# Traveling Salesman Problem with Time Windows

Results obtained using Gurobi for solving the Traveling Salesman Problem with Time Windows, using the models described in Mathematical Optimization: Solving Problems using Python and Gurobi. Benchmark instances are available in this site. CPU time limited to 3600 seconds. (Click on values for selecting instance type and time window factor.)

 Performance data Dumas Dumas Dumas Dumas Gendreau Gendreau Gendreau Gendreau Gendreau Gendreau Gendreau CPU time required [20] [40] [60] [80] [80] [100] [120] [140] [160] [180] [200] Number of solution failures [20] [40] [60] [80] [80] [100] [120] [140] [160] [180] [200] Solutions [20] [40] [60] [80] [80] [100] [120] [140] [160] [180] [200]

## Solutions failed

### Instance type: Gendreau, time window factor: 160

Results obtained using Gurobi for solving the Traveling Salesman Problem with Time Windows, using the models described in Mathematical Optimization: Solving Problems using Python and Gurobi. Benchmark instances are available in this site. CPU time limited to 3600 seconds. (Click on values for selecting instance type and time window factor.)

Benchmark instances used are described in "Gendreau et al. 1998. A Generalized Insertion Heuristic for the Traveling Salesman Problem with Time Windows. Operations Research, 43, 330 - 335."

 Label Description mtz-tw model based on Miller-Tucker-Zemlin's one-index potential formulation mtz-strong based on Miller-Tucker-Zemlin's one-index potential formulation, with stronger constraints mtz-2idx based on Miller-Tucker-Zemlin's formulation, two-index potential formulation

 Instance Size mtz-tw mtz-strong mtz-2idx n20w160.001, n20w160.002, n20w160.003, n20w160.004, n20w160.005 20 0 0 0 n40w160.001, n40w160.002, n40w160.003, n40w160.004, n40w160.005 40 1 0 0 n60w160.001, n60w160.002, n60w160.003, n60w160.004, n60w160.005 60 3 2 4 n80w160.001, n80w160.002, n80w160.003, n80w160.004, n80w160.005 80 8 7 9 n100w160.001, n100w160.002, n100w160.003, n100w160.004, n100w160.005 100 13 12 14