J.; Kajanus, M. Utilizing the analytic hierarchy process AHP in SWOT analysis—A hybrid method and its application to a forest-certification case. For. Policy Econ.
**2000**, 1, 41–52. [Google Scholar] [CrossRef] - Chang, H.-H.; Huang, W.-C. Application of a quantification SWOT analytical method. Math. Comput. Model.
**2006**, 43, 158–169. [Google Scholar] [CrossRef] - Ananda, J.; Herath, G. The use of Analytic Hierarchy Process to incorporate stakeholder preferences into regional forest planning. For. Policy Econ.
**2003**, 5, 13–26. [Google Scholar] [CrossRef] - Moslem, S.; Farooq, D.; Ghorbanzadeh, O.; Blaschke, T. Application of the AHP-BWM Model for Evaluating Driver Behavior Factors Related to Road Safety: A Case Study for Budapest. Symmetry
**2020**, 12, 243. [Google Scholar] [CrossRef] [Green Version] - Moslem, S.; Çelikbilek, Y. An integrated grey AHP-MOORA model for ameliorating public transport service quality. Eur. Transp. Res. Rev.
**2020**, 12, 68. [Google Scholar] [CrossRef] - Duleba, S.; Çelikbilek, Y.; Moslem, S.; Esztergár-Kiss, D. Application of grey analytic hierarchy process to estimate mode choice alternatives: A case study from Budapest. Transp. Res. Interdiscip. Perspect.
**2022**, 13, 100560. [Google Scholar] [CrossRef] - Gündoğdu, F.K.; Duleba, S.; Moslem, S.; Aydın, S. Evaluating public transport service quality using picture fuzzy analytic hierarchy process and linear assignment model. Appl. Soft Comput.
**2021**, 100, 106920. [Google Scholar] [CrossRef] - Ho, W. Integrated analytic hierarchy process and its applications–A literature review. Eur. J. Oper. Res.
**2008**, 186, 211–228. [Google Scholar] [CrossRef] - Leskinen, L.A.; Leskinen, P.; Kurttila, M.; Kangas, J.; Kajanus, M. Adapting modern strategic decision support tools in the participatory strategy process—A case study of a forest research station. For. Policy Econ.
**2006**, 8, 267–278. [Google Scholar] [CrossRef] - Taleai, M.; Mansourian, A.; Sharifi, A. Surveying general prospects and challenges of GIS implementation in developing countries: A SWOT–AHP approach. J. Geogr. Syst.
**2009**, 11, 291–310. [Google Scholar] [CrossRef] - Masozera, M.K.; Alavalapati, J.R.R.; Jacobson, S.K.; Shresta, R.K. Assessing the suitability of community-based management for the Nyungwe Forest Reserve, Rwanda. For. Policy Econ.
**2006**, 8, 206–216. [Google Scholar] [CrossRef] - Stewart, R.A.; Mohamed, S.; Daet, R. Strategic implementation of IT/IS projects in construction: A case study. Autom. Constr.
**2002**, 11, 681–694. [Google Scholar] [CrossRef] [Green Version] - Shresthaa, R.K.; Alavalapati, R.R.; Kalmbacher, R.S. Exploring the potential for silvopasture adoption in South-Central Florida: An application of SWOT–AHP method. Agric. Syst.
**2004**, 81, 185–199. [Google Scholar] [CrossRef] - Pamučar, D.; Ćirović, G.; Sekulović, D. Development of an integrated transport system in distribution centres: A FA’WOT analysis. Teh. Vjesn.
**2015**, 22, 649–658. [Google Scholar] [CrossRef] [Green Version] - Al Mustafa, S.A.; Khan, M.; Hussain, M. Implementing Barcode Medication Administration Systems in Public Sector Medical Units. Int. J. Decis. Support Syst. Technol.
**2018**, 10, 23–39. [Google Scholar] [CrossRef] [Green Version] - Alharthi, H.; Sultana, N.; Al-Amoudi, A.; Basudan, A. An Analytic Hierarchy Process-based Method to Rank the Critical Success Factors of Implementing a Pharmacy Barcode System. Perspect. Health Inf. Manag.
**2015**, 12, 1g. [Google Scholar] - Nathnail, E.; Gogas, M.; Adamos, G. Urban Freight Terminals: A Sustainability Cross-case Analysis. Transp. Res. Procedia
**2016**, 16, 394–402. [Google Scholar] [CrossRef] [Green Version] - Stoilova, S.; Kunchev, L. Study of criteria for evaluation of transportation with intermodal transport. In Proceedings of the 16th International Scientific Conference Engineering for Rural Development, Jelgava, Latvia, 24–26 May 2017; pp. 349–357. [Google Scholar]
- Carlucci, D. Evaluating and selecting key performance indicators: An ANP-based model. Meas. Bus. Excel.
