Article abstract

Journal of Economics and International Business Management

Research Article | Published April 2021 | Volume 9, Issue 1, pp. 1-19.

doi: https://doi.org/10.33495/jeibm_v9i1.20.134

 

Analysis of the Performance of Cargo Clearance Formalities Based on Fuzzy VIKOR Clustering Model: A Case of Dar es Salaam Seaport

 


 

 

Erick P. Massami*

Malima M. Manyasi

 

Email Author



 

Dar es Salaam Maritime Institute.



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Citation: Massami EP, Manyasi MM (2021). Analysis of the Performance of Cargo Clearance Formalities Based on Fuzzy VIKOR Clustering Model: A Case of Dar es Salaam Seaport. J. Econ. Int. Bus. Manage. 9(1): 1-19. doi: 10.33495/jeibm_v9i1.20.134.

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 Abstract 


The ability of a seaport to attract traffic in a highly competitive environment can be negatively affected by its inefficiency and ineffectiveness of cargo clearance formalities. This study analyses the performance of cargo clearance processes at Dar es Salaam Seaport. A questionnaire survey was conducted on a random and stratified sampling of key cargo clearance service providers in Dar es Salaam Port. The data is analysed by Fuzzy VIKOR Clustering Model (FVCM) which is a combination of Fuzzy Clustering Model (FCM) and Fuzzy VIKOR Model (FVM). The results of the analysis reveal that the Customs Authority, Freight Clearing & Forwarding Companies, and Other Government Departments as the most effective agencies in business process management in the port. Moreover, Shipping Agents are the least effective agency in business process management in the port.

Keywords  Seaport   Cargo Clearance   Fuzzy Clustering Model   Fuzzy VIKOR Model   Analysis   

 

 

Copyright © 2021 Author(s) retain the copyright of this article.

This article is published under the terms of the Creative Commons Attribution License 4.0

 

 

 References 

 

Alguliyev RM, Alguliyev RM, Mahmudova RS (2015). Multi-criteria personnel selection by the Modified Fuzzy VIKOR Method. The Sci. World J. 2015:1-16.

Apostolos NG, Petros T, Dimitris Z (2013). Suppliers’ logistics service quality and its effect to retailers’ behaviors intentions. Procedia Soc. Behav. J. 2:48-52.

Chang TH (2014). Fuzzy VIKOR method: A case study of the hospital service evaluation in Taiwan. Inform. Sci. 271:196-212.

Chen SH (2011). Exploring digital capital of automated cargo clearance business websites. Expert Syst. Appl. 38(4):3590-3599.

Chen TY (2018). Remoteness index-based Pythagorean fuzzy VIKOR methods with a generalized distance measure for multiple criteria decision analysis. Inform. Fusion, 41:129-150.

Choi S, Kim S (2017). An investigation of operating behaviour characteristics of a wind power system using a fuzzy clustering method. Expert Syst. Appl. 81:244-250.

D’Urso P, Leski JM (In Press). Fuzzy Clustering of fuzzy data based on robust loss functions and ordered weighted averaging. Fuzzy Sets and Systems.

David H (2015). Weakness in the supply chain: who packed the box? World Customs J. pp. 11-21.

Dumas M, Rosa ML, Mendling J, Reijers HA (2013). Fundamentals of Business Process Management. Berlin: Springer-Verlag.

Elliott D, Bonsignori C (2019). The influence of customs capabilities and express delivery on trade flows. J. Air Transport Manage. 74: 54-71.

Giordani P, Ramos-Guajardo AB (2016). A fuzzy clustering procedure for random fuzzy sets. Fuzzy Sets Syst. 305:54-69.

Guillon A, Lesot M, Marsala C (2019). A proximal framework for fuzzy subspace clustering. Fuzzy Sets Syst. 366:34-45.

Gul M, Ak MF, Guneri AF (2019). Pythagorean fuzzy VIKOR-based approach for safety risk assessment in mine industry. J. Safety Res. 69:135-153.

Haleh H, Hamidi A (2011). A fuzzy MCDM model for allocating orders to suppliers in a supply chain under uncertainty over a multi-period time horizon. Expert Syst. Appl. 38(8):9076-9083.

Hatori T, Sato-Ilic M (2014). A Fuzzy clustering method using the relative structure of the belongingness of objects to clusters. Procedia Comput. Sci. 35:994-1002.

Ines K, Cedonue D, Jugovie A (2011). Customer based port service quality model. Prom Traffic and Transport. J. 25(6):495-502.

