Research Article
Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing
| | Input: | | (1) | max_Iter: the maximum iteration | | (2) | nP: the particle size | | (3) | nR: the size of external repository | | (4) | : the lth particle inertia weight | | (5) | c1l: the lth particle personal learning coefficient | | (6) | c2l: the lth particle global learning coefficient | | (7) | nG: the count of grids per dimension | | | Output: the optimal solutions | | (1) | Initialize particles in the swarm (position and velocity) | | (2) | Evaluate each particle by calculating the corresponding objective function | | (3) | Initialize the external repository using nondominated solution maintaining | | (4) | Iter = 0 | | (5) | For Iter = 1 : max_Iter | | (6) | For each particle | | (7) | Update each particle’s position and velocity | | (8) | Evaluate the newly generated particle | | (9) | Update the pbest,l | | (10) | End for | | (11) | Update the external repository and control the repository size | | (12) | Select from the external repository by adaptive grids | | (13) | Adjust the parameters by self-adaptive flight parameter mechanism | | (14) | Iter = Iter + 1 | | (15) | End for | | (16) | Report the optimal results |
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