### Table 3: Discrete Optimization Results

"... In PAGE 7: ... The improved GA was never applied to the latter, for reasons to be discussed later. Table3 suggests that the choice of penalty function actually dictates performance to a much larger extent than does the optimization method. With both simulated annealing and genetic algo- rithms, optimal points were located much more Table 3: Discrete Optimization Results... In PAGE 7: ... were prevalent among results obtained using f 1 . The results in Table3 are of limited utilityin comparing the performance of genetic algorithms to that of simulated annealing. The former were able to locate the global optimum slightly more often using function f 1 , and both the global and local optima slightly less often using the larger penalty functions.... ..."

### Table 1 Discrete-event simulation algorithm for stochastic event structures.

"... In PAGE 11: ... It is not di cult to check that the above described discrete-event system for stochastic event structure is a time-homogeneous GSMP. The complete simulation algorithm for stochastic event structure hE;A;Ri with E = (E;#;7!;l) is presented in Table1 . For random variable U with distribution FU let FU( ) denote a sample of U; output state si = (Ei;Ri;Hi), for i gt; 0.... ..."

### Table 5. Optimization Results using Response Surface and Kriging Models Avg. # of Avg. # of Verified

1998

"... In PAGE 8: ... The optimization results are summarized in Table 5. As shown in Table5 , each optimization problem is solved using: (a) the RS model approximations and (b) the kriging model approximations for thrust, weight, and GLOW. The optimization is performed using the Generalized Reduced Gradient (GRG) algorithm in OptdesX.... ..."

Cited by 11

### Table 5. Optimization Results using Response Surface and Kriging Models Avg. # of Avg. # of Verified

1998

"... In PAGE 8: ... The optimization results are summarized in Table 5. As shown in Table5 , each optimization problem is solved using: (a) the RS model approximations and (b) the kriging model approximations for thrust, weight, and GLOW. The optimization is performed using the Generalized Reduced Gradient (GRG) algorithm in OptdesX.... ..."

Cited by 11

### Table 1: Evidence about some of the advantages in solving SCOPs via meta- heuristics instead of using exact classical methods.

2006

"... In PAGE 2: ... In contrast, approaches based on metaheuristics are capable of finding good and sometimes optimal solutions to problem instances of realistic size, in a generally smaller computation time. Table1 lists some papers in the literature providing evidence about the advantages in solving SCOPs1 via metaheuristics instead of using exact classical methods. This survey paper is the first attempt to put under a unifying view the several applications of metaheuristics to SCOPs, and it should contribute in balancing the literature, where a number of surveys and books about solving SCOPs via classical techniques exist, but none about using metaheuristics, despite the re- search literature is already quite rich.... In PAGE 2: ... Finally, section 6 highlights the conclusions. 1Legend for the SCOPs of Table1 : VRPSD = vehicle routing problem with stochastic demands, SCP = set covering problem, TSPTW = traveling salesman problem with stochastic time windows, PTSP = probabilistic traveling salesman problem, SSP = shop scheduling problem, SDTCP = stochastic discrete time-cost problem, SOPTC = sequential ordering problem with time constraints, VRPSDC = vehicle routing problem with stochastic demands and customers.... ..."

### Table 4: Comparison of stochastic frontier and least-squares estimates Stochastic frontier Ordinary least squares

"... In PAGE 4: ... 18 Table 3: Simulated changes in production for selected variables. 19 Table4 : Comparison of stochastic frontier and least-squares estimates 20 Table 5: Estimation results from restricted models. 21 Table 6: Tested restrictions about model specification 22 Table 7: Estimated model parameters from full panel and from balanced panel 23 Figure 1: Area planted to rice and rice production in Bicol for 1978, 1983 and 1994 17 ... ..."

### Table 2: Stochastic Production Frontier and Technical Efficiency Estimates

"... In PAGE 35: ... 5. Results Estimation of the Skill Index Table2 reports the results of stochastic production frontier and technical efficiency equations that are used to construct the skill index. As explained earlier, these estimates are carried out only for the self-... In PAGE 41: ... In the empirical analysis so far, we established that only the relatively skilled tenants obtain fixed rent contracts. In this section, we use the stochastic frontier estimates [ Table2 ] to directly establish the efficiency effects of different contract types. included some observations where both share and fixed rent is 0 although hire is equal to 1.... ..."

### Table 1: Simulation results for the two-node tandem queue using the optimal IF obtained numerically.

"... In PAGE 7: ... 0 5 10 15 20 25 30 35 40 0 20 40 60 80 100 Buffer 1 contents Buffer 2 contents Figure 4: The optimal IF for the two-node tandem queue. In Table1 , we compate the efficiency of the splitting method using the naive IF and the numerically obtained optimal IF. To see what the effect is of estimating the IF via the reverse-time method, we repeated the experiment by using the estimated optimal IF instead of the true optimal IF.... ..."

### Table 1. Stochastic automata for

1999

"... In PAGE 3: ...The set of edges ?! between locations is defined as the smallest relation satisfying the rules in Table1 . The func- tion F is defined by F(xG) = G for each clock x in p.... In PAGE 6: ... Since in our semantics (cf. Table1 ) a location corre- sponds to a term, simulation can be carried out on the ba- sis of expressions rather than using their semantic repre- sentation. This means that the stochastic automaton is not entirely generated a priori but only the parts that are re- quired to choose the next step.... In PAGE 6: ...erm pi (i.e. location) and the input specification E. From term pi the set of clocks (pi) to be set is determined (by module (A) in Figure 1) and the set of possible next edges is computed according to the inference rules of Table1 (by module (B)). To compute the next valuation we only need to keep track off the last valuation vi.... ..."

Cited by 9

### Table 8. Optimization to Average Thermal Via Density

"... In PAGE 24: ... The average thermal via density of 23.9%, same as the midrange case, was used as the desired objective value for Table8 . As you can see, with the same average thermal via density, the thermal properties are improved considerably over the thermal via placements with uniform thermal via densities.... ..."