research-article

Multi-objective Risk Assessment Management via Zero-One Desirability Programming Model: Thailand - Cambodia Beverage Logistics Solutions

Authors:

Nitidetch Koohathongsumrit,

Pongchanun LuangpaiboonAuthors Info & Claims

ICFET '20: Proceedings of the 6th International Conference on Frontiers of Educational Technologies

Pages 144 - 148

https://doi.org/10.1145/3404709.3404756

Published: 13 July 2020 Publication History

Abstract

This paper presents a hybrid approach of analytic hierarchy process (AHP) and desirability function in forms of the zero-one desirability programming model (ZODP). An aim is to select the appropriate multimodal freight transportation networks under various basic requirements of the community. This optimization problem contains incommensurate and conflicting objectives and a decision maker (DM)'s preference information decides a satisfactory compromise desirability level. The AHP method is first used to structure the transportation problem systematically. Hierarchically, the structure starts with the main objective to be achieved, then the criteria needed with consistency weight, and lastly the alternatives which are necessary to fulfill the main objectives. The AHP results are then formulated in the ZODP model, as there are multiple criteria and priorities. After solving the ZODP model with the relative weights integration from the AHP, the proposed model would be highly effective in generating a compromise route that is faithful to the DM's preference structure. Selected alternatives can be implemented by the management due to the allocated resources or all criteria are met and the proposed method allows the DM to adjust any of the preference parameters as well.

References

[1]

Seo, Y. J., Chen, F. and Roh, S. Y. 2017. Multimodal transportation: the case of laptop from Chongqing in China to Rotterdam in Europe. Asian J. Shipp. Logist. 33, 3, 155--165.

[2]

Luangpaiboon, P. 2015. Evolutionary elements on composite ascent algorithm for multiple response surface optimisation. J. Intell. Manuf. 26, 3, 539--552.

Digital Library

[3]

Chang, T.-S. 2008. Best routes selection in international intermodal networks. Comput. Oper. Res. 35, 9, 2877--2891.

Digital Library

[4]

Sattayaprasert, W., Hanaoka, S., Taneerananon, P. and Pradhananga, R. 2008. Creating a risk-based network for hazmat logistics by route prioritization with AHP. IATSS, 32, 1, 74--87.

[5]

Ko, H. J. (2009). A DSS approach with fuzzy AHP to facilitate international multimodal transportation network. KMI Int. J. Marit. Aff. and Fish, 1, 1, 51--70.

[6]

Ayed, H., Galvez-Fernandez, C., Habbas, Z. and Khadraoui, D. 2011. Solving time-dependent multimodal transport problems using a transfer graph model. Comput. Ind. Eng. 61, 2, 391--401.

Digital Library

[7]

Xie, Y., Lu, W., Wang, W. and Quadrifoglio, L. 2012. A multimodal location and routing model for hazardous materials transportation. J. Hazard. Mater. 227--228, 135--411.

[8]

Kengpol, A. and Tuammee, S. 2016. The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study. Int. J. Prod. Res. 54, 4, 1020--1038.

[9]

Xiong, G. and Wang, Y. 2014. Best routes selection in multimodal networks using multi-objective genetic algorithm. J. Comb. Optim. 28, 3, 655--673.

Digital Library

[10]

Arunyanart, S., Ohmori, S. and Yoshimoto, K. 2016. Selection of export route option in GMS region recommendation for current situation. J. Jpn. Ind. Manage Assoc. 67, 2E, 193--201.

[11]

Tokcaer, S. and özpeynirci, ö. 2016. A bi-objective multimodal transportation planning problem with an application to a petrochemical ethylene manufacturer. Marit. Econ. Logist. 20, 1, 72--88.

[12]

Wang, Y. and Yeo, G.-T. 2017. Intermodal route selection for cargo transportation from Korea to Central Asia by adopting fuzzy Delphi and fuzzy ELECTRE I methods. Marit. Policy Manag. 45, 1, 3--18.

[13]

Fazayeli, S., Eydi, A. and Kamalabadi, I. N. 2018. Location-routing problem in multimodal transportation network with time windows and fuzzy demands: Presenting a two-part genetic algorithm. Comput. Ind. Eng. 119, 233--246.

[14]

Singh, S. P. and Singh, P. (2018). A hybrid decision support model using axiomatic fuzzy set theory in AHP and TOPSIS for multicriteria route selection. Complex Intell. Syst. 4, 2, 133--143.

