Robust reliability and resource allocation - Models and algorithms

Antti Toppila

Research output: ThesisDoctoral ThesisCollection of Articles

Abstract

Organizational decision makers (DMs) such as companies, institutions and public sector agencies rely on mathematical models for decision support. Often these models have parameters such as probabilities of events and outcomes of actions, which typically are epistemically uncertain due to the lack of historical data or other information. In such cases, DMs often need to understand how this epistemic uncertainty impacts the decision recommendations. This Dissertation considers models for supporting allocation decisions in settings where epistemic uncertainty is modeled explicitly through incomplete information. The resulting decision recommendations that account for epistemic uncertainty are derived through dominance: Alternative A dominates alternative B if A is at least as good as B for all parameters that are compatible with the available incomplete information, and moreover, strictly better for some. A dominated alternative should not be selected, because there exist at least one alternative that is not worse for any parameters and is strictly better for some. Thus, the decision recommendation to select an alternative that is non-dominated (ND) is robust with respect to the epistemic uncertainty. In the models considered in this Dissertation, generating the ND alternatives leads to a computationally challenging combinatorial optimization problem. Several exact algorithms and approximative methods for computing the ND alternatives are developed. The exact methods are based on classical dynamic programming and branch-and-bound algorithms, as well as binary decision diagrams, which have recently been used in solving challenging optimization problems. The simplification methods, on the other hand, are more ad hoc in nature and based on problem specific approaches. This Dissertation contributes by providing ways for analyzing the impact of epistemic uncertainty with incomplete information in application areas which are central in the fields of risk analysis and decision analysis, namely (i) probabilistic risk analysis based on importance measures, (ii) allocation of resources to reliability enhancing actions, (iii) project portfolio selection, and (iv) resource allocation to standardization activities. The developed methods are generic in that they could likely be adopted with small refinements even in other application areas.
Translated title of the contributionRobusti luotettavuuden ja resurssien allokointi
Original languageEnglish
QualificationDoctor's degree
Awarding Institution
  • Aalto University
Supervisors/Advisors
  • Salo, Ahti, Supervising Professor
  • Salo, Ahti, Thesis Advisor
Publisher
Print ISBNs978-952-60-7070-4
Electronic ISBNs978-952-60-7071-1
Publication statusPublished - 2016
MoE publication typeG5 Doctoral dissertation (article)

Keywords

  • epistemic uncertainty
  • incomplete information
  • combinatorial optimization
  • importance measures
  • binary decision diagrams
  • project portfolio selection

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