Abstract
A growing interest is expressed by organizations for the development of approaches enabling to take advantage of past experiences to improve their decision processes; they may be referred to as Lessons Learned (LL) processes. Within the LL processes implementation framework, the development of semi-automatic approaches able to distinguish criteria having major influence on the evaluation of experiences is crucial for identifying relevant recommendations and performing efficient prescriptive analysis. In this paper, we propose to contribute to LL study by focusing on the definition of an approach enabling, in a specific setting, to identify the criteria most influencing the decision process regarding the overall performance evaluation of a reduced set of experiences. The proposed approach is framed on Multi-Criteria Decision Analysis, and specifically is based on the Electre tri method. In this paper, an illustration of the proposed approach is provided studying the evaluation of logistical response strategies in humanitarian emergency situations.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Sometimes referred to as Experience feedback.
- 2.
This de facto prevents the use of traditional Machine Learning approaches.
- 3.
Note that Electre tri non-compensatory behaviour is defined by the fact that whenever \(g_j(a)-g_j(b)\) is greater than \(p_j(g_j(a)\)) no distinction is made computing the concordance index; a big difference thus cannot compensate any negative difference on another criterion j with \(j \ne i\).
References
Malvache, P., Prieur, P.: Mastering corporate experience with the REX method. In: Proceedings of ISMICK 1993, pp. 33–41 (1993)
De How, R., Benus, B., Vogler, M., Metselaar, C.: The commonKADS organization model: content, usage and computer support. Expert Syst. Appl. 11(1), 29–40 (1996)
Ermine, J.L., Chaillot, M., Bigeon, P., Charreton, B., Malavieille, D.: Méthode pour la gestion des connaissances. Ingénierie des systèmes d?information, AFCET-Hermès 4(4), 541–575 (1996)
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)
Dyer, J.S.: Multiattribute utility theory (MAUT). In: Greco, S., Ehrgott, M., Figueira, J. (eds.) Multiple Criteria Decision Analysis, vol. 233, pp. 285–314. Springer, New York (2016). https://doi.org/10.1007/978-1-4939-3094-4_8
Figueira, J.R., Mousseau, V., Roy, B.: ELECTRE methods. In: Greco, S., Ehrgott, M., Figueira, J.R. (eds.) Multiple Criteria Decision Analysis, vol. 233, pp. 155–185. Springer, New York (2016). https://doi.org/10.1007/978-1-4939-3094-4_5
Figueira, J.R., Greco, S., Roy, B., Słowiński, R.: An overview of ELECTRE methods and their recent extensions. J. Multi-Criteria Decis. Anal. 20(1–2), 61–85 (2013)
Vincke, P.: Multicriteria Decision-Aid. Wiley, Chichester (1992)
Mousseau, V., Slowinski, R.: Inferring an ELECTRE TRI model from assignment examples. J. Glob. Optim. 12(2), 157–174 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
L’Héritier, C., Imoussaten, A., Harispe, S., Dusserre, G., Roig, B. (2018). Identifying Criteria Most Influencing Strategy Performance: Application to Humanitarian Logistical Strategy Planning. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-91479-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91478-7
Online ISBN: 978-3-319-91479-4
eBook Packages: Computer ScienceComputer Science (R0)