A Compromised Decision-Making Approach to Third-Party Logistics Selection in Sustainable Supply Chain Using Fuzzy AHP and Fuzzy VIKOR Methods

With the effects of the COVID-19 pandemic, the e-commerce trend is driving faster, significantly impacting supply chains around the world. Thus, the importance of logistics and supply chain functions has been amplified in almost every business that ships physical goods. In Vietnam, the logistics service sector has seen rapid expansion. Since more and more businesses are seeking third-party logistics (3PL) providers to outsource the logistics functions, this article aims to offer decision-makers an integrated and consistent model for evaluating and selecting the most efficient 3PLs. To this end, the authors exploit a hybrid multi-criteria method which is fuzzy analytic hierarchy process (FAHP) and fuzzy vlsekriterijumska optimizacija i kompromisno resenje (FVIKOR) while examining the most influential and conflicting criteria regarding economic, service level, environmental, social, and risk aspects. Fuzzy information in the natural decision-making process is considered, linguistic variables are used to mitigate the uncertain levels in the criteria weights. First, FAHP (the weighting method) is adopted to evaluate and calculate each criterion’s relative significant fuzzy weight. FVIKOR (the compromised ranking method) is then used to rank the alternatives. The combination of FAHP and FVIKOR methods provides more accurate ranking results. As a result, reliability and delivery time, voice of customer, logistics cost, network management, and quality of service are the most impactful factors to the logistics outsourcing problem. Eventually, the optimized 3PLs were determined that fully meet the criteria of sustainable development. The developed integrated model offers the complete and robust 3PLs evaluation and selection process and can also be a powerful decision support tool for other industries.

[1]  Valério Antonio Pamplona Salomon,et al.  ANP Applied to the Evaluation of Performance Indicators of Reverse Logistics in Footwear Industry , 2015, ITQM.

[2]  Kevin Cullinane,et al.  A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy , 2019, Expert Syst. Appl..

[3]  Gülçin Büyüközkan,et al.  Selection of the strategic alliance partner in logistics value chain , 2008 .

[4]  Chia-Nan Wang,et al.  Supporting Better Decision-Making: A Combined Grey Model and Data Envelopment Analysis for Efficiency Evaluation in E-Commerce Marketplaces , 2020, Sustainability.

[5]  M. Goh,et al.  Sustainable third-party reverse logistic provider selection with fuzzy SWARA and fuzzy MOORA in plastic industry , 2017 .

[6]  Tsung-Han Chang.  Fuzzy VIKOR method: A case study of the hospital service evaluation in Taiwan , 2014, Inf. Sci..

[7]  Tim Coltman,et al.  What Drives the Choice of Third Party Logistics Provider? , 2008 .

[8]  Alvydas Balezentis,et al.  Personnel selection based on computing with words and fuzzy MULTIMOORA , 2012, Expert Syst. Appl..

[9]  Ravi Shankar,et al.  Selection of logistics service provider: An analytic network process (ANP) approach , 2007 .

[10]  Patricia J. Daugherty,et al.  JIT Systems and External Logistics Suppliers , 1994 .

[11]  Artur Karczmarczyk,et al.  Generalised framework for multi-criteria method selection , 2018, Omega.

[12]  D. Chang.  Applications of the extent analysis method on fuzzy AHP , 1996 .

[13]  R. Singh,et al.  Outsourcing decisions in reverse logistics: Sustainable balanced scorecard and graph theoretic approach , 2016 .

[14]  Wojciech Salabun,et al.  Are MCDA Methods Benchmarkable? A Comparative Study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II Methods , 2020, Symmetry.

[15]  Ying-Chyi Chou,et al.  Assessing the Human Resource in Science and Technology for Asian Countries: Application of Fuzzy AHP and Fuzzy TOPSIS , 2019, Symmetry.

[16]  Wojciech Sałabun,et al.  Efficiency of Methods for Determining the Relevance of Criteria in Sustainable Transport Problems: A Comparative Case Study , 2020, Sustainability.

[17]  J. Buckley.  Ranking alternatives using fuzzy numbers , 1985 .

[18]  Chia-Nan Wang,et al.  Assessing Renewable Energy Production Capabilities Using DEA Window and Fuzzy TOPSIS Model , 2021, Symmetry.

[19]  Aicha Aguezzoul,et al.  Third-party logistics selection problem: A literature review on criteria and methods , 2014 .

[20]  Wang Ying,et al.  Multi-agent framework for third party logistics in E-commerce , 2005, Expert Syst. Appl..

[21]  Tugba Efendigil,et al.  A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness , 2008, Comput. Ind. Eng..

[22]  Wei-Kai Wang,et al.  An integrated fuzzy approach for provider evaluation and selection in third-party logistics , 2009, Expert Syst. Appl..

[23]  J. Sarkis.  Supply chain sustainability: learning from the COVID-19 pandemic , 2020, International Journal of Operations & Production Management.

[24]  S. Meysam Mousavi,et al.  An integrated weighting and ranking model based on entropy, DEA and PCA considering two aggregation approaches for resilient supplier selection problem , 2020, J. Comput. Sci..

[25]  Mustafa Batuhan Ayhan,et al.  A two stage approach for supplier selection problem in multi-item/multi-supplier environment with quantity discounts , 2015, Comput. Ind. Eng..

[26]  Chia-Chi Sun,et al.  A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods , 2010, Expert Syst. Appl..

[27]  Kannan Govindan,et al.  Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA , 2019, Omega.

[28]  Abit Balin,et al.  A real case study on the selection of suitable roll stabilizer type for motor yachts using hybrid fuzzy AHP and VIKOR methodology , 2020 .

