Performance evaluation of ant colony optimization-based solution strategies on the mixed-model assembly line balancing problem

The aim of this article is to compare the performances of iterative ant colony optimization (ACO)-based solution strategies on a mixed-model assembly line balancing problem of type II (MMALBP-II) by addressing some particular features of real-world assembly line balancing problems such as parallel workstations and zoning constraints. To solve the problem, where the objective is to minimize the cycle time (i.e. maximize the production rate) for a predefined number of workstations in an existing assembly line, two ACO-based approaches which differ in the mission assigned to artificial ants are used. Furthermore, each ACO-based approach is conducted with two different pheromone release strategies: global and local pheromone updating rules. The four ACO-based approaches are used for solving 20 representative MMALBP-II to compare their performance in terms of computational time and solution quality. Detailed comparison results are presented.