Picture Fuzzy Decision-Making Approach for Sustainable Last-Mile Delivery

In light of the increasing importance of last-mile delivery (LMD) and the associated high costs, air pollution, and logistical challenges, research on sustainable LMD is highly trending and dynamic. The selection of sustainable LMD mode is an emerging problem for decision-makers in the logistics industry. The key question is how to determine the best LMD mode from a set of alternatives under numerous criteria with ambiguous, vague, and uncertain sustainability-related information. This paper aims to provide an advanced decision-making approach for sustainable LMD. Firstly, 20 sustainable LMD mode evaluation criteria are identified. Secondly, picture fuzzy sets (PFSs) are exploited to help decision-makers to more naturally express their preferences by voting. Thirdly, a hybrid picture fuzzy criteria weighting method based on the Direct rating and R-norm entropy is developed to compute the importance of evaluation criteria. Fourthly, a novel picture fuzzy Combined Compromise Solution method is formulated to rank alternative LMD modes. Fifthly, the presented picture fuzzy approach for sustainable LMD is implemented in the real-life decision-making context. The results show that “e-cargo bike” is the best alternative in the Pardubice context. The comparative analysis with three state-of-the-art PFS-based MCDM methods approved the high reliability of the provided approach. The sensitivity analyses of the trade-off parameter and balancing factor confirmed the high robustness of the presented approach. The introduced approach can help decision-makers in the logistics industry to elucidate sustainable LMD mode. It can solve not only the highlighted problem but also other MCDM problems under the picture fuzzy environment.

[1]  Selçuk Perçin,et al.  Evaluation of third‐party logistics (3PL) providers by using a two‐phase AHP and TOPSIS methodology , 2009 .

[2]  Yandong He,et al.  Optimal Partner Combination for Joint Distribution Alliance using Integrated Fuzzy EW-AHP and TOPSIS for Online Shopping , 2016 .

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

[4]  James W. Evans,et al.  Sustainable city logistics — Making cargo cycles viable for urban freight transport , 2015 .

[5]  Frank Teuteberg,et al.  Understanding and assessing crowd logistics business models – using everyday people for last mile delivery , 2017 .

[6]  Prasenjit Chatterjee,et al.  A structured framework for sustainable supplier selection using a combined BWM-CoCoSo model , 2019, Proceedings of 6th International Scientific Conference Contemporary Issues in Business, Management and Economics Engineering ‘2019.

[7]  E. Zavadskas,et al.  A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems , 2019, Management Decision.

[8]  Wout Dullaert,et al.  What Is the Right Delivery Option for You? Consumer Preferences for Delivery Attributes in Online Retailing , 2019, Journal of Business Logistics.

[9]  Luis Martínez,et al.  A phase change material selection using the interval-valued target-based BWM-CoCoMULTIMOORA approach: A case-study on interior building applications , 2020, Appl. Soft Comput..

[10]  Roberto Pinto,et al.  The Collection-And-Delivery Points Implementation Process from the Courier, Express and Parcel Operator's Perspective , 2018 .

[11]  Prasenjit Chatterjee,et al.  Selection of commercially available alternative passenger vehicle in automotive environment , 2020 .

[12]  D. S. Hooda,et al.  On generalized measures of fuzzy entropy , 2004 .

[13]  Vasanth Kamath,et al.  Sustainability assessment of last-mile logistics and distribution strategies: The case of local food networks , 2020 .

[15]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[16]  L. Švadlenka,et al.  A Proposal for a Decision-Making Tool in Third-Party Logistics (3PL) Provider Selection Based on Multi-Criteria Analysis and the Fuzzy Approach , 2019, Sustainability.

[17]  Teodor Gabriel Crainic,et al.  Collaboration partner selection for city logistics planning under municipal freight regulations , 2016 .

[18]  G. Don Taylor,et al.  Design and analysis of delivery 'pipelines' in truckload trucking , 2009 .

[19]  Morteza Yazdani,et al.  Application of a Gray-Based Decision Support Framework for Location Selection of a Temporary Hospital during COVID-19 Pandemic , 2020, Symmetry.

[20]  S. Boyles,et al.  Urban consolidation solutions for parcel delivery considering location, fleet and route choice , 2017 .

[21]  Prasenjit Chatterjee,et al.  An Integrated Methodology for Evaluation of Electric Vehicles Under Sustainable Automotive Environment , 2019, Advances in Environmental Engineering and Green Technologies.

[22]  Dick E. Boekee,et al.  The R-Norm Information Measure , 1980, Inf. Control..

[23]  D. Zindani,et al.  Excogitating Material Rankings Using Novel Aggregation Multiplicative Rule (AMR): A Case for Material Selection Problems , 2020 .

[24]  Huchang Liao,et al.  Cold Chain Logistics Management of Medicine with an Integrated Multi-Criteria Decision-Making Method , 2019, International journal of environmental research and public health.

