### Table 3: Minimizing the total expected bed shortage Procedure Total exp. shortage Comp. time (s)

"... In PAGE 21: ... Table3 shows that the total expected bed shortage drops from 37.82 to 34.... ..."

### Table 4.Calculation of Total Shortages and Excesses Time Period 0 1 2 3 4 5 6 7 8 9 10 11 Inventory 0 18

"... In PAGE 3: ...Table4 illustrates this calculation. This calculation is repeated for each part and each forecasting methods under consideration.... ..."

### Table II. The distance-based selection yielded fewer sets, with a distortion performance similar or inferior to our algorithm, and is therefore not considered further in this paper. The unsatisfactory performance is caused by the fact that a lot of boundary sensors are chosen in the first set, resulting in more total number of sensors in that set, while at the same time causing a shortage of them in later sets.

2007

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### Table 8 Shortage Level

2002

"... In PAGE 12: ... Figure 8b shows the very drastic reduction in EV water use imposed by distribution I (with respect to the non-ration case) compared to the more mild curtailment in demand needed to absorb the effects of increased frequency of shortage. Yet to emphasize the complex relationship between the WTP and the shortage probability distribution the example in Table8 illustrates that for a particular consumer there is no WTP to go from distribution L to the more benign M, which represents a 1% reduction in the EV of shortage! This is because the possibility of having a very severe event (40% shortage) makes LT conservation measures necessary. For this particular set of parameters these LT conservation measures alone are more than enough to cope with small shortages of 10% and no extra short-term measures are required.... ..."

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### Table 2: Probabilities of meeting capacity demands for tools of lead time=2..12 Since infrequent shortages are achieved at the expense of buying extra capacity, we also report the ratio of expected excess tool capacity to expected capacity required (see Table 3). Ratios are converted to percentages for readability. \True quot; is the expected excess capacity actually required to meet the demand with a probability of 84.1%. As expected, Proportion (Allocation) consistently installs too much (not enough) capacity.

1999

"... In PAGE 11: ...1%. Table2 depicts actual probabilities of meeting the true capacity demand when capacities are selected according to SeDFAM variance estimates. Similar computations were done for the Allocation and Proportion Schemes.... ..."

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### Table 4 The simulations show that the insurance companies make a small profit in about 88% of the decades. This is because they receive flood premiums (2 per cent of the bundled property insurance premium). In decades with minor flood failures the balance is slightly negative; premiums are not sufficient to cover for compensations. In extreme decades the shortage is even larger, in 231 time-periods the deficit is greater than 100 million HUF. In the 140 decades with most failures, the deficit amounts to over 300 million HUF.

2003

"... In PAGE 62: ... Probability Weighted Average Outcome 0,88180 (no failure) -3 000 0,09130 1 347 000 0,02010 1 947 000 0,00520 2 697 000 0,00150 3 297 000 0,00010 4 647 000 Table4 . Individual balance, scenario1.... ..."

### Table A1: Distribution of Length of Component Shortage Length of Shortage Probability

### Table 2: Alternative Shortage Probability Distributions for Example

1995

"... In PAGE 7: ... Demand hardening can be examined with this two-stage mathematical programming approach. The shortages from Table2 for four increasingly severe shortage distributions are applied to the example household. The results in Table 3 indicate the effect of demand hardening, increased cost for short-term water conservation and increased overall costs with increased optimized long-term water conservation effort.... ..."

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