Multiobjective Construction Schedule Optimization Using Modified Niched Pareto Genetic Algorithm

Abstract

A construction schedule must satisfy multiple project objectives that often conflict with each other. While several earlier approaches attempted to generate optimal schedules in terms of several criteria, most of their optimization processes were segmented into multiple steps. Owing to such a lack of simultaneous optimization, limited alternative solutions could be searched and some trade-offs between goals could not be identified. This paper presents an optimization approach that enables a simultaneous search for an optimal construction schedule in terms of three objectives: minimization of construction duration, cost, and resource fluctuation. A multiobjective optimization (MOO) approach was adopted to generate scheduling solutions considering all those objectives. To enable a simultaneous optimization, we propose a new data structure that can compute the performances of solutions in terms of all the objectives at the same time. A Niched Pareto Genetic Algorithm (NPGA) is modified to facilitate the optimization procedure. Then the proposed optimization approach is implemented in an existing case study. The result indicates that the proposed approach has the capability to explore and generate a greater range of solutions compared to existing models. Trade-offs between all three objectives are identified, limitations and further research needs are discussed.