MOSCOPEA: Multi-objective construction scheduling optimization using elitist non-dominated sorting genetic algorithm

Managing multiple construction projects simultaneously involves sharing and allocating different types of resources, including cash, equipment, and manpower among different concurrent projects. Moreover, ensuring the availability of cash throughout the multiple projects' execution period increases the scheduling complexity. Accordingly, this paper presents the development of an automated system that optimizes the scheduling of multiple construction projects with respect to multiple objectives considering both financial and resource aspects under a single platform. The automated system is named Multi-Objective SCheduling OPtimization using Evolutionary Algorithm (MOSCOPEA). The system aims to help contractors in devising schedules that obtain optimal tradeoffs between different projects' objectives, namely: duration of multiple projects, total cost, financing cost, maximum required credit, profit, and resource fluctuations and peak demand. Moreover, it offers the flexibility in selecting the desired set of objectives to be optimized together. Finally, the developed system is tested and implemented using different case studies of different project sizes obtained from literature to demonstrate its capabilities in scheduling optimization.