Customer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approaches

Optimization and prediction of customer satisfaction in the shipping industry impacts immensely upon strategic planning and consequently on the targeted market share of a corporation. In shipping industry, accurate measures of customer satisfaction are usually very cumbersome to elaborate. In this w...

Full description

Saved in:
Bibliographic Details
Published in:Computational economics 2019-08, Vol.54 (2), p.647-667
Main Authors: Bekiros, Stelios, Loukeris, Nikolaos, Matsatsinis, Nikolaos, Bezzina, Frank
Format: Article
Language:English
Subjects:
Artificial intelligence
Behavioral/Experimental Economics
Classification
Computer Appl. in Social and Behavioral Sciences
Customer satisfaction
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Forecasting
Heuristic methods
Market shares
Math Applications in Computer Science
Multiple criterion
Neural networks
Operations Research/Decision Theory
Optimization
Shipping
Shipping industry
Strategic planning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c381t-56033ccd3308cd20092dd7469e85c3eba7bf7a1f65c9241c9c81dfd90e508ed93
cites cdi_FETCH-LOGICAL-c381t-56033ccd3308cd20092dd7469e85c3eba7bf7a1f65c9241c9c81dfd90e508ed93
container_end_page 667
container_issue 2
container_start_page 647
container_title Computational economics
container_volume 54
creator Bekiros, Stelios
Loukeris, Nikolaos
Matsatsinis, Nikolaos
Bezzina, Frank
description Optimization and prediction of customer satisfaction in the shipping industry impacts immensely upon strategic planning and consequently on the targeted market share of a corporation. In shipping industry, accurate measures of customer satisfaction are usually very cumbersome to elaborate. In this work we aim to reveal the most effective optimization methods, employing artificial intelligence approaches such as rough sets, neural networks, advanced classification methods as well as multi-criteria analysis under a comparative framework vis-à-vis their forecasting performance.
doi_str_mv 10.1007/s10614-018-9842-5
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2093012820</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2093012820</sourcerecordid><originalsourceid>FETCH-LOGICAL-c381t-56033ccd3308cd20092dd7469e85c3eba7bf7a1f65c9241c9c81dfd90e508ed93</originalsourceid><addsrcrecordid>eNp1kMFKAzEQhoMoWKsP4C3gOTpJNrvJsRS1hYpC9Ry2SbabYnfXJEX69qas4MnTzOH__hk-hG4p3FOA6iFSKGlBgEqiZMGIOEMTKipGlKqKczQBxSpSgVKX6CrGHQAIytgE6fkhpn7vAl7XycemNsn3HX4Lzvpx9R1OrcPr1g-D77Z42dmMhCP-9qnFi-MmeItfXKpJ6w7Bx-QNng1D6GvTuniNLpr6M7qb3zlFH0-P7_MFWb0-L-ezFTFc0kRECZwbYzkHaSyD_K61VVEqJ4XhblNXm6aqaVMKo1hBjTKS2sYqcAKks4pP0d3Ymw9_HVxMetcfQpdPagaKA2WSQU7RMWVCH2NwjR6C39fhqCnok0c9etTZoz551CIzbGRiznZbF_6a_4d-AJ5Hdp4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2093012820</pqid></control><display><type>article</type><title>Customer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approaches</title><source>International Bibliography of the Social Sciences (IBSS)</source><source>EconLit with Full Text</source><source>ABI/INFORM Global</source><source>Springer Link</source><creator>Bekiros, Stelios ; Loukeris, Nikolaos ; Matsatsinis, Nikolaos ; Bezzina, Frank</creator><creatorcontrib>Bekiros, Stelios ; Loukeris, Nikolaos ; Matsatsinis, Nikolaos ; Bezzina, Frank</creatorcontrib><description>Optimization and prediction of customer satisfaction in the shipping industry impacts immensely upon strategic planning and consequently on the targeted market share of a corporation. In shipping industry, accurate measures of customer satisfaction are usually very cumbersome to elaborate. In this work we aim to reveal the most effective optimization methods, employing artificial intelligence approaches such as rough sets, neural networks, advanced classification methods as well as multi-criteria analysis under a comparative framework vis-à-vis their forecasting performance.