Abstract
In the field of construction management, project crashing is an approach to shortening the project duration by reducing the duration of several critical project activities to less than their normal activity duration. The goal of crashing is to shorten the project duration while minimizing the crashing cost. In this research, a novel method for construction project crashing is proposed. The method is named as novel improved differential evolution (NIDE). The proposed NIDE is developed by an integration of the differential evolution (DE) and a new probabilistic similarity-based selection operator (PSSO) that aims at improving the DE’s selection process. The PSSO has the role as a scheme for preserving the population diversity and fending off the premature convergence. The experimental result has demonstrated that the newly established NIDE can successfully escape from local optima and achieve a significantly better optimization performance.
Highlights
In the field of construction management, a construction project can be typically defined as a set of individual activities with their technical/managerial constraints
To verify the performance of the proposed algorithm, three different algorithms are used for performance comparison: the Genetic Algorithm (GA) [30], the Particle Swarm Optimization (PSO) [5], the differential evolution (DE) [13], and the Adaptive Differential Evolution with Optional External Archive (JADE) [31]
This study has proposed a novel optimization model, namely, novel improved differential evolution (NIDE), to tackle complex nature of the project crashing problem
Summary
In the field of construction management, a construction project can be typically defined as a set of individual activities with their technical/managerial constraints. The time-cost functions may consist of discontinuous pieces, that is, defined at several separated domains [5] This situation happens when an activity can Journal of Construction Engineering be performed by several methods, each of which leads to a different range of possible time and cost. The DE’s ability to deal with complex optimization problem can be hindered because it employs a greedy criterion to make decision whether or not to accept a newly created individual [13] Under this criterion, a new individual is allowed to survive if and only if it reduces the value of the cost function [22]. Some conclusions are mentioned in the last section of this paper
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