Abstract: For the purpose of solving the deficiency of traditional job-shop scheduling problem(JSP), summarize some production process characteristics and constraints and establish the simulation model of flexible JSP(FJSP). To minimize the weighed sum of E/T penalties of jobs among the machines, improve the quality of the initial population and accelerate the speed of the algorithm’s convergence, the continuous ant optimization algorithm is adopted to solve the multi-variable and multi-constraint combinatorial optimization problem. In iterative, the solution set can be used as the Pareto neighborhood non-dominated sorting. Compared with the adaptive immune genetic algorithm and hybrid particle swarm optimization algorithm, the proposed algorithm can rise above efficiently such difficulties of the basic ant colony algorithm as stagnation and poor global search ability. Now the proposed method has been applied to a workshop scheduling on trial. It is shown that its effects on the improvement of processing efficiency, decreasing of production cost, and lowering the cooperation expenses are remarkable.

Key words: Due punishment, Flexible job-shop scheduling problem, Multi-objective optimization, Pareto optimal solution