</description><identifier>ISSN: 0927-7099</identifier><identifier>EISSN: 1572-9974</identifier><identifier>DOI: 10.1007/s10614-018-9842-5</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Artificial intelligence ; Behavioral/Experimental Economics ; Classification ; Computer Appl. in Social and Behavioral Sciences ; Customer satisfaction ; Economic Theory/Quantitative Economics/Mathematical Methods ; Economics ; Economics and Finance ; Forecasting ; Heuristic methods ; Market shares ; Math Applications in Computer Science ; Multiple criterion ; Neural networks ; Operations Research/Decision Theory ; Optimization ; Shipping ; Shipping industry ; Strategic planning</subject><ispartof>Computational economics, 2019-08, Vol.54 (2), p.647-667</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2018</rights><rights>Computational Economics is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c381t-56033ccd3308cd20092dd7469e85c3eba7bf7a1f65c9241c9c81dfd90e508ed93</citedby><cites>FETCH-LOGICAL-c381t-56033ccd3308cd20092dd7469e85c3eba7bf7a1f65c9241c9c81dfd90e508ed93</cites><orcidid>0000-0002-9376-8726</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2093012820/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2093012820?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,11667,12826,27901,27902,33200,36037,44339,74638</link.rule.ids></links><search><creatorcontrib>Bekiros, Stelios</creatorcontrib><creatorcontrib>Loukeris, Nikolaos</creatorcontrib><creatorcontrib>Matsatsinis, Nikolaos</creatorcontrib><creatorcontrib>Bezzina, Frank</creatorcontrib><title>Customer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approaches</title><title>Computational economics</title><addtitle>Comput Econ</addtitle><description>Optimization and prediction of customer satisfaction in the shipping industry impacts immensely upon strategic planning and consequently on the targeted market share of a corporation. In shipping industry, accurate measures of customer satisfaction are usually very cumbersome to elaborate. In this work we aim to reveal the most effective optimization methods, employing artificial intelligence approaches such as rough sets, neural networks, advanced classification methods as well as multi-criteria analysis under a comparative framework vis-à-vis their forecasting performance.</description><subject>Artificial intelligence</subject><subject>Behavioral/Experimental Economics</subject><subject>Classification</subject><subject>Computer Appl. in Social and Behavioral Sciences</subject><subject>Customer satisfaction</subject><subject>Economic Theory/Quantitative Economics/Mathematical Methods</subject><subject>Economics</subject><subject>Economics and Finance</subject><subject>Forecasting</subject><subject>Heuristic methods</subject><subject>Market shares</subject><subject>Math Applications in Computer Science</subject><subject>Multiple criterion</subject><subject>Neural networks</subject><subject>Operations Research/Decision Theory</subject><subject>Optimization</subject><subject>Shipping</subject><subject>Shipping industry</subject><subject>Strategic planning</subject><issn>0927-7099</issn><issn>1572-9974</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><sourceid>M0C</sourceid><recordid>eNp1kMFKAzEQhoMoWKsP4C3gOTpJNrvJsRS1hYpC9Ry2SbabYnfXJEX69qas4MnTzOH__hk-hG4p3FOA6iFSKGlBgEqiZMGIOEMTKipGlKqKczQBxSpSgVKX6CrGHQAIytgE6fkhpn7vAl7XycemNsn3HX4Lzvpx9R1OrcPr1g-D77Z42dmMhCP-9qnFi-MmeItfXKpJ6w7Bx-QNng1D6GvTuniNLpr6M7qb3zlFH0-P7_MFWb0-L-ezFTFc0kRECZwbYzkHaSyD_K61VVEqJ4XhblNXm6aqaVMKo1hBjTKS2sYqcAKks4pP0d3Ymw9_HVxMetcfQpdPagaKA2WSQU7RMWVCH2NwjR6C39fhqCnok0c9etTZoz551CIzbGRiznZbF_6a_4d-AJ5Hdp4</recordid><startdate>20190801</startdate><enddate>20190801</enddate><creator>Bekiros, Stelios</creator><creator>Loukeris, Nikolaos</creator><creator>Matsatsinis, Nikolaos</creator><creator>Bezzina, Frank</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AO</scope><scope>8BJ</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JBE</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>M0C</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-9376-8726</orcidid></search><sort><creationdate>20190801</creationdate><title>Customer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approaches</title><author>Bekiros, Stelios ; Loukeris, Nikolaos ; Matsatsinis, Nikolaos ; Bezzina, Frank</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c381t-56033ccd3308cd20092dd7469e85c3eba7bf7a1f65c9241c9c81dfd90e508ed93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Artificial