Grouping batch and sorting order (GBSO) for smelting charges is a key cycle in refined copper strip producing. To improve its scientific degree, optimization problem of GBSO for smelting charges in refined copper strip producing by artificial immune algorithm (AIA) is studied. The multi-objective optimization model for GBSO of smelting charges is established with the objectives which includes minimizing fluctuation in ingredient ratios between the nearest neighbor charges, meeting order priority requirement as much as possible, insuring order due date as much as possible, maximizing number of same brand charges and sorting order in continuous way for same order as much as possible. Some constraints are adequately considered, which includes order due data of each charge, sorting order limit for the charge with high quality requirement, sorting order limit for the charge with the high ratio of virtual orders, sorting order limit for the charge including multi-order, sorting order continuously for same brand charges, ingredient ratio limit for the charge which is sorted order as the first charge, ingredient ratio difference limit between each two nearest neighbor charges. To solve the model in easy way, some constraints are transformed into the objective function, and AIA is designed. The detail steps of AIA is designed in detail including antibody representation and encoding, affinity calculation, clone selection, antibody population updating. To validate the validity of above model and AIA, select practical typical GBSO problem of smelting charges as application instance. Application result shows that, the obtained optimal solution is better than that by heuristic method both on diversity and optimization degree. AIA is suitable for solving complex problem with requirement on solution diversity.
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