A multihesitant fuzzy linguistic multicriteria decision‐making approach for logistics outsourcing with incomplete weight information

• Author: Zhang‐peng Tian,Jing Wang,Jian‐qiang Wang,Dong‐yan Zhao
• Published date: 01 May 2018
• DOI: http://doi.org/10.1111/itor.12448
• Date: 01 May 2018

Intl. Trans. in Op. Res. 25 (2018) 831–856

DOI: 10.1111/itor.12448

INTERNATIONAL

TRANSACTIONS

IN OPERATIONAL

RESEARCH

A multihesitant fuzzy linguistic multicriteria decision-making

approach for logistics outsourcing with incomplete weight

information

Jing Wanga,b , Jian-qiang Wanga, Zhang-peng Tianaand Dong-yan Zhaob

aManagement Science & Information Management Department, Schoolof Business, Central South University, Changsha

410083, China

bBusiness & Economics Department, International College, Central South Universityof Forestry and Technology,

Changsha 410004, China

E-mail: 30422815@qq.com[J. Wang]; jqwang@csu.edu.cn[J.-q.Wang]; zp_tian@qq.com[Tian];

zhaodongyan@csuft.edu.cn[Zhao]

Received 5 August 2016; received in revised form 14 July 2017; accepted 24 July 2017

Abstract

In selecting logistics service providers, the evaluationcriteria can be easily prioritized and possibly interrelated

with each other, and the assessment of alternatives under qualitative criteria is usually accomplished by

more than one decision maker. A novel multicriteria decision-making approach with multihesitant fuzzy

linguistic term elements (MHFLTEs) based on the Heronian mean (HM) and prioritized average operators

can effectively deal with the problems inherent in such a scenario. Multihesitant fuzzy linguistic term sets

(MHFLTSs)were proposed on the basis of multihesitant fuzzy sets (MHFSs) and hesitant fuzzy linguistic sets

(HFLSs), where each MHFLTE can contain nonconsecutive and repeatedlinguistic ter ms. Using MHFLTEs,

one decision maker can provide one or several consecutive linguistic terms in evaluating an alternative under

one specific criterion, different decision makers’ evaluation values can be collected, and the frequency of a

linguistic term in the evaluationinformation can accord with reality.This paper revises the basic operations and

comparison method for MHFLTEs on the basis of the originals and defines some multihesitant fuzzy linguistic

HM operators for MHFLTEs to deal with problems in which weight information cannot be accurately

established for criteria, but their priorities can be provided in groups or without groups. Finally, the validity

and effectiveness of the proposedapproach are demonstrated through an illustrationof a logistics outsourcing

problem and a comparison analysis.

Keywords: multihesitant fuzzy linguistic term sets; prioritized average operators; Heronian mean operators; linguistic

decision making

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2017 The Authors.

International Transactionsin Operational Research C

2017 International Federation of OperationalResearch Societies

Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA02148,

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832 J. Wang et al. / Intl. Trans. in Op. Res. 25 (2018) 831–856

1. Introduction

Porter’s value chain theory categorizes inbound logistics, operations, outbound logistics, market-

ing, and sales and service as primary activities; improving any of these activities can greatly smooth

the running of a business (Porter, 1985). Each organization needs to concentrate on developing

competitive advantages in its core areas, and at the same time logistics plays a critical role in

business; therefore logistics outsourcing has emerged as a popular strategy. Logistics outsourcing,

also called third-party logistics, involves employing external companies or contractors to perform

some or all logistics functions, including transportation,distribution, warehousing, inventory man-

agement, order processing, and materials handling, which were originally performed within the

outsourcing company (Lieb et al., 1993). Researchers generally attribute the increasing adoption

of logistics outsourcing to the benefits it brings: cost reduction, performance improvement, greater

competitiveness, and virtual organization through strategic alliance. At the same time, logistics ser-

vice providers have made great progress in efficiency and the scope and quality of their services. In

this changing landscape, identifying an optimal logisticscontractor among the available alternatives

always requires comprehensive consideration and systematic comparison.

Most of the existing research into selecting and evaluating logistics service providers uses crisp

numbers (Liou and Chuang, 2010; Ho et al., 2012) or intuitionistic fuzzy sets (Wan et al., 2015)

to express evaluation values, and only some studies have considered linguistic variables (Kannan

et al., 2009; Liu and Wang,2009). Cost is the most widely utilized criterion, followed by relationship,

services, and quality (Aguezzoul, 2014). Numerical dataare availablefor quantitative criteria such as

cost, but linguistic assessments are preferable for qualitative criteria such as relationship, for which

subjective judgments are required. When the multiple criteria being considered are interrelated with

each other, or more than one decision maker participates in assessing the criteria, the selection and

evaluation of logistics service providers becomes an intractable issue.

Zadeh (1975) proposed a fuzzy linguistic approach in which a linguistic variable can take a

value as a word or sentence from a natural or artificial language and can then be used to evaluate

alternatives with respect to various criteria. This proposal has formed the basis for a great deal

of research on the related topics (Kahraman et al., 2015; Rodr´

ıguez et al., 2016a). For example,

Xu (2004) defined uncertain linguistic variables, whose values are intervals of linguistic terms such

as [s1,s3]. Furthermore, the membership degree of a single linguistic term can be described with

the assistance of fuzzy sets (Zadeh, 1965) and their extensions, including intuitionistic linguistic

sets (Wang and Li, 2010; Yu et al., 2018), neutrosophic linguistic sets (Ye, 2015; Tian et al., 2017),

duplex linguistic sets (Yang et al., 2012), and gray linguistic sets (Tian et al., 2015). Some of these

linguistic sets have been extended based on uncertain linguistic variables (Liu et al., 2014; Ye,

2014; Tian et al., 2016). Recently, several studies have applied hesitant fuzzy linguistic term sets

(HFLTSs; Rodr´

ıguez et al., 2012, 2014), which consist of discrete but consecutive linguistic terms

such as {s1,s2,s3}; these sets use comparative linguistic expressions to express the fluctuation of

one decision maker’s assessment among several possible linguistic values. Although HFLTSs can

provide richer expressions than single linguistic terms and can better address vague and imprecise

information, their form cannot reflect the divergence of different decision makers because it only

permits consecutive values. In order to extend HFLTSs, Wang (2015) proposed extended HFLTSs

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2017 The Authors.

International Transactionsin Operational Research C

2017 International Federation of OperationalResearch Societies