[25]  Gwo-Hshiung Tzeng,et al.  A new hybrid MCDM model combining DANP with VIKOR to improve e-store business , 2013, Knowl. Based Syst..

[26]  Xiucheng Guo,et al.  Analyzing Service Quality Evaluation Indexes of Rural Last Mile Delivery Using FCE and ISM Approach , 2020, Inf..

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

[28]  Xindong Peng,et al.  Pythagorean fuzzy MCDM method based on CoCoSo and CRITIC with score function for 5G industry evaluation , 2019, Artificial Intelligence Review.

[29]  Ali Karaşan,et al.  Solid Waste Disposal Site Selection by Using Neutrosophic Combined Compromise Solution Method , 2019, EUSFLAT Conf..

[30]  M. Schenk,et al.  Impact Assessment Model for the Implementation of Cargo Bike Transshipment Points in Urban Districts , 2020 .

[31]  Le Hoang Son Generalized picture distance measure and applications to picture fuzzy clustering , 2016, Appl. Soft Comput..

[32]  Jianliang Peng Selection of Logistics Outsourcing Service Suppliers Based on AHP , 2012 .

[33]  S. K. Goyal,et al.  A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty , 2011, Math. Comput. Model..

[35]  Qaisar Khan,et al.  Different Approaches to Multi-Criteria Group Decision Making Problems for Picture Fuzzy Environment , 2018, Bulletin of the Brazilian Mathematical Society, New Series.

[36]  Miguel Figliozzi,et al.  Study of Sidewalk Autonomous Delivery Robots and Their Potential Impacts on Freight Efficiency and Travel , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[37]  C. Macharis,et al.  Crowd logistics: an opportunity for more sustainable urban freight transport? , 2017, European Transport Research Review.

[38]  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 .

[39]  Nils Boysen,et al.  Optimizing the changing locations of mobile parcel lockers in last-mile distribution , 2020, Eur. J. Oper. Res..

[40]  Madhumangal Pal,et al.  Picture fuzzy Dombi aggregation operators: Application to MADM process , 2019, Appl. Soft Comput..

[41]  Prasenjit Chatterjee,et al.  Development of an integrated decision making model for location selection of logistics centers in the Spanish autonomous communities , 2020, Expert Syst. Appl..

[42]  Zivko Erceg,et al.  A New Model for Stock Management in Order to Rationalize Costs: ABC-FUCOM-Interval Rough CoCoSo Model , 2019, Symmetry.

[43]  Camille Kamga,et al.  Cargo cycles for local delivery in New York City: Performance and impacts , 2017 .

[44]  Jarosław Wątróbski,et al.  Multi-Criteria Analysis of Electric Vans for City Logistics , 2017 .

[45]  Xindong PENG,et al.  FUZZY DECISION MAKING METHOD BASED ON COCOSO WITH CRITIC FOR FINANCIAL RISK EVALUATION , 2020 .

[46]  Russell G. Thompson,et al.  A multi-criteria spatial evaluation framework to optimise the siting of freight consolidation facilities in inner-city areas , 2020 .

[47]  Valentina Carbone,et al.  A typology of logistics at work in collaborative consumption , 2018 .

[48]  Huchang Liao,et al.  A Hesitant Fuzzy Linguistic Combined Compromise Solution Method for Multiple Criteria Decision Making , 2019 .

[49]  Nguyen Xuan Thao,et al.  Similarity measures of picture fuzzy sets based on entropy and their application in MCDM , 2019, Pattern Analysis and Applications.

[50]  A. Perego,et al.  ‘Pony express’ crowdsourcing logistics for last-mile delivery in B2C e-commerce: an economic analysis , 2020, International Journal of Logistics Research and Applications.

[51]  Xiucheng Guo,et al.  Using the FAHP, ISM, and MICMAC Approaches to Study the Sustainability Influencing Factors of the Last Mile Delivery of Rural E-Commerce Logistics , 2019, Sustainability.

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

[53]  Rajesh Joshi,et al.  A novel decision-making method using R-Norm concept and VIKOR approach under picture fuzzy environment , 2020, Expert Syst. Appl..

[54]  H. Giray Resat,et al.  Design and Analysis of Novel Hybrid Multi-Objective Optimization Approach for Data-Driven Sustainable Delivery Systems , 2020, IEEE Access.

[55]  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).

[56]  Mohammad Marufuzzaman,et al.  Last mile delivery drone selection and evaluation using the interval-valued inferential fuzzy TOPSIS , 2020 .

[57]  Chompoonut Amchang,et al.  Locational Preference of Last Mile Delivery Centres: A Case Study of Thailand Parcel Delivery Industry , 2018 .

[58]  Sujoy Bhattacharya,et al.  E-fulfillment performance evaluation for an e-tailer: a DANP approach , 2019, International Journal of Productivity and Performance Management.

[59]  Rohit Gupta,et al.  Analysis of barriers to implement drone logistics , 2020, International Journal of Logistics Research and Applications.

