### Table 3: Main statistics of multi-commodity problems.

"... In PAGE 28: ...Experimental results Table 2 and Table3 display the main statistics of the first and second problem sets after preprocessing by LIPSOL. The first set consists of a selection of larger problems of the NETLIB collection.... ..."

### TABLE 8.2.1. Multi-commodity network flow test problems statistics

1989

Cited by 50

### Table 1: Node counts and time for instances of multi-commodity network ow problems CPLEX CPLEX + CUTS

2007

"... In PAGE 126: ... CPLEX branch-and-bound was used to solve the two mixed integer programming formulations. Table1 1: Comparison of two formulations: lower and upper bounds were returned at the end of 300s of computation time (P1) (P2) Prob LB UB LB UB E10 10 0.00 0.... In PAGE 127: ... This shows that as an integer programming formulation, with no additional cuts or heuristics added, formulation (P 2) performs better than formulation (P 1). Table1 2: Comparison of two formulations: Node counts and solve times (P1) (P2) Prob Node Count Time Node Count Time E10 10 240 1.5 56 0.... In PAGE 128: ... The time limit was 300s, so if optimal solution is not found in the allotted time for a problem the corresponding entry for solve time is 300s and node count entry is the number of nodes explored in 300s. Table1 3: Comparison of two formulations with cutting planes and heuristics: lower and upper Bounds after 300s of computation time (P1) (P2) Prob LB UB LB UB E10 10 0.00 0.... In PAGE 128: ...00 0.00 Entries in bold represent that optimal solution was found in 300 second Looking at the results from Table1 3, we can see that, with the help of cuts and heuristics, formulation (P 1) was able to provide better results than (P 2). More problems were solved to optimality and for except one, the bounds provided for the problems not solved to optimality in allotted time by formulation (P 1) were stronger than formulation (P 2).... In PAGE 129: ...Table1 4: Comparison of two formulations with cutting Planes and heuristics: node counts and computation times (P1) (P2) Prob Node Count Time Node Count Time E10 10 0 4.01 0 0.... ..."

### Table A8. Relationship of Respondent Characteristics to Probability of Using USDA, Probit Model Results, for Sources Used for Regular Reading DEPENDENT

### Table A19. Relationship of Respondent Characteristics to Probability of Using USDA, Probit Model Results, for Sources Used for Market Analysis DEPENDENT

### Table A24. Relationship of Respondent Characteristics to Point Values Given for USDA Information, OLS Model, for Sources Used for Market Analysis DEPENDENT

### Table 1. IC multi-commodity problem parameters i c(i) h(i) b(i) Distribution

2003

Cited by 6

### Table I: PERFORMANCE COMPARISON OF DIFFERENT ROUTING ALGORITHMS IN SINGLE-COMMODITY (S.C.) AND MULTI-COMMODITY (M.C.) CASES

### Table 2.3: Performance of ADP on mgrid problems The results show how the interior-point method for linear programming can be e ectively used to e ciently solve large instances of multi-commodity ow problems.

### Table 11: System Revenue and Accepted Loads for Two-Carrier Alliances

2006

"... In PAGE 67: ...2.1 Results and Insights from Two-Carrier Alliances Rounded results pertaining to the alliance optimal solution are contained in Table11 . The \Carriers quot; column indicates the class from which each carrier is selected; in instance class 1, for example, both carriers in the alliance are carriers from class C1.... ..."

Cited by 1