[29]  Chia-Nan Wang,et al.  Outsourcing Reverse Logistics for E-Commerce Retailers: A Two-Stage Fuzzy Optimization Approach , 2021, Axioms.

[30]  Ahmad Hemmati,et al.  A truck and drones model for last-mile delivery: A mathematical model and heuristic approach , 2020 .

[31]  Benjamin P.-C. Yen,et al.  Coordinating operational policy with financial hedging for risk-averse firms , 2016 .

[32]  A. Rizzi,et al.  A fuzzy TOPSIS methodology to support outsourcing of logistics services , 2006 .

[33]  Ming-Chih Tsai,et al.  Demand choices of high-tech industry for logistics service providers—an empirical case of an offshore science park in Taiwan , 2007 .

[34]  Hokey Min,et al.  A hybrid quality function deployment and fuzzy decision-making methodology for the optimal selection of third-party logistics service providers , 2013 .

[35]  Daizhong Su,et al.  Integration of Web Services technology with business models within the total product design process for supplier selection , 2006, Comput. Ind..

[36]  Christina Sichtmann,et al.  The influence of ethical culture on supplier selection in the context of sustainable sourcing , 2012 .

[37]  Krzysztof Palczewski,et al.  The fuzzy TOPSIS applications in the last decade , 2019, KES.

[38]  Soroush Avakh Darestani,et al.  Green Logistics Outsourcing Employing Multi Criteria Decision Making and Quality Function Deployment in the Petrochemical Industry , 2019 .

[39]  Georgios Sermpinis,et al.  Operational risk: Emerging markets, sectors and measurement , 2015, Eur. J. Oper. Res..

[40]  B. Çatay,et al.  Third‐party logistics provider selection: insights from a Turkish automotive company , 2007 .

[41]  P R Murphy,et al.  DETERMINANTS OF SUCCESSFUL LOGISTICAL RELATIONSHIPS: A THIRD-PARTY PROVIDER PERSPECTIVE , 1995 .

[42]  Kim Hua Tan,et al.  Leveraging the supply chain flexibility of third party logistics - Hybrid knowledge-based system approach , 2008, Expert Syst. Appl..

[43]  A. Noorul Haq,et al.  Integration of closed loop distribution supply chain network and 3PRLP selection for the case of battery recycling , 2011 .

[44]  James J. H. Liou,et al.  Developing a hybrid multi-criteria model for selection of outsourcing providers , 2010, Expert Syst. Appl..

[45]  Mohammad-Bagher Jamali,et al.  A game theoretic approach to investigate the effects of third-party logistics in a sustainable supply chain by reducing delivery time and carbon emissions , 2019, Journal of Cleaner Production.

[46]  Hadi Ghaderi,et al.  Sustainable third-party reverse logistics provider evaluation and selection using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria , 2018, Appl. Soft Comput..

[47]  Manoj Kumar Tiwari,et al.  Novel fuzzy hybrid multi-criteria group decision making approaches for the strategic supplier selection problem , 2015, Expert Syst. Appl..

[48]  Wojciech Salabun,et al.  A comparative case study of the VIKOR and TOPSIS rankings similarity , 2020, KES.

[49]  Fumin Deng,et al.  PCA-DEA-tobit regression assessment with carbon emission constraints of China’s logistics industry , 2020, Journal of Cleaner Production.

[50]  Wei Yang,et al.  An MAGDM based on constrained FAHP and FTOPSIS and its application to supplier selection , 2011, Math. Comput. Model..

[51]  Shaligram Pokharel,et al.  A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider , 2009 .

[52]  Edmundas Kazimieras Zavadskas,et al.  Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers , 2019, Comput. Ind. Eng..

[53]  Qing Wang,et al.  Dynamic organization model of automated negotiation for 3PL providers selection , 2020, Inf. Sci..

[54]  Chia-Nan Wang,et al.  Transportation Optimization Models for Intermodal Networks with Fuzzy Node Capacity, Detour Factor, and Vehicle Utilization Constraints , 2020 .

[55]  Ramesh Anbanandam,et al.  A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics , 2014, Expert Syst. Appl..

[56]  Ganesh Vaidyanathan,et al.  A framework for evaluating third-party logistics , 2005, CACM.

[57]  Jyoti Dhingra Darbari,et al.  Environmental management partner selection for reverse supply chain collaboration: A sustainable approach. , 2019, Journal of environmental management.

[58]  Ravinder Kumar,et al.  Supply Chain Management in SMEs: a case study , 2012, Int. J. Manuf. Res..

[59]  S. G. Deshmukh,et al.  Selection of Third-Party Logistics (3PL): A Hybrid Approach Using Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP) , 2005 .

[60]  Wojciech Sałabun,et al.  A New Coefficient of Rankings Similarity in Decision-Making Problems , 2020, ICCS.

[61]  Rakesh D. Raut,et al.  Sustainable evaluation and selection of potential third-party logistics (3PL) providers: An integrated MCDM approach , 2018 .

[62]  Shu-Ping Wan,et al.  An intuitionistic fuzzy linear programming method for logistics outsourcing provider selection , 2015, Knowl. Based Syst..

[63]  Gi-Tae Yeo,et al.  Weighing the Key Factors to Improve Vietnam's Logistics System , 2018, The Asian Journal of Shipping and Logistics.

[64]  Dragan Pamučar,et al.  Ranking and Assessment of the Efficiency of Social Media using the Fuzzy AHP-Z Number Model - Fuzzy MABAC , 2020 .