intelligence</topic><topic>Behavioral/Experimental Economics</topic><topic>Classification</topic><topic>Computer Appl. in Social and Behavioral Sciences</topic><topic>Customer satisfaction</topic><topic>Economic Theory/Quantitative Economics/Mathematical Methods</topic><topic>Economics</topic><topic>Economics and Finance</topic><topic>Forecasting</topic><topic>Heuristic methods</topic><topic>Market shares</topic><topic>Math Applications in Computer Science</topic><topic>Multiple criterion</topic><topic>Neural networks</topic><topic>Operations Research/Decision Theory</topic><topic>Optimization</topic><topic>Shipping</topic><topic>Shipping industry</topic><topic>Strategic planning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bekiros, Stelios</creatorcontrib><creatorcontrib>Loukeris, Nikolaos</creatorcontrib><creatorcontrib>Matsatsinis, Nikolaos</creatorcontrib><creatorcontrib>Bezzina, Frank</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>ProQuest Pharma Collection</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>International Bibliography of the Social Sciences</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>ProQuest advanced technologies &amp; aerospace journals</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Computational economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bekiros, Stelios</au><au>Loukeris, Nikolaos</au><au>Matsatsinis, Nikolaos</au><au>Bezzina, Frank</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Customer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approaches</atitle><jtitle>Computational economics</jtitle><stitle>Comput Econ</stitle><date>2019-08-01</date><risdate>2019</risdate><volume>54</volume><issue>2</issue><spage>647</spage><epage>667</epage><pages>647-667</pages><issn>0927-7099</issn><eissn>1572-9974</eissn><abstract>Optimization and prediction of customer satisfaction in the shipping industry impacts immensely upon strategic planning and consequently on the targeted market share of a corporation. In shipping industry, accurate measures of customer satisfaction are usually very cumbersome to elaborate. In this work we aim to reveal the most effective optimization methods, employing artificial intelligence approaches such as rough sets, neural networks, advanced classification methods as well as multi-criteria analysis under a comparative framework vis-à-vis their forecasting performance.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10614-018-9842-5</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-9376-8726</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0927-7099
ispartof Computational economics, 2019-08, Vol.54 (2), p.647-667
issn 0927-7099
1572-9974
language eng
recordid cdi_proquest_journals_2093012820
source International Bibliography of the Social Sciences (IBSS); EconLit with Full Text; ABI/INFORM Global; Springer Link
subjects Artificial intelligence
Behavioral/Experimental Economics
Classification
Computer Appl. in Social and Behavioral Sciences
Customer satisfaction
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Forecasting
Heuristic methods
Market shares
Math Applications in Computer Science
Multiple criterion
Neural networks
Operations Research/Decision Theory
Optimization
Shipping
Shipping industry
Strategic planning
title Customer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approaches
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T10%3A55%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Customer%20Satisfaction%20Prediction%20in%20the%20Shipping%20Industry%20with%20Hybrid%20Meta-heuristic%20Approaches&rft.jtitle=Computational%20economics&rft.au=Bekiros,%20Stelios&rft.date=2019-08-01&rft.volume=54&rft.issue=2&rft.spage=647&rft.epage=667&rft.pages=647-667&rft.issn=0927-7099&rft.eissn=1572-9974&rft_id=info:doi/10.1007/s10614-018-9842-5&rft_dat=%3Cproquest_cross%3E2093012820%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c381t-56033ccd3308cd20092dd7469e85c3eba7bf7a1f65c9241c9c81dfd90e508ed93%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2093012820&rft_id=info:pmid/&rfr_iscdi=true