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

[61]  Rajesh Joshi,et al.  A new picture fuzzy information measure based on Tsallis–Havrda–Charvat concept with applications in presaging poll outcome , 2020, Comput. Appl. Math..

[62]  Chunyong Wang,et al.  SOME GEOMETRIC AGGREGATION OPERATORS BASED ON PICTURE FUZZY SETS AND THEIR APPLICATION IN MULTIPLE ATTRIBUTE DECISION MAKING , 2017 .

[63]  L. Švadlenka,et al.  A MODEL FOR BUSINESS PERFORMANCE IMPROVEMENT: A CASE OF THE POSTAL COMPANY , 2020 .

[64]  Fatih Ecer,et al.  Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model , 2020 .

[65]  Junjae Chae,et al.  A Courier Service with Electric Bicycles in an Urban Area: The Case in Seoul , 2019, Sustainability.

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

[67]  Yuji Murayama,et al.  Optimal Location Analysis of Delivery Parcel-Pickup Points Using AHP and Network Huff Model: A Case Study of Shiweitang Sub-District in Guangzhou City, China , 2020, ISPRS Int. J. Geo Inf..

[68]  Christine Bauer,et al.  "Crowd logistics": the contribution of social crowds in logistics activities , 2016, Int. J. Web Inf. Syst..

[69]  Dragan Pamucar,et al.  Sustainability assessment of OPEC countries: Application of a multiple attribute decision making tool , 2019 .

[70]  Valentina Carbone,et al.  The Rise of Crowd Logistics: A New Way to Co‐Create Logistics Value , 2017 .

[71]  Nils Boysen,et al.  Scheduling last-mile deliveries with truck-based autonomous robots , 2018, Eur. J. Oper. Res..

[72]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[73]  Slobodan Zecevic,et al.  A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection , 2014, Expert Syst. Appl..

[74]  Luca Staricco,et al.  The spatial dimension of cycle logistics , 2016 .

[75]  Massimiliano M. Schiraldi,et al.  A logistics provider evaluation and selection methodology based on AHP, DEA and linear programming integration , 2011 .

[76]  Khalid A Aljohani,et al.  A Stakeholder-Based Evaluation of the Most Suitable and Sustainable Delivery Fleet for Freight Consolidation Policies in the Inner-City Area , 2018, Sustainability.

[77]  Huchang Liao,et al.  Selection third-party logistics service providers in supply chain finance by a hesitant fuzzy linguistic combined compromise solution method , 2019, Economic Research-Ekonomska Istraživanja.

[78]  C. Hamilton Changing Service Provision in Rural Areas and the Possible Impact on Older People: A Case Example of Compulsory Post Office Closures and Outreach Services in England , 2015, Social Policy and Society.

[79]  Satish Kumar,et al.  A Jensen-α-Norm Dissimilarity Measure for Intuitionistic Fuzzy Sets and Its Applications in Multiple Attribute Decision Making , 2018, Int. J. Fuzzy Syst..

[80]  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.

[81]  Anjali Awasthi,et al.  A hybrid approach integrating Affinity Diagram, AHP and fuzzy TOPSIS for sustainable city logistics planning , 2012 .

[82]  Huchang Liao,et al.  A GREY COMBINED COMPROMISE SOLUTION (COCOSO-G) METHOD FOR SUPPLIER SELECTION IN CONSTRUCTION MANAGEMENT , 2019 .

[83]  Márcio de Almeida D'Agosto,et al.  Electric vehicles in the last mile of urban freight transportation: A sustainability assessment of postal deliveries in Rio de Janeiro-Brazil , 2019, Transportation Research Part D: Transport and Environment.

[84]  Prasenjit Chatterjee,et al.  Intelligent Decision Making Tools in Manufacturing Technology Selection , 2018 .

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

[86]  Linchuan Yang,et al.  Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making , 2019, Sustainability.

[87]  Kinga Kijewska,et al.  Analysis of Parcel Lockers’ Efficiency as the Last Mile Delivery Solution – The Results of the Research in Poland , 2016 .

[88]  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.

[89]  Alptekin Ulutaş,et al.  Location selection for logistics center with fuzzy SWARA and CoCoSo methods , 2020, J. Intell. Fuzzy Syst..

[90]  Huchang Liao,et al.  SUPPLIER SELECTION FOR HOUSING DEVELOPMENT BY AN INTEGRATED METHOD WITH INTERVAL ROUGH BOUNDARIES , 2020 .

[91]  Shinya Hanaoka,et al.  Evaluating the logistics performance of intermodal transportation in Thailand , 2008 .

[92]  Alok Raj,et al.  Analyzing critical success factors for implementation of drones in the logistics sector using grey-DEMATEL based approach , 2019, Comput. Ind. Eng..

[93]  Nitin Gupta,et al.  R-norm Intuitionistic Fuzzy Information Measures and Its Computational Applications , 2012 .