Optimized crew selection for scheduling of repetitive projects

Abstract

PurposeThe purpose of this paper is to identify optimum crew formations at unit execution level of repetitive projects that minimize project duration, project cost, crew work interruptions and interruption costs, simultaneously.Design/methodology/approachThe model consists of four modules. The first module quantifies uncertainties associated with the crew productivity rate and quantity of work using the fuzzy set theory. The second module identifies feasible boundaries for activity relaxation. The third module computes direct cost, indirect cost and interruption costs, including idle crew cost as well as mobilization and demobilization costs. The fourth module identifies near-optimum crew formation using a newly developed multi-objective optimization model.FindingsThe developed model was able to provide improvements of 0.2, 16.86 and 12.98% for minimization of project cost, crew work interruptions and interruption costs from US$1,505,960, 8.3 days and US$8,300, as recently reported in the literature, to US$1,502,979, 6.9 days and US$7,222, respectively, without impacting the optimized project duration.Originality/valueThe novelty of this paper lies in its activity-relaxation free float that considers the effect of postponing early finish dates of repetitