Construction scheduling with artificial agents and the ant colony optimization metaheuristic

The research outlined in this paper aims the development of a methodology to arrive at critical path calculations in construction networks using Ant Colony Optimization (ACO) algorithms. Ant Colony Optimization is a population-based, artificial multi-agent, general-search technique for the solution of difficult combinatorial problems. The method's theoretical roots are based on the behaviour of real ant colonies and the collective trail-laying and trail-following of its members in searching for optimal solutions in traversing multiple paths. In essence, ACO is inspired by the foraging behaviour of natural ant colonies which optimize their path from an origin (ant nest) to a destination (food source) by taking advantage of knowledge acquired by other ants that previously traversed the possible paths. In computer implementations of the ACO algorithms, artificial ants are both agents and solution-construction procedures that stochastically build solutions by considering (1) artificial pheromone trails which change dynamically at run time to reflect the agents' acquired search experience, and (2) heuristic information on the problem/network being solved. The paper outlines the fundamental mathematical background of the ACO method and a suggested possible implementation strategy for solving for longest (critical) paths in construction schedule networks.