Ant Colony Optimization in Construction Scheduling

Abstract

Activity scheduling and solution of activity networks for longest paths are of paramount importance to construction management, for they provide construction management teams with fundamental knowledge about a project, its projected duration, resource utilization and cost distribution over time. The most widely used method in analyzing such networks and solving for longest (critical) paths in activity networks is the Critical Path Method (CPM), a deterministic approach that relies on forward and backward pass calculations that exhaustively traverse the network adding activity durations to path durations to arrive at the total network duration and thus the longest path in a net work. The paper presents 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 with its theoretical roots based on the behavior 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 essen ce, ACO is inspired by the foraging behavior 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 ants that previously traversed the possible paths and the p heromone trail these ants leave behind as the traverse the paths to optimal solution. In computer implementations of the ACO algorithms, artificial ants are 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. The ACO virtual multi-agent approach is supplemented by a database management system and a custom software interface that allo ws for the merging of this artificial intelligence technique with more traditional critical path calculation (CPM) techniques. Algorithms, sample software interface, and sample case studies are also outlined. Finally, basic advantages and limitations of th e ACO method are discussed and possible future research directions are explored.