**2010**, 14, 66–76. [Google Scholar] [CrossRef] - Alvandi, M.; Fazli, S.; Yazdani, L.; Aghaee, M. An Integrated MCDM Method in Ranking BSC Perspectives and key Performance Indicators (KPIs). Manag. Sci. Lett.
**2012**, 2, 995–1004. [Google Scholar] [CrossRef] - Mladenovic, G.; Vajdic, N.; Wündsch, B.; Salaj, A.T. Use of key performance indicators for PPP transport projects to meet stakeholders’ performance objectives. Built Environ. Proj. Asset Manag.
**2013**, 3, 228–249. [Google Scholar] [CrossRef] - Podgórski, D. Measuring operational performance of OSH management system–A demonstration of AHP-based selection of leading key performance indicators. Saf. Sci.
**2015**, 73, 146–166. [Google Scholar] [CrossRef] [Green Version] - Sénquiz-Díaz, C. The Effect of Transport and Logistics on Trade Facilitation and Trade: A PLS-SEM Approach. Economics
**2021**, 9, 11–24. [Google Scholar] [CrossRef] - Durmić, E.; Stević, Ž.; Chatterjee, P.; Vasiljević, M.; Tomašević, M. Sustainable supplier selection using combined FUCOM–Rough SAW model. Rep. Mech. Eng.
**2020**, 1, 34–43. [Google Scholar] [CrossRef] - Solakivi, T.; Ojala, L.; Laari, S.; Lorentz, H.; Toyli, J.; Malmsten, J.; Viherlehto, N. Finland State of Logistics 2014; University of Turku: Turku, Finland, 2015. [Google Scholar]
- Roy, J.; Chatterjee, K.; Bandhopadhyay, A.; Kar, S. Evaluation and selection of Medical Tourism sites: A rough AHP based MABAC approach. arXiv
**2016**, arXiv:1606.08962. [Google Scholar] - Chattopadhyay, R.; Das, P.P.; Chakraborty, S. Development of a Rough-MABAC-DoE-based Metamodel for Supplier Selection in an Iron and Steel Industry. Oper. Res. Eng. Sci. Theory Appl.
**2022**. [Google Scholar] [CrossRef] - Jeon, C.M.; Amekudzi, A.A.; Guensler, R.L. Evaluating Plan Alternatives for Transportation System Sustainability: Atlanta Metropolitan Region. Int. J. Sustain. Transp.
**2010**, 4, 227–247. [Google Scholar] [CrossRef] - Cadena, P.C.B.; Magro, J.M.V. Setting the Weights of Sustainability Criteria for the Appraisal of Transport Projects. Transport
**2015**, 30, 298–306. [Google Scholar] [CrossRef] [Green Version] - Sremac, S.; Stević, Ž.; Pamučar, D.; Arsić, M.; Matić, B. Evaluation of a Third-Party Logistics (3PL) Provider Using a Rough SWARA–WASPAS Model Based on a New Rough Dombi Agregator. Symmetry
**2018**, 10, 305. [Google Scholar] [CrossRef] [Green Version] - Badi, I.; Abdulshahed, A.M.; Shetwan, A.G. A Case Study of Supplier Selection for A Steelmaking Company in Libya by Using the Combinative Distance-Based Assessment (CODAS) Model. Decis. Mak. Appl. Manag. Eng.
**2018**, 1, 16–33. [Google Scholar] [CrossRef] - Stević, Ž.; Pamučar, D.; Zavadskas, E.K.; Ćirović, G.; Prentkovskis, O. The Selection of Wagons for the Internal Transport of a Logistics Company: A Novel Approach Based on Rough BWM and Rough SAW Methods. Symmetry
**2017**, 9, 264. [Google Scholar] [CrossRef] [Green Version] - Radović, D.; Stević, Ž.; Pamučar, D.; Zavadskas, E.K.; Badi, I.; Antuchevičiene, J.; Turskis, Z. Measuring Performance in Transportation Companies in Developing Countries: A Novel Rough ARAS Model. Symmetry
**2018**, 10, 434. [Google Scholar] [CrossRef] [Green Version] - Pamučar, D.; Sremac, S.; Stević, Ž.; Ćirović, G.; Tomić, D. New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods. Neural Comput. Appl.