Kim Y, Chung ES (2013). Fuzzy VIKOR approach for assessing the vulnerability of the water supply to climate change and variability in South Korea. Appl. Math. Model. 37(22):9419-9430.

Li H, Gong M, Wang Q, Liu J, Su L (2016). A multi-objective fuzzy clustering method for change detection in SAR images. Appl. Soft Comput. 46:767-777.

Liang D, Zhang Y, Xu Z, Jamaldeen A (2019). Pythagorean fuzzy VIKOR approaches based on TODIM for evaluating internet banking website quality of Ghanaian banking industry. Appl. Soft Comput. 78: 583-594.

Liu HC, Liu L, Liu N, Mao LX (2012). Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Syst. Appl. 39(17):12926-12934.

Makiri MF (2013). The effect of crane allocation on ship turnaround time: empirical evidence from port of Dar es Salaam. Global J. Logist. Bus. Manage. Vol.13: 28-39.

Martincus CV, Carballo J, Graziano A (2015). Customs. J. Int. Econ. 96(1):119-137.

Massami EP, Myamba BM (2016). Fuzzy analysis and evaluation of supply chain performance: A focus on leather products in Tanzania. International J. Logistic Syst. Manage. 23(3):299-313.

Opricovic S (2011). Fuzzy VIKOR with an application to water resources planning. Expert Syst. Appl. 38(10):12983-12990.

Opricovic, S, Tzeng GH (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156(2):445-455.

Pak JY, Thai VV, Yeo GT (2015). Fuzzy MCDM approach for evaluating intangible resources affecting Port service quality. The Asian J. Shipping and Logistics. 31(4):459-468.

Panteial PWC (2014). Measures to enhance the efficiency and quality of port services in the Kenya Port. [Online]. Available: https:www.pwc.comsite (Retrieved on October 16, 2016).

Pourakbar M, Zuidwijk RA (2018). The role of customs in securing containerized global supply chains. Eur. J. Oper. Res. 271(1):331-340.

Raballand G, Refas S, Beuran M, Isik G (2012). Why does cargo spend weeks in Sub-Saharan African Ports? Lessons from six countries. [Online]. Available: https://unctad.org (Retrieved on January 02, 2020).

Rostamzadeh R, Govindan K, Esmaeili A, Sabaghi M (2015). Application of fuzzy VIKOR for evaluation of green supply chain management practices. Ecol. Indic. 49:188-203.

Tanzania Ports Authority (2020). A simple guide to clearance of cargo through the Port of Dar es Salaam, Tanzania. [Online]. Available: https:www.fifaandflowtrading.co.tz (Retrieved on January 02, 2020).

Tóth B, Vad J (2018). A fuzzy clustering method for periodic data, applied for processing turbomachinery beamforming maps. J. Sound Vib. 434:298-313.

Trabelsi M, Frigui H (2019). Robust fuzzy clustering for multiple instance regression. Pattern Recognition 90:424-435.

UNDP (2008). Shipping and Incoterms: Practice Guide. [Online]. Available: https://www.coursehero.com (Retrieved on January 02, 2020).

Vaghi C, Lucietti L (2016). Costs and benefits of speeding-up reporting formalities in maritime transport. Transport. Res. Procedia, 14:213-222.

Vahabzadeh AH, Asiaei A, Zailani S (2015). Reprint of Green decision-making model in reverse logistics using fuzzy VIKOR method. Resour. Conserv. Recycl. 104(Part B):334-347.

World Bank (2013). Tanzania Economic Update: Opening the Gates; how port of Dar es Salaam can transform Tanzania. New York: World Bank. Wu T, Zhou Y, Xiao Y, Needell D, Nie F (In Press). Modified fuzzy clustering with segregated cluster centroids. Neurocomputing.

Wu Z, Ahmad J, Xu J (2016). A group decision making framework based on fuzzy VIKOR approach for machine tool selection with linguistic information. Appl. Soft Comput. 42:314-324.

Yiming T, Xianghui H, Pedrycz W, Xiaocheng S (2019). Possibilistic fuzzy clustering with high-density viewpoint. Neurocomputing, 329:407-423.

Zarinbal M, Zarandi MHF, Turksen IB (2015). Relative entropy collaborative fuzzy clustering method. Pattern Recognition 48(3):933-940.

Zhiliang R, Zeshui X, Hai W (2017). Dual hesitant fuzzy VIKOR method for multi-criteria group decision making based on fuzzy measure and new comparison method. Inform. Sci. (388-389):1-16.