### Table 1: Overview of lower bounds (LB) and upper bounds (UB) on the com- petitive ratio of deterministic algorithms for on-line dial-a-ride problems

in information

"... In PAGE 3: ...reemptive version. These results are presented in Section 4. We notice that there is no difference between the preemptive version and the non-preemptive version of the problem if the server has infinite capacity, whence we inherit the matching lower and upper bound of 3 of the preemptive version for this case. An overview of the results is given in Table1 . For the exposition we have omitted to refer to [5] for the first 2-competitive algorithm... ..."

### Table 5.1: Overview of lower bounds (LB) and upper bounds (UB) on the competitive ratio of deterministic algorithms for online dial-a-ride problems. capacity LB UB complete ride information

### Table 3.2: Data Requirements for Estimating ADART Impacts Compared to Dial-a-Ride Impact Category Data Requirements Dial-a-Ride ADART

### Table 9: Performance of algorithms A1 and A2 for the problem class DAR50

"... In PAGE 24: ... Algorithm A1 on the other hand produces high quality solutions in an acceptable amount of computation time regardless of the problem class. The results presented in Table9 indicate that both algorithms are capable of pro- ducing high quality solutions for instances of the dial-a-ride problem. 4 DRIVE Since the general pickup and delivery problem does not capture all the characteristics of the direct transportation system at Van Gend amp; Loos BV, the branch-and-price al- gorithm cannot be applied directly.... ..."

### TABLE 12 ADVANCED PUBLIC TRANSPORTATION SYSTEMS

"... In PAGE 50: ... TABLE12 (Continued) Project Name Description Portland Smart Bus - OR This project reviews the German-made Flexible Operation Command and Control System (FOCCS) that integrates fixed-route transit, dial-a-ride minibus, and contract taxi services. Status: The investigation of FOCCS has been completed and the evaluation is currently underway.... ..."

### Table 1. Results for Feasibility Problems for a Given k (partitional clustering) and Unspecified k (hierarchical clustering)

2005

"... In PAGE 2: ... Our recent work [4] explored the computational complexity (difficulty) of the feasibility problem: Given a value of k, does there exist at least one clustering solution that satisfies all the constraints and has k clusters? Though it is easy to see that there is no feasible solution for the three cannot-link constraints CL(a,b),CL(b,c),CL(a,c) for k lt; 3, the general feasibility problem for cannot-link constraints is NP-complete by a reduction from the graph coloring problem. The complexity results of that work, shown in Table1 (2nd column), are important for data mining because when problems are shown to be in- tractable in the worst-case, we should avoid them or should not expect to find an exact solution efficiently. We begin this paper by exploring the feasibility of agglomerative hierarchical clus- tering under the above four mentioned instance and cluster-level constraints.... ..."

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### Table 1. Instances checked

1998

"... In PAGE 6: ... Lastly, we checked that these eliminants had only real roots. Table1 gives the number of instances we know have been checked. By Lemma 3.... In PAGE 6: ...able 1 gives the number of instances we know have been checked. By Lemma 3.7(ii), there is a bijection between instances of (m; p) = (a; b) and (m; p) = (b; a). Table1 also lists... ..."

Cited by 10

### Table 3 Calculation of maximum earliness and maximum tardiness for all feasible sequences Sequ-

"... In PAGE 8: ... By using the idle insert algorithm [16], the objective function reduces one unit and improves to 5, whereas se- quence 2-1-3 with the objective function 4 is obtained by a complete enumer- ation of n/1/I/ETmax. In Table3 , all feasible sequences are given. As seen, the first sequence Processing time 1 6 2 Due date 12 5... ..."

### Table 3 and 4 display a comparison between different algorithms in terms of the average and the maximum deviations, the percentages of instances to which a feasible and an optimal solutions are found. The deviations are computed from optimal solutions for the sets J10-20, and from lower bounds for J30 since no optima solutions are known to these instances.

"... In PAGE 7: ...0 55.8 - Table3 : Comparison with other heuristics. (In %).... ..."

### Table 1: Optimal and Feasible Optimal Mappings for FFT-Hist

1995

"... In PAGE 9: ... Both modules are replicated maximally subject to this constraint. colffts rowffts hist Module 1 Module 2 Number of instances 3 8 4 10 ri: Processors per instance pi: Mapping onto a 64 processor mesh Figure 6: FFT-Hist program mapping(256, Message) Table1 shows the optimal mappings for the four ver- sions of FFT-Hist predicted by the mapping algorithm. In all cases, module 1 contains colffts and module 2 con- tains rowffts and hist, but the number of processors assigned to each instance of the module (pi) and the number of replicated module instances (ri) vary.... ..."

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