**2019**, 31, 5045–5068. [Google Scholar] [CrossRef] - Pamučar, D.; Vasin, L.; Atanasković, P.; Miličić, M. Planning the City Logistics Terminal Location by Applying the Greenp-Median Model and Type-2 Neurofuzzy Network. Comput. Intell. Neurosci.
**2016**, 2016, 6972818. [Google Scholar] [CrossRef] [Green Version] - Akyuz, G.A.; Erkan, T.E. Supply chain performance measurement: A literature review. Int. J. Prod. Res.
**2009**, 48, 5137–5155. [Google Scholar] [CrossRef] - Wong, C.Y.; Karia, N. Explaining the competitive advantage of logistics service providers: A resource-based view approach. Int. J. Prod. Econ.
**2010**, 128, 51–67. [Google Scholar] [CrossRef] - Qureshi, M.; Kumar, D.; Kumar, P. An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers. Asia Pac. J. Mark. Logist.
**2008**, 20, 227–249. [Google Scholar] [CrossRef] - Shaik, M.; Abdul-Kader, W. Performance measurement of reverse logistics enterprise: A comprehensive and integrated approach. Meas. Bus. Excel.
**2012**, 16, 23–34. [Google Scholar] [CrossRef] - Deng, J.L. Grey Control Systems; Press of Huazhong University of Science and Technology: Wuhan, China, 1985. [Google Scholar]
- Deng, J.L. Introduction to grey system theory. J. Grey Syst.
**1989**, 1, 1–24. [Google Scholar] - Liu, S.F. On Perron-Frobenius theorem of grey nonnegative matrix. J. Grey Syst.
**1989**, 1, 157–166. [Google Scholar] - Mardani, A.; Nilashi, M.; Zavadskas, E.K.; Awang, S.R.; Zare, H.; Jamal, N.M. Decision Making Methods Based on Fuzzy Aggregation Operators: Three Decades Review from 1986 to 2017. Int. J. Inf. Technol. Decis. Mak.
**2018**, 17, 391–466. [Google Scholar] [CrossRef] - Zadeh, L.A. Fuzzy sets. Inf. Control.
**1965**, 8, 338–353. [Google Scholar] [CrossRef] [Green Version] - Ali, Y.; Ahmad, M.; Sabir, M.; Shah, S.A. Regional development through energy infrastructure: A comparison and optimization of Iran-Pakistan-India (IPI) & Turkmenistan-Afghanistan-Pakistan-India (TAPI) gas pipelines. Oper. Res. Eng. Sci. Theory Appl.
**2021**, 4, 82–106. [Google Scholar] [CrossRef] - Gorcun, O.F.; Senthil, S.; Küçükönder, H. Evaluation of tanker vehicle selection using a novel hybrid fuzzy MCDM technique. Decis. Mak. Appl. Manag. Eng.
**2021**, 4, 140–162. [Google Scholar] [CrossRef] - Sharma, H.K.; Kumari, K.; Kar, S. Forecasting Sugarcane Yield of India based on rough set combination approach. Decis. Mak. Appl. Manag. Eng.
**2021**, 4, 163–177. [Google Scholar] [CrossRef] - Sharma, H.K.; Singh, A.; Yadav, D.; Kar, S. Criteria selection and decision making of hotels using Dominance Based Rough Set Theory. Oper. Res. Eng. Sci. Theory Appl.
**2022**. [Google Scholar] [CrossRef] - Hara, T.; Uchiyama, M.; Takahasi, S.E. A refinement of various mean inequalities. J. Inequal. Appl.
**1998**, 2, 387–395. [Google Scholar] [CrossRef] - Pamučar, D.; Stević, Ž.; Sremac, S. A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM). Symmetry
**2018**, 10, 393. [Google Scholar] [CrossRef] [Green Version] - Dâmbean, C.A.; Gabor, M.R. Implications of Emotional Intelligence in Human Resource Management. Econ. Innov. Econ. Res.
**2021**, 9, 73–90. [Google Scholar] [CrossRef] - Karamaşa, Ç.; Ergün, M.; Gülcan, B.; Korucuk, S.; Memiş, S.; Vojinović, D. Rankıng value-creatıng green approach practıces ın logıstıcs companıes operatıng ın the TR A1 regıon and choosıng ıdeal green marketıng strategy. Oper. Res. Eng. Sci. Theory Appl.
**2021**, 4, 21–38. [Google Scholar] [CrossRef] - Fosu, P. Does Railway Lines Investments Matter for Economic Growth? Econ. Innov. Econ. Res.
**2021**, 9, 11–24. [Google Scholar] [CrossRef] - Messinis, S.; Vosniakos, G. An agent-based Flexible Manufacturing System controller with Petri-net enabled algebraic deadlock avoidance. Rep. Mech. Eng.
**2020**, 1, 77–92. [Google Scholar] [CrossRef]

