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The additive model was then used to evaluate the offers under consideration, see Fig. 8.
Offers 3 and 6 were the best and worst ranked tenders. Looking at the utility intervals, we
could discard offer 6, because its maximum utility was lower than the minimum utility of
offer 2. Consequently, we concluded that it was dominated. Although offer 3 appeared to be
the most highly recommended, however, the overlapped utility intervals were examined in
more detail through SA.
Fig. 7. The attribute weights in the selection of a supplier for cleaning services
First, the stability interval for all objectives throughout the hierarchy were computed, i.e.,
the interval where the average normalized weight for any objective can vary without the
best ranked offer changing. The resulting stability intervals for all objectives throughout
the hierarchy were [0, 1], which meant that, whatever their relative importance, offer 3
remained the best-ranked tender, except for Delivery conditions and human resources , and
Quality control procedures with stability weight intervals [0, 0.598] and [0, 0.748],
respectively. Taking into account that the narrower a stability weight interval is the more
sensitive the offers ranking is, we concluded that the offers ranking was robust regarding
the elicited weights.
Fig. 8. Offer evaluation
On the other hand, only offer 3 was non-dominated and potentially optimal, so we could
definitively conclude that it is the best offer. Monte Carlo simulation techniques were
performed led to the same finding; offer 3 was the best.
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