There are many methods that can be used to solve optimization problems. Genetic Algorithm(GA) is different from other methods in mathematical programming. All methods other than GA have only one solution in optimizing Computation. Solutions calculated by GA have a diverseness in solution set. This feature is useful for calculating solutions in multi-objective optimization problems.

We chose a multi-objective bin packing problem. One objective function is to minimize the number of bins used and the other is the deviation of center of gravity which is created by load pattern of items.

Items are different in size and weight. The changing packing order or methods results in  an increase in the number of bins and the center of gravity becomes larger. If the number of bins used is increasing, it results in times of driving operations in truckload transportation to increase. Finally, it connects to rising transportation costs. If the center of gravity deviates from correct position, fuel consumption gets heavier. It is a trade-off relationship between two objective functions. In the multi-objective optimization problems, it selects an appropriate solution from the solutions available.

We will show an effectiveness of GA in solving the multi-objective optimization problem in numerical simulation.