Construction management everywhere faces problems and challenges. Resource scheduling is a crucial part of project planning of any management companies. Successful tradeoff optimization resource scheduling problems within the project scope is necessary to maximize overall company benefits. This study investigated the potential use of various advanced techniques to improve multiple objective Differential Evolution. Three hybrid multiple objective Differential Evolution (MODE) algorithms that integrate chaotic maps, opposition-based learning technique and Artificial Bee Colony are introduced to solve the resource scheduling problems. Firstly, chaotic initialized adaptive multiple objective Differential Evolution (CAMODE) model is presented. CAMODE utilizes the advantages of chaotic sequences for generating an initial population and an external elitist archive to store non-dominated solutions found during the evolutionary process. In order to maintain the exploration and exploitation capabilities during various phases of optimization process, an adaptive mutation operation is introduced. Secondly, opposition-based Multiple Objective Differential Evolution (OMODE) model is presented. OMODE employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. Finally, a new hybrid multiple-objective artificial bee colony with differential evolution (MOABCDE) model is proposed. The proposed algorithm integrates crossover operations from differential evolution (DE) with the original artificial bee colony (ABC) in order to balance the exploration and exploitation phases of the optimization process. Numerous real construction case studies including time-cost-quality tradeoff, time-cost tradeoffs in resource-constrained, time-cost-labor utilization tradeoff and time-cost-environment impact tradeoff problems are used to demonstrate the proposed models. The proposed models are validated by comparing with current widely used multiple objective algorithms, including the non-dominated sorting genetic algorithm (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC) and previous works via comparison indicators and hypothesis test. Experimental results obtained from the proposed models confirm that using the newly established models can be a highly beneficial for decision-makers when solving various problems in the field of construction management.

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