Picture Fuzzy ARAS Method for Freight Distribution Concept Selection

Companies can perform their freight distribution in three different ways. The first concept, the in-house concept, represents the use of a company’s own resources and knowledge to organize transportation from the production to retailers or from the warehouse to customers. The opposite concept is to outsource distribution activities by hiring third-party logistics providers. The third concept represents a combination of the previous two. Although the arguments in favor of outsourcing can be found in the literature, an appropriate selection of a freight distribution concept is specific for each company and depends on many evaluation criteria and their symmetrical roles. This paper presents a methodology that can be used by companies that need to choose their freight distribution concept. An advanced extension of the Additive Ratio ASsessment (ARAS) method is developed to solve the freight distribution concept selection problem. To illustrate the implementation of the proposed methodology, a tire manufacturing company from the Czech Republic is taken as a case study. However, the proposed picture fuzzy ARAS method is general and can be used by any other company. To validate the novel picture fuzzy ARAS method, a comparative analysis with the nine existing state-of-the-art picture fuzzy multi-criteria decision-making methods is provided.

[1]  Edmundas Kazimieras Zavadskas,et al.  Multiple Criteria Decision Support System for Assessment of Projects Managers in Construction , 2012, Int. J. Inf. Technol. Decis. Mak..

[2]  Fuad E. Alsaadi,et al.  Projection models for multiple attribute decision making with picture fuzzy information , 2016, International Journal of Machine Learning and Cybernetics.

[3]  K. Khalili-Damghani,et al.  A novel hybrid MCDM approach for outsourcing supplier selection , 2016 .

[4]  Zenonas Turskis,et al.  An integrated model for extending brand based on fuzzy ARAS and ANP methods , 2014 .

[5]  Edmundas Kazimieras Zavadskas,et al.  A Novel Approach for Evaluation of Projects Using an Interval-Valued Fuzzy Additive Ratio Assessment (ARAS) Method: A Case Study of Oil and Gas Well Drilling Projects , 2018, Symmetry.

[6]  Edmundas Kazimieras Zavadskas,et al.  A Model Based on Aras-G and AHP Methods for Multiple Criteria Prioritizing of Heritage Value , 2013, Int. J. Inf. Technol. Decis. Mak..

[7]  O. I. Tukel,et al.  Outsourcing decision support: a survey of benefits, risks, and decision factors , 2006 .

[8]  Kant Rao,et al.  Global Supply Chains: Factors Influencing Outsourcing of Logistics Functions , 1994 .

[9]  Jozef Gašparík,et al.  CONTROL OF MODULAR CONVEYOR AND AUTOMATED HANDLING DEVICES INTERCONNECTION , 2018 .

[10]  Van Kien Pham,et al.  Evaluating and Selecting the Best Outsourcing Service Country in East and Southeast Asia: An AHP Approach , 2016 .

[11]  Hideki Aoyama,et al.  An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness , 2016, PloS one.

[12]  Hu Xu,et al.  Supplier selection in nuclear power industry with extended VIKOR method under linguistic information , 2016, Appl. Soft Comput..

[13]  T.C.E. Cheng,et al.  Third-party purchase: An empirical study of third-party logistics providers in China , 2016 .

[14]  Gülçin Büyüközkan,et al.  An extension of ARAS methodology under Interval Valued Intuitionistic Fuzzy environment for Digital Supply Chain , 2018, Appl. Soft Comput..

[15]  Jabir Arif,et al.  Outsourcing of Logistics’ Activities: Impact Analysis on Logistics Service Performance , 2018, 2018 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA).

[16]  Bui Cong Cuong,et al.  Picture fuzzy sets , 2015 .

[17]  Abbas Maghsoudi,et al.  BWM-ARAS: A new hybrid MCDM method for Cu prospectivity mapping in the Abhar area, NW Iran , 2019, Spatial Statistics.

[18]  Hu-Chen Liu,et al.  Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition in Considering Conflicting Opinions , 2019, Applied Sciences.

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

[20]  Fujun Lai,et al.  Disentangling the driving factors of logistics outsourcing: a configurational perspective , 2019, J. Enterp. Inf. Manag..

[21]  Le Wang,et al.  The differences in hotel selection among various types of travellers: A comparative analysis with a useful bounded rationality behavioural decision support model , 2020 .

