Scenario-based portfolio model for building robust and proactive strategies

Vilkkumaa, Eeva; Liesiö, Juuso; Salo, Ahti; Ilmola‐Sheppard, Leena

doi:10.1016/j.ejor.2017.09.012

Cited by 34 publications

(18 citation statements)

References 59 publications

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“…Two features that affect many real world problems are related to the uncertainty of the performances expected from activation of facilities [47,62] and to the presence of a plurality of stakeholders [56]. In the following we introduce these further elements in our model.…”

Section: Uncertainty and Plurality Of Stakeholdersmentioning

confidence: 99%

A general space-time model for combinatorial optimization problems (and not only)

Barbati

Corrente

Greco

2020

Omega

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We consider the problem of defining a strategy consisting of a set of facilities taking into account also the location where they have to be assigned and the time in which they have to be activated. The facilities are evaluated with respect to a set of criteria. The plan has to be devised respecting some constraints related to different aspects of the problem such as precedence restrictions due to the nature of the facilities. Among the constraints, there are some related to the available budget. We consider also the uncertainty related to the performances of the facilities with respect to considered criteria, and plurality of stakeholders participating to the decision. The considered problem can be seen as the combination of some prototypical operations research problems: knapsack problem, location problem and project scheduling. Indeed, the basic brick of our model is a variable x ilt which takes value 1 if facility i is activated in location l at time t, and 0 otherwise. Due to the conjoint consideration of a location and a time in the decision variables, what we propose can be seen as a general space-time model for operations research problems. We discuss how such a model permits to handle complex problems using several methodologies including multiple attribute value theory and multiobjective optimization. With respect to the latter point, without any loss of the generality, we consider the compromise programming and an interactive methodology based on the Dominance-based Rough Set Approach. We illustrate the application of our model with a simple didactic example.

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Section: Uncertainty and Plurality Of Stakeholdersmentioning

confidence: 99%

A general space-time model for combinatorial optimization problems (and not only)

Barbati

Corrente

Greco

2020

Omega

View full text Add to dashboard Cite

show abstract

“…This is a traditional probabilistic interpretation of uncertainty in which each attribute evaluation is a random variable and the unfolding of the future is the ‘experiment’ whose outcome (a scenario) determines the realization of the random variable. In this interpretation, a scenario is formally equivalent to a “state of nature.” The probabilisitic approach has probably been the most popular way to aggregate across scenarios (e.g., Levary & Wan, ; Vilkkumaa et al ., ), but has also been criticized Stewart et al . ().…”

Section: Scenario Planning In Multicriteria Decision Analysismentioning

confidence: 99%

“…The probabilisitic approach has probably been the most popular way to aggregate across scenarios (e.g., Levary & Wan, 1999;Vilkkumaa et al, 2018), but has also been criticized Stewart et al (2013). On one hand, these criticisms can appear quite techical and esoteric-the set of scenarios does not constitute a complete probability space, and so the scenario "likelihoods" are not probabilities; the scenarios also do not represent the same dimensions in probability space, and thus are not likelihoods (in a statistical sense) either.…”

Section: Scenari O Pl Anning In Multicriteria Decis Ion Analys Ismentioning

confidence: 99%

Scenario planning in the analytic hierarchy process

Durbach

2019

Futures & Foresight Science

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Multicriteria decision analysis and scenario planning are complementary tools for supporting large‐scale, strategic decision making, but there has been limited interaction between the fields. This paper describes how scenario planning can be integrated with the analytic hierarchy process (AHP), one of the most popular approaches in decision analysis and one that is arguably more accessible for new users. Scenario planners looking for an avenue into multi‐criteria decision analysis may find the AHP a useful introduction, and AHP practitioners may find in scenario planning a tool with which to address problems with large, structural or “deep” uncertainties. A common understanding of scenarios as plausible futures, rather than states of nature, is emphasized, as is how scenarios can be viewed as a kind of “meta‐attribute” over which possible courses of action can be compared. A simulation experiment assesses the potential effects of ignoring scenario‐specific information, as well as of different ways of constructing scenarios.

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“…Although suggestions for simplifying the process of applying MAUT have been put forward and tested (Durbach & Stewart, 2012a;Durbach & Stewart, 2012b) its role in intuitive logics scenario planning is problematical because it is based on the assumption that probabilities can be estimated for the states of nature. Scenarios cannot in most cases be regarded as a mutually exhaustive set of states of nature (Durbach, 2019) and, even if they could be, it is doubtful whether reliable probabilities could be estimated for them given their uniqueness and the high degree of uncertainty associated with scenario planning applications (Tversky & Kahneman, 1974;van der Heijden, 1996. pp 28-29; but see Vilkkumaa et al, 2018 for a counter argument when scenarios can be assumed to be mutually exhaustive).…”

Section: Literature Reviewmentioning

confidence: 99%

Supporting multiattribute decisions in scenario planning using a simple method based on ranks

Goodwin

2019

Futures & Foresight Science

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A neglected area of scenario planning is the provision of support for strategic decisions that involve multiple attributes. When the number of scenarios and attributes is large, conventional multiattribute decision analysis methods require the elicitation of a large number of values and weights, which can be demanding and time consuming for decision makers. This paper examines the effectiveness of using a simple approximation to the simple multiattribute rating method (SMART) that is based purely on the ranking of options and attributes. The method was tested on 250,000 simulated decision problems and was found to perform well when assessed on the basis of its hit rate (the percentage of times it identified the same best option as SMART) and the utility loss resulting from the approximation. In large problems, where simplifications are likely to be most useful, it outperformed an alternative approximation method, SMARTER, which is more complex to apply, and it was almost as effective as SMARTER on smaller problems.

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Scenario-based portfolio model for building robust and proactive strategies

Cited by 34 publications

References 59 publications

A general space-time model for combinatorial optimization problems (and not only)

A general space-time model for combinatorial optimization problems (and not only)

Scenario planning in the analytic hierarchy process

Supporting multiattribute decisions in scenario planning using a simple method based on ranks

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