Intelligent Optimization Algorithms: A Stochastic Closed-Loop Supply Chain Network Problem Involving Oligopolistic Competition for Multiproducts and Their Product Flow Routings
Table 4
Comparison of the optimal strategies obtained by PSO algorithm and GA in the reverse logistics of Example 2.
PSO
GA
Mean
Stdev.
Mean
Stdev.
Firm 1
Optimal percentage of product 1 returning from demand market to firm 1 via recovery center
0.5709
0.0269
0.5151
0.0110
0.5927
0.0250
0.5211
0.0226
0.6977
0.0224
0.6428
0.0145
Optimal percentage of product 2 returning from demand market to firm 1 via recovery center
0.5790
0.0317
0.5413
0.0043
0.6262
0.0218
0.6666
0.0135
0.7497
0.0204
0.7562
0.0134
Firm 2
Optimal percentage of product 1 returning from demand market to firm 2 via recovery center
0.4084
0.0352
0.4199
0.0152
0.5694
0.0244
0.5242
0.0190
0.5859
0.0313
0.5452
0.0180
Optimal percentage of product 2 returning from demand market to firm 2 via recovery center
0.4000
0.0326
0.3517
0.0158
0.5401
0.0231
0.5403
0.0182
0.6085
0.0366
0.6510
0.0066
Firm 3
Optimal percentage of product 1 returning from demand market to firm 3 via recovery center
0.3107
0.0235
0.3166
0.0080
0.4213
0.0175
0.4384
0.0138
0.5590
0.0317
0.5069
0.0249
Optimal percentage of product 2 returning from demand market to firm 3 via recovery center
0.2819
0.0215
0.2287
0.0071
0.3262
0.0318
0.3523
0.0152
0.5463
0.0176
0.5463
0.0229
Firm 4
Optimal percentage of product 1 returning from demand market to firm 4 via recovery center
0.2110
0.0315
0.2177
0.0143
0.3224
0.0293
0.3159
0.0152
0.4405
0.0297
0.4358
0.0094
Optimal percentage of product 2 returning from demand market to firm 4 via recovery center
0.1726
0.0214
0.1219
0.0080
0.2561
0.0221
0.2699
0.0220
0.3390
0.0334
0.3169
0.0162
Note: optimal percentage of product returning from demand market to firm via recovery center − optimal percentage of product returning from demand market to firm via recovery center (, , and ).
