Improved Ant Colony Optimization for One-Dimensional Bin Packing Problem with Precedence Constraints

Bin packing is a well studied problem which has many applications. In this paper, a new design of ant colony optimization (ACO) is proposed for solving the one- dimensional bin packing problem with precedence constraints (PCBPP). The proposed algorithm made use of the trail information which is deposited between the item and the item selected position, and pheromone summation rules was adopted. Furthermore, heuristic methods, which synthesis consider size of the item and the number of successors and are effective for the bin packing problem, are introduced into our ACO for quick obtaining a solution. The effectiveness of our ACO is investigated through computational results for test instances.