### Table 2.1 lists the optimal GTP sets we get by using Algorithm 2.1 with m = 3 and Team size Optimal GTP set Performance

### TABLE I BOUNDS ON PERFORMANCE RATIO (RULE PERFORMANCE OVER OPTIMAL PERFORMANCE) WITH n ROBOTS AND m TARGETS. Bidding Team Objective

2005

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### Table 2: Average number of teams.

2005

"... In PAGE 15: ... Hence ri estimates the probability of having i teams in the conjectured optimal policy. Table2 shows the average number of teams, PM i=2 iri, as a function of the number of servers M and buffer size B for the two sets of numerical experiments described previously, and Tables 3 and 4 display the values of r2, r3, and rM for various numbers of servers M and buffer sizes B for the first and second sets of numerical experiments, respectively.... In PAGE 15: ...Table 2: Average number of teams. As expected, Table2 shows that the average number of teams increases both with the number of servers M and with the buffer size B. However, the growth rate is rather slow, so that the average number of teams is significantly smaller than the maximum possible number of teams (i.... In PAGE 15: ...verage number of teams is significantly smaller than the maximum possible number of teams (i.e., minfM; B +2g) for large M and B. Moreover, Table2 shows that for given values of M and B, the average number of teams in the random and deterministic cases are quite similar, with the averages being slightly larger when the service rates are generated at random, rather than deterministically (this may be due to the fact that we use a larger range of possible values when the service rates are generated at random, rather than deterministically, leading to larger differences between the... ..."

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### Table 2. Project completion time variability across teams

2004

"... In PAGE 10: ... Project completion time variation in case of abandonment and rework (for a staffing level of 35 people) across teams also indicates how optimal is, given an avail- able staffing level, the resource usage the algorithm is able to achieve. As shown in Table2 , while for small numbers of peo- ple available the resource usage is optimal (i.e.... ..."

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### Table 2. Project completion time variability across teams

2004

"... In PAGE 10: ... Project completion time variation in case of abandonment and rework (for a staffing level of 35 people) across teams also indicates how optimal is, given an avail- able staffing level, the resource usage the algorithm is able to achieve. As shown in Table2 , while for small numbers of peo- ple available the resource usage is optimal (i.e.... ..."

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### Table 1. Comparison of results for various approaches.

"... In PAGE 8: ... 4. Numerical Results Table1 compares the balance and uniformity (t,s) of (n,2) de Bruijn sequences... In PAGE 9: ... In the case of Algorithm II, the characteristics of the sequences obtained by the optimal mappings with respect to both balance and uniformity criteria are shown. ------------------------- Table1 goes here ------------------------- In Table 1, we observe that: 1. Although Algorithm I generates sequences with optimal uniformity (minimum s), the corresponding balance criterion t is rather large.... In PAGE 9: ... In the case of Algorithm II, the characteristics of the sequences obtained by the optimal mappings with respect to both balance and uniformity criteria are shown. -------------------------Table 1 goes here ------------------------- In Table1 , we observe that: 1. Although Algorithm I generates sequences with optimal uniformity (minimum s), the corresponding balance criterion t is rather large.... ..."

### Table 1. Performance Characteristics of Different AM Implementations

1997

"... In PAGE 7: ...ficient, buffered writes in the SCI DSM only. Performance measurements on the UCSB SCI cluster show competitive performance behavior of the SCI AM system ( Table1 ). Our own implementation, depicted in the first row of Table 1, adds little over- head to the raw latency of 9.... ..."

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### Table 3b. Solution Statistics for Model 2 (Minimization)

1999

"... In PAGE 4: ...6 Table 2. Problem Statistics Model 1 Model 2 Pt Rows Cols 0/1 Vars Rows Cols 0/1 Vars 1 4398 4568 4568 4398 4568 170 2 4546 4738 4738 4546 4738 192 3 3030 3128 3128 3030 3128 98 4 2774 2921 2921 2774 2921 147 5 5732 5957 5957 5732 5957 225 6 5728 5978 5978 5728 5978 250 7 2538 2658 2658 2538 2658 120 8 3506 3695 3695 3506 3695 189 9 2616 2777 2777 2616 2777 161 10 1680 1758 1758 1680 1758 78 11 5628 5848 5848 5628 5848 220 12 3484 3644 3644 3484 3644 160 13 3700 3833 3833 3700 3833 133 14 4220 4436 4436 4220 4436 216 15 2234 2330 2330 2234 2330 96 16 3823 3949 3949 3823 3949 126 17 4222 4362 4362 4222 4362 140 18 2612 2747 2747 2612 2747 135 19 2400 2484 2484 2400 2484 84 20 2298 2406 2406 2298 2406 108 Table3 a. Solution Statistics for Model 1 (Maximization) Pt Initial First Heuristic Best Best LP Obj.... In PAGE 5: ...) list the elapsed time when the heuristic procedure is first called and the objective value corresponding to the feasible integer solution returned by the heuristic. For Table3 a, the columns Best LP Obj. and Best IP Obj.... In PAGE 5: ... report, respectively, the LP objective bound corresponding to the best node in the remaining branch-and-bound tree and the incumbent objective value corresponding to the best integer feasible solution upon termination of the solution process (10,000 CPU seconds). In Table3 b, the columns Optimal IP Obj., bb nodes, and Elapsed Time report, respectively, the optimal IP objective value, the total number of branch-and-bound tree nodes solved, and the total elapsed time for the solution process.... ..."

### Table 2: Technology Mapping results

"... In PAGE 8: ... The results show that the Boolean approach reduces the number of matching algorithm calls, nd smaller area circuits in better CPU time, and reduces the initial network graph because generic 2-input base function are used. Table2 presents a comparison between SIS and Land for the library 44-2.genlib, which is distributed with the SIS package.... ..."

### Table 3: Team probabilities for randomly generated service rates.

2005

"... In PAGE 16: ... Finally, rM increases with the buffer size in all cases. Note however that for fixed B, Tables 3 and 4 show that rM decreases as M increases (in fact, when M = 10 in Table3 , then rM = 0 for all B 2 f0; 1; : : : ; 5; 10; 15; 20g). Together with Table 2, this suggests that the conjectured optimal policy is likely to have some servers grouped into teams, at least for large numbers of servers M.... ..."

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