[22]  Zenonas Turskis,et al.  Integrated Fuzzy Multiple Criteria Decision Making Model for Architect Selection , 2012 .

[23]  Nimet Yapici Pehlivan,et al.  Determination of individuals' life satisfaction levels living in Turkey by FMCDM methods , 2019, Kybernetes.

[24]  Taewon Hwang,et al.  Balancing in-house and outsourced logistics services: effects on supply chain agility and firm performance , 2018, Service Business.

[25]  P. Bajec,et al.  A Make-or-buy Decision Process for Outsourcing , 2010 .

[26]  E. Aktas,et al.  The use of outsourcing logistics activities: The case of turkey , 2011 .

[27]  J. Machuca,et al.  Determinants of success in transport services outsourcing: empirical study in Europe , 2018 .

[28]  Dejan Brcanov,et al.  The location of public logistic centers: an expanded capacity-limited fixed cost location-allocation modeling approach , 2013 .

[29]  L. Hendry,et al.  Sustainable procurement: comparing in-house and outsourcing implementation modes , 2020, Production Planning & Control.

[30]  Sean Handley The perilous effects of capability loss on outsourcing management and performance , 2012 .

[31]  Kannan Govindan,et al.  A novel hybrid multiple attribute decision-making approach for outsourcing sustainable reverse logistics , 2020, Journal of Cleaner Production.

[32]  Edmundas Kazimieras Zavadskas,et al.  Multiple criteria analysis of foundation instalment alternatives by applying Additive Ratio Assessment (ARAS) method , 2010 .

[33]  Guiwu Wei,et al.  Picture fuzzy cross-entropy for multiple attribute decision making problems , 2016 .

[34]  E. Zavadskas,et al.  A new fuzzy additive ratio assessment method (ARAS‐F). Case study: The analysis of fuzzy multiple criteria in order to select the logistic centers location , 2010 .

[35]  Cengiz Kahraman,et al.  An Integrated Intuitionistic Fuzzy AHP and TOPSIS Approach to Evaluation of Outsource Manufacturers , 2018, J. Intell. Syst..

[36]  A. Marasco Third-party logistics: A literature review , 2008 .

[37]  Tomi Solakivi,et al.  Logistics outsourcing, its motives and the level of logistics costs in manufacturing and trading companies operating in Finland , 2013 .

[38]  T. Baležentis,et al.  An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods , 2012 .

[39]  Guiwu Wei,et al.  TODIM Method for Picture Fuzzy Multiple Attribute Decision Making , 2018, Informatica.

[40]  George Alex Thopil,et al.  A framework for sustainable utility scale renewable energy selection in South Africa , 2019, Journal of Cleaner Production.

[41]  Chin-Nung Liao,et al.  Integrated FAHP, ARAS-F and MSGP methods for green supplier evaluation and selection , 2015 .

[42]  Wei-Zhang Liang,et al.  An Integrated EDAS-ELECTRE Method With Picture Fuzzy Information for Cleaner Production Evaluation in Gold Mines , 2018, IEEE Access.

[43]  Zenonas Turskis,et al.  A hybrid linguistic fuzzy multiple criteria group selection of a chief accounting officer , 2014 .

[44]  J. Liou,et al.  An outsourcing provider decision model for the airline industry , 2013 .

[45]  Zenonas Turskis,et al.  A Hybrid Fuzzy Group Multi-Criteria Assessment of Structural Solutions of the Symmetric Frame Alternatives , 2019, Symmetry.

[46]  Jalil Heidary Dahooie,et al.  Competency‐based IT personnel selection using a hybrid SWARA and ARAS‐G methodology , 2018 .

[47]  Maamar Bettayeb,et al.  Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method , 2020 .

[48]  Edmundas Kazimieras Zavadskas,et al.  Assessment of priority alternatives for preservation of historic buildings using model based on ARAS and AHP methods , 2014 .

[49]  Henrikas Sivilevičius,et al.  Multiple criteria selection of pile-column construction technology , 2012 .

[50]  Cun Wei,et al.  EDAS METHOD FOR MULTIPLE CRITERIA GROUP DECISION MAKING WITH PICTURE FUZZY INFORMATION AND ITS APPLICATION TO GREEN SUPPLIERS SELECTIONS , 2019, Technological and Economic Development of Economy.