[15]

Meethom, W. and Koohathongsumrit, N. 2018. Design of decision support system for road freight transportation routing using multilayer zero one goal programming. Eng. J. 22, 6, 185--205.

[16]

Gul, M., Guneri, A. F. and Nasirli, S. M. 2019. A fuzzy-based model for risk assessment of routes in oil transportation. Int. J. Environ. Sci. Technol. 16, 8, 4671--4686.

[17]

Turgut, M. and Ustundag, A. 2014. A hybrid risk evaluation model for automotive production. Int. J. Mach. Learn. Comput. 4, 5, 458--462.

[18]

Yang, B. H. 2019. Monotonic estimation for the survival probability over a risk-rated portfolio by discrete-time hazard rate models. Int. J. Mach. Learn. Comput. 9, 5, 675--681.

[19]

Miftachurohmah, N., Mukhlash, I. and Sutikno, S. 2019. web-based implementation of risk analysis of paddy production with ENSO indicators. Int. J. Mach. Learn. Comput. 9, 3, 304--309.

[20]

Harrington, E. C. 1965. The desirability function. Ind. Qual. Control. 21, 10, 494--498.

[21]

Luangpaiboon, P., Boonhao, S. and Montemanni, R. 2019. Steepest ant sense algorithm for parameter optimisation of multi-response processes based on taguchi design. J. Intell. Manuf. 30, 1, 441--457.

Digital Library

[22]

Saaty, T. L. 1980. The analytic hierarchy process. McGraw-Hill, New York, NY.

[23]

Singh, A. K. 2016. Competitive service quality benchmarking in airline industry using AHP. BIJ. 23, 4, 768--791.

[24]

Pandey, R. K. and Panda, S. S. 2015. Optimization of bone drilling using Taguchi methodology coupled with fuzzy based desirability function approach. J. Intell. Manuf. 26, 6, 1121--1129.

Digital Library

[25]

Jeong, I. J. and Kim, K. J. 2009. An interactive desirability function method to multiresponse optimisation. Eur. J. Oper. Res. 195, 2, 412--426.

[26]

Chen, H.-W., Wong, W. K. and Xu, H. (2012). An augmented approach to the desirability function. J. Appl. Stat. 39, 3, 599--613.

[27]

Zhang, L., Ma, Y., Ouyang, L. and Liu, J. 2016. Balancing the subjective and objective weights for correlated multiresponse optimization. Qual. Reliab. Eng. Int. 32, 3, 817--835.

[28]

Lee, D. H., Jeong, I. J. and Kim, K. J. 2018. A desirability function method for optimizing mean and variability of multiple responses using a posterior preference articulation approach. Qual. Reliab. Eng. Int. 34, 3, 360--376.

[29]

Hazir, E., Erdinler, E. S. and Koc, K. H. 2018. Optimization of CNC cutting parameters using design of experiment (DOE) and desirability function. J. Forestry Res. 29, 5, 1423--1434.

[30]

Singh, A., Datta, S., Mahapatra, S. S., Singha, T. and Majumdar, G. 2013. Optimization of bead geometry of submerged arc weld using fuzzy based desirability function approach. J. Intell. Manuf. 4, 1, 35--44.

Digital Library

[31]

Kaewfak, K. and Ammarapala, V. 2018. The decision making of freight route in multimodal transportation. Suranaree J. Sci. Technol. 25, 1, 1--10.

[32]

Hallikas, J., Virolainen, V. and Tuominen, M. 2002. Risk analysis and assessment in network environments: A dyadic case study. Int. J. Prod. Econ. 78, 1, 45--55.

[33]

Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V. and Tuominen, M. 2004. Risk management processes in supplier networks. Int. J. Prod. Econ. 90, 1, 47--58.

[34]

Oliveira, M., Fontes, D. B. M. M. and Pereira, T. 2015. Evaluating vehicle painting plans in an automobile assembly plant using an integrated AHP - PROMETHEE approach. Int. T. Oper. Res. 25, 4, 1383--1406.

