Multi-Objective Symbiotic Organisms Optimization for Making Time-Cost Tradeoffs in Repetitive Project Scheduling Problem

Abstract

Time-cost problems that arise in repetitive construction projects are commonly encountered in project scheduling. Numerous time-cost trade-off approaches, such as mathematical, metaheuristic, and evolutionary methods, have been extensively studied in the construction community. Currently, the scheduling of a repetitive project is conducted using the traditional precedence diagramming method (PDM), which has two fundamental limitations: (1) progress is assumed to be linear from start to finish; and (2) activities in the schedule are connected each other only at the end points. This paper proposes a scheduling method that allows the use of continuous precedence relationships and piece-wise linear and nonlinear activity-time-production functions that are described by the use of singularity functions. This work further develops an adaptive multiple objective symbiotic organisms search (AMOSOS) algorithm that modifies benefit factors in the basic SOS to balance exploration and exploitation processes. Two case studies of its application are analyzed to validate the scheduling method, as well as to demonstrate the capabilities of AMOSOS in generating solutions that optimally trade-off minimizing project time with minimizing the cost of non-unit repetitive projects. The results thus obtained indicate that the proposed model is feasible and effective relative to the basic SOS algorithm and other state-of-the-art algorithms.

A scenario-based robust time–cost tradeoff model to handle the effect of COVID-19 on supply chains project management

“…Mainly time-cost-tradeoff (TCT) models have been implemented in many applications (e.g. Tran et al 2019) and uncertain approaches of these tradeoff models have been developed and applied (e.g. Godinho and Paulo Costa 2020).…”

Optimizing time–cost in generalized construction projects using multiple-objective social group optimization and multi-criteria decision-making methods

“…Moreover, ant colony optimization, shuffled frog leaping, DE, symbiotic organism search, teaching learning-based optimization, etc. are among the EAs proposed for the TCT problem (Ashuri and Tavakolan, 2015; Toğan and Azim Eirgash, 2019; Tran et al , 2019; Zhang and Thomas Ng, 2012). The majority of the previous investigations consider some aspects of the TCT problem such as generating set of solutions without providing decision-making, neglecting general dependencies in project scheduling or using single objective instead of multiple objectives.…”

“…To assess comparative effectiveness of the proposed algorithm, we compared MOSGO performance with that of four widely used algorithms – the multi-objective DE (MODE) (Ali et al , 2012), the multi-objective ABC (MOABC) algorithm (Martín-Moreno and Vega-Rodríguez, 2018), multi-objective PSO (MOPSO) (Saremi et al , 2018) and the non-dominated sorting GA (NSGA-II) (Deb et al , 2002). The MOSGO is also compared to the recent developed MOEAs applied to the TCT problem including multi-objective teaching learning-based optimization (MOTLBO) (Toğan and Azim Eirgash, 2019) and multi-objective symbiotic organisms search (Tran et al , 2019). For fair comparison in all considered algorithms, two common parameters are set with population size of 100 and 200 and a maximum generation of 200 and 250 for Cases 1 and 2, respectively.…”

Resilient Scheduling as a Response to Uncertainty in Construction Projects

“…Schedule management concentrates on the processes that are essential to appropriately deliver critical project aspects, such as time, cost, resources, etc. [2] Scheduling methods which are used to establish reliable construction plans can be broadly classified into exact [3][4][5][6], heuristic [7][8][9], or metaheuristic approaches [10][11][12][13][14]. Current practices in the domain of construction planning and scheduling are oriented towards automatic schedule development and the application of specialized optimization techniques [15].…”