SWOT Factors | Weights | SWOT Factors | Weights | SWOT Factors | Weights |
---|---|---|---|---|---|

S1 | [0.160,0.225] | W3 | [0.204,0.435] | O3 | [0.155,0.376] |

S2 | [0.154,0.352] | W4 | [0.204,0.784] | O4 | [0.285,0.691] |

S3 | [0.312,0.853] | W5 | [0.109,0.359] | O5 | [0.116,0.135] |

S4 | [0.139,0.187] | W6 | [0.073,0.114] | O6 | [0.067,0.073] |

S5 | [0.095,0.095] | W7 | [0.045,0.079] | O7 | [0.106,0.106] |

S6 | [0.085,0.124] | W8 | [0.067,0.079] | T1 | [0.060,0.069] |

S7 | [0.055,0.069] | W9 | [0.038,0.055] | T2 | [0.062,0.125] |

W1 | [0.073,0.178] | O1 | [0.086,0.258] | T3 | [0.042,0.236] |

W2 | [0.109,0.286] | O2 | [0.185,0.428] | T4 | [0.061,0.579] |

Factor | w_{j} | Subfactor | Local Weights | Global Weights | Local Rank | Global Rank |
---|---|---|---|---|---|---|

Strengths | [0.254,0.266] | S1 | [0.16,0.225] | [0.041,0.060] | 3 | 12 |

S2 | [0.154,0.352] | [0.039,0.094] | 2 | 10 | ||

S3 | [0.312,0.853] | [0.079,0.227] | 1 | 1 | ||

S4 | [0.139,0.187] | [0.035,0.050] | 4 | 15 | ||

S5 | [0.095,0.095] | [0.024,0.025] | 6 | 22 | ||

S6 | [0.085,0.124] | [0.022,0.033] | 5 | 21 | ||

S7 | [0.055,0.069] | [0.014,0.018] | 7 | 26 | ||

Weaknesses | [0.311,0.576] | W1 | [0.073,0.178] | [0.023,0.103] | 5 | 11 |

W2 | [0.109,0.286] | [0.034,0.165] | 4 | 6 | ||

W3 | [0.204,0.435] | [0.064,0.251] | 2 | 2 | ||

W4 | [0.204,0.784] | [0.064,0.452] | 1 | 3 | ||

W5 | [0.109,0.359] | [0.034,0.207] | 3 | 5 | ||

W6 | [0.073,0.114] | [0.023,0.065] | 6 | 14 | ||

W7 | [0.045,0.079] | [0.014,0.046] | 8 | 19 | ||

W8 | [0.067,0.079] | [0.021,0.046] | 7 | 18 | ||

W9 | [0.038,0.055] | [0.012,0.032] | 9 | 24 | ||

Opportunities | [0.256,0.292] | O1 | [0.086,0.258] | [0.022,0.075] | 4 | 13 |

O2 | [0.185,0.428] | [0.047,0.125] | 3 | 7 | ||

O3 | [0.155,0.376] | [0.040,0.110] | 2 | 9 | ||

O4 | [0.285,0.691] | [0.073,0.202] | 1 | 4 | ||

O5 | [0.116,0.135] | [0.030,0.039] | 5 | 17 | ||

O6 | [0.067,0.073] | [0.017,0.021] | 7 | 25 | ||

O7 | [0.106,0.106] | [0.027,0.031] | 6 | 20 | ||

Threats | [0.179,0.264] | T1 | [0.06,0.069] | [0.011,0.018] | 4 | 27 |

T2 | [0.062,0.125] | [0.011,0.033] | 3 | 23 | ||

T3 | [0.042,0.236] | [0.007,0.062] | 2 | 16 | ||

T4 | [0.061,0.579] | [0.011,0.153] | 1 | 8 |

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**MDPI and ACS Style**

Popović, V.; Pamučar, D.; Stević, Ž.; Lukovac, V.; Jovković, S.
Multicriteria Optimization of Logistics Processes Using a Grey FUCOM-SWOT Model. *Symmetry* **2022**, *14*, 794.
https://doi.org/10.3390/sym14040794

**AMA Style**

Popović V, Pamučar D, Stević Ž, Lukovac V, Jovković S.
Multicriteria Optimization of Logistics Processes Using a Grey FUCOM-SWOT Model. *Symmetry*. 2022; 14(4):794.
https://doi.org/10.3390/sym14040794

**Chicago/Turabian Style**

Popović, Vladimir, Dragan Pamučar, Željko Stević, Vesko Lukovac, and Srđan Jovković.
2022. "Multicriteria Optimization of Logistics Processes Using a Grey FUCOM-SWOT Model" *Symmetry* 14, no. 4: 794.
https://doi.org/10.3390/sym14040794