[51]  Edmundas Kazimieras Zavadskas,et al.  Multi-criteria selection of a deep-water port in the Eastern Baltic Sea , 2015, Appl. Soft Comput..

[52]  Edmundas Kazimieras Zavadskas,et al.  A new additive ratio assessment (ARAS) method in multicriteria decision‐making , 2010 .

[53]  Edmundas Kazimieras Zavadskas,et al.  A new evaluation model for corporate financial performance using integrated CCSD and FCM-ARAS approach , 2019, Economic Research-Ekonomska Istraživanja.

[54]  Jana Fabianova,et al.  Operative production planning utilising quantitative forecasting and Monte Carlo simulations , 2019 .

[55]  Edmundas Kazimieras Zavadskas,et al.  A Novel Method for Multiple Criteria Analysis: Grey Additive Ratio Assessment (ARAS-G) Method , 2010, Informatica.

[56]  Edmundas Kazimieras Zavadskas,et al.  Multi-criteria decision-making system for sustainable building assessment/certification , 2015 .

[57]  Moslem Alimohammadlou,et al.  A model for prioritizing outsourceable activities in universities through an integrated fuzzy-MCDM method , 2019, International Journal of Construction Management.

[58]  Juan-juan Peng,et al.  A multi-criteria decision-making framework for risk ranking of energy performance contracting project under picture fuzzy environment , 2018, Journal of Cleaner Production.

[59]  Yan-Kai Fu,et al.  An integrated approach to catering supplier selection using AHP-ARAS-MCGP methodology , 2019, Journal of Air Transport Management.

[60]  Dragisa Stanujkic,et al.  Extension of the ARAS Method for Decision-Making Problems with Interval-Valued Triangular Fuzzy Numbers , 2015, Informatica.

[61]  Rohit Bhatnagar,et al.  A Comparative Study on the Use of Third Party Logistics Services by Singaporean and Malaysian Firms , 2006 .

[62]  İbrahim Zeki Akyurt,et al.  An integrated ARAS and interval type-2 hesitant fuzzy sets method for underground site selection: Seasonal hydrogen storage in salt caverns , 2019, Journal of Petroleum Science and Engineering.

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

[64]  Edmundas Kazimieras Zavadskas,et al.  Multiple criteria assessment of alternatives for built and human environment renovation , 2010 .

[65]  Madjid Tavana,et al.  An Integrated Intuitionistic Fuzzy AHP and SWOT Method for Outsourcing Reverse Logistics Highlights , 2015 .

[66]  Lin Li,et al.  Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project , 2018, Appl. Soft Comput..

[67]  F. Lee,et al.  Outsourcing reverse logistics of high-tech manufacturing firms by using a systematic decision-making approach: TFT-LCD sector in Taiwan , 2010 .

[68]  Vladik Kreinovich,et al.  Picture fuzzy sets - A new concept for computational intelligence problems , 2013, 2013 Third World Congress on Information and Communication Technologies (WICT 2013).

[69]  Zenonas Turskis,et al.  Multi-criteria analysis of electricity generation technologies in Lithuania , 2016 .

[70]  Hui Liu,et al.  Models for multiple attribute decision making with picture fuzzy information , 2019, J. Intell. Fuzzy Syst..

[71]  Rudolf Kampf,et al.  The application of simulation model of a milk run to identify the occurrence of failures , 2018 .

[72]  A. Mokrini,et al.  A fuzzy multi-criteria decision analysis approach for risk evaluation in healthcare logistics outsourcing: Case of Morocco , 2020, Health services management research.

[73]  Le Hoang Son Measuring analogousness in picture fuzzy sets: from picture distance measures to picture association measures , 2017, Fuzzy Optim. Decis. Mak..

[74]  Hülya Torun,et al.  Multi Criteria Decision Making Based on TOPSIS Method with Extended Fuzzy Sets , 2019, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making.

[75]  Yanbing Ju,et al.  Study of site selection of electric vehicle charging station based on extended GRP method under picture fuzzy environment , 2019, Comput. Ind. Eng..

[76]  Jurgita Antucheviciene,et al.  Measuring Performance in Transportation Companies in Developing Countries: A Novel Rough ARAS Model , 2018, Symmetry.