Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Time-Dependent Fuzzy Programming Approach for the Green Multimodal Routing Problem with Rail Service Capacity Uncertainty and Road Traffic Congestion
Shandong Univ Finance & Econ, Sch Management Sci & Engn, 7366 Second Ring East Rd, Jinan 250014, Shandong, Peoples R China..
WU Vienna Univ Econ & Business, Inst Prod Management, Welthandelspl 1, A-1020 Vienna, Austria..
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0003-4057-4124
Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China..
2018 (English)In: Complexity, ISSN 1076-2787, E-ISSN 1099-0526, article id 8645793Article in journal (Refereed) Published
Abstract [en]

This study explores an operational-level container routing problem in the road-rail multimodal service network. In response to the demand for an environmentally friendly transportation, we extend the problem into a green version by using both emission charging method and bi-objective optimization to optimize the CO2 emissions in the routing. Two uncertain factors, including capacity uncertainty of rail services and travel time uncertainty of road services, are formulated in order to improve the reliability of the routes. By using the triangular fuzzy numbers and time-dependent travel time to separately model the capacity uncertainty and travel time uncertainty, we establish a fuzzy chance-constrained mixed integer nonlinear programming model. A linearization-based exact solution strategy is designed, so that the problem can be effectively solved by any exact solution algorithm on any mathematical programming software. An empirical case is presented to demonstrate the feasibility of the proposed methods. In the case discussion, sensitivity analysis and bi-objective optimization analysis are used to find that the bi-objective optimization method is more effective than the emission charging method in lowering the CO2 emissions for the given case. Then, we combine sensitivity analysis and fuzzy simulation to identify the best confidence value in the fuzzy chance constraint. All the discussion will help decision makers to better organize the green multimodal transportation.

Place, publisher, year, edition, pages
WILEY-HINDAWI , 2018. article id 8645793
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-232424DOI: 10.1155/2018/8645793ISI: 000437972700001Scopus ID: 2-s2.0-85056253990OAI: oai:DiVA.org:kth-232424DiVA, id: diva2:1235305
Note

QC 20180725

Available from: 2018-07-25 Created: 2018-07-25 Last updated: 2019-03-18Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Zhang, Chen
By organisation
Health Informatics and Logistics
In the same journal
Complexity
Transport Systems and Logistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 230 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf