An ant colony optimization for partner selection in virtual enterprise

Partner selection is one of the core problems in the phase of virtual enterprise (VE) creation, since the selection of right partners is crucial to the success of VE. To effectively solve the partner selection and optimization problem in VE practice, a mathematical model with the objective of minimizing the total manufacturing cost of tasks within the due date is discussed in this paper. As the objective formulation is not continuous and differential, it cannot be solved exactly by integer programming, an improved ant colony optimization algorithm (IACO) is proposed to solve the problem. A crossover operator which is usually used in genetic algorithm (GA) is introduced into IACO, so it can improve the search ability of ant colony and make consequently solution better. Finally, an illustrative example is presented to show the efficiency of the algorithm.