Multi-objective Job Shop Scheduling using a Modified Artificial Bee Colony Algorithm

To optimize job shop scheduling in smelting process of high-precision copper ingot effectually, a multiobjective model with some nonlinear constraints is established according to the existing production capacity and melting technology of smelting production line for copper ingot. Two objectives of minimizing production time and penalty value for the plans not containing some orders is formulated in this model, which can meet clients' delivery date and reduce production cost. In the paper, a novel modified artificial bee colony algorithm (MABC) is proposed firstly for multi-objective job scheduling optimization according to cooperative coevolution is the concept in symbiosis theory. A new encoding rule for the algorithm is designed to solve the scheduling model of job shop effectively. The experiment results using the actual data in production show that MABC is robust and effective. MABC can obtain better solutions compared to multi-objective particle swarm optimization (MOPSO) and nondominated sorting genetic algorithm II (NSGAII) when solving this scheduling model.