Optimizing Construction Project Labor Utilization Using Differential Evolution: A Comparative Study of Mutation Strategies

Abstract

In construction management, the task of planning project schedules with consideration of labor utilization is very crucial. However, the commonly used critical path method (CPM) does not inherently take into account this issue. Consequently, the labor utilization of the project schedule derived from the CPM method often has substantial low ebbs and high peaks. This research proposes a model to obtain project schedule with the least fluctuation in labor demand while still satisfying the project deadline and maintain the project cost. The Differential Evolution (DE), a fast and efficient metaheuristic, is employed to search for the most desirable solution of project execution among numerous combinations of activities’ crew sizes and start times. Furthermore, seven DE’s mutation strategies have also been employed for solving the optimization at hand. Experiment results point out that theTarget-to-Best 1and a new hybrid mutation strategy can attain the best solution of project schedule with the least fluctuation in labor demand. Accordingly, the proposed framework can be an effective tool to assist decision-makers in the project planning phase.

Fuzzy Input Parameters Influence on the Oscillation of a 2D Frame Structure Subjected to Harmonic Dynamic Load

“…The DE algorithm is described in Figure 2. 3: For g = 1: G 4: Assess the population and identify the best individual xbest 5: For i = 1: P 6: Identify the instance mother xi 7: Generate 3 random positive integers r1, r2, r3 8: Determination of mutation coefficient F = N (0.5, 0.22) and the probability of hybridization Cr = 0.8 9: Create mutant vectors that follow (9a) or (9b) DE/rand/1: To improve the optimization of the differential evolution algorithm in Figure 2, Hoang's research [12] has proposed a new method of optimizing "Optimization of mixed mutant differential evolution -HCDE". HCDE has basic steps (population initialization, hybridization, selection) similar to conventional DE methods.…”

“…HCDE has basic steps (population initialization, hybridization, selection) similar to conventional DE methods. However, in the mutation step of individuals, Hoang [12] proposed a new mutation equation, This new equation is the combination of equations (9a) and (9b). The new approach helps to speed up the convergence of the algorithm while avoiding the search process from falling into a locally optimized solution.…”

A Combined Modal Correlation Criterion for Structural Damage Identification with Noisy Modal Data

“…Among the plethora of such algorithms, the differential evolution (DE) algorithm is adopted in this study to solve the structural damage identification problem of Section 3.1. DE is considered a fast and efficient metaheuristic having gained increased popularity in the engineering optimization community, with numerous applications in various research fields [24].…”

Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning

“…The DE algorithm [22] is unquestionably one of the most powerful approaches for solving complex optimization problems [44,45]. Successful applications of the DE optimization algorithm as well as other metaheuristic approaches for solving complex or ill-defined engineering problems have been observed in various fields [46][47][48][49][50][51]. Given that the problem of interest is to minimize a cost function (X), where the number of decision variables is D, the DE algorithm can be described in Algorithm 1.…”