Cited By

Wang ZXi YXiao X(2024)Research on Multi-Parameter Optimization of Conical Roller Line Processing Technology Based on Satisfaction FunctionProcesses10.3390/pr1209202012:9(2020)Online publication date: 19-Sep-2024
https://doi.org/10.3390/pr12092020
Koohathongsumrit NChankham W(2023)Route selection in multimodal supply chainsApplied Soft Computing10.1016/j.asoc.2023.110167137:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110167
Koohathongsumrit NChankham W(2022)A hybrid approach of fuzzy risk assessment-based incenter of centroid and MCDM methods for multimodal transportation route selectionCogent Engineering10.1080/23311916.2022.20916729:1Online publication date: 27-Jul-2022
https://doi.org/10.1080/23311916.2022.2091672
Show More Cited By

Index Terms

Multi-objective Risk Assessment Management via Zero-One Desirability Programming Model: Thailand - Cambodia Beverage Logistics Solutions
1. Applied computing
  1. Operations research
    1. Transportation

Recommendations

Consistency-based risk assessment with probabilistic linguistic preference relation

Display Omitted Propose the probabilistic linguistic preference relation (PLPR).Discuss the consistency of PLPR from the perspective of digraph.Present the consistency and acceptable consistency measures of PLPR.Establish an optimization model to ...
Route selection in multimodal supply chains: A fuzzy risk assessment model-BWM-MARCOS framework
Abstract
Selecting a route in distribution networks can be considered as a multiple criteria decision-making problem owing to the involvement of conflicting decision criteria, many possible alternatives, different preferences, and ...
Highlights
- The proposed framework innovatively integrates fuzzy risk modeling and MCDM methods.
A Hybrid MCDM Method for Route Selection of Multimodal Transportation Network
ISNN '08: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks

Multimodal movements are those in which two or more different transportation modes are linked end-to-end in order to move freight and/or people from point of origin to point of destination. Computational efficiency required to select the path will be ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICFET '20: Proceedings of the 6th International Conference on Frontiers of Educational Technologies

June 2020

235 pages

ISBN:9781450375337

DOI:10.1145/3404709

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICFET 2020

ICFET 2020: 2020 The 6th International Conference on Frontiers of Educational Technologies

June 5 - 8, 2020

Tokyo, Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
88
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang ZXi YXiao X(2024)Research on Multi-Parameter Optimization of Conical Roller Line Processing Technology Based on Satisfaction FunctionProcesses10.3390/pr1209202012:9(2020)Online publication date: 19-Sep-2024
https://doi.org/10.3390/pr12092020
Koohathongsumrit NChankham W(2023)Route selection in multimodal supply chainsApplied Soft Computing10.1016/j.asoc.2023.110167137:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110167
Koohathongsumrit NChankham W(2022)A hybrid approach of fuzzy risk assessment-based incenter of centroid and MCDM methods for multimodal transportation route selectionCogent Engineering10.1080/23311916.2022.20916729:1Online publication date: 27-Jul-2022
https://doi.org/10.1080/23311916.2022.2091672
Atthirawong WLuangpaiboon P(2022)Hazardous waste management system for Thailand’s local administrative organization via route and location selectionJournal of the Air & Waste Management Association10.1080/10962247.2022.211099372:10(1121-1136)Online publication date: 22-Aug-2022
https://doi.org/10.1080/10962247.2022.2110993
Koohathongsumrit NMeethom W(2022)A Fuzzy Decision-Making Framework for Route Selection in Multimodal Transportation NetworksEngineering Management Journal10.1080/10429247.2022.202720534:4(689-704)Online publication date: 17-Feb-2022
https://doi.org/10.1080/10429247.2022.2027205
Atthirawong WLuangpaiboon P(2022)Location management for the supply of PD fluid via large neighborhood search based virus optimization algorithmScientific Reports10.1038/s41598-022-26385-712:1Online publication date: 16-Dec-2022
https://doi.org/10.1038/s41598-022-26385-7
Kosuge NShibasaki RSanui KOkubo K(2021)Impact of Cambodian international logistics policies on container cargo flow in a comprehensive intermodal transport networkInternational Journal of Logistics Research and Applications10.1080/13675567.2021.196789827:3(386-410)Online publication date: 19-Aug-2021
https://doi.org/10.1080/13675567.2021.1967898
Koohathongsumrit NMeethom W(2021)An integrated approach of fuzzy risk assessment model and data envelopment analysis for route selection in multimodal transportation networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2020.114342171:COnline publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1016/j.eswa.2020.114342
Koohathongsumrit NLuangpaiboon P(2021)An integrated FAHP–ZODP approach for strategic marketing information system project selectionManagerial and Decision Economics10.1002/mde.348943:6(1792-1809)Online publication date: 13-Nov-2021
https://doi.org/10.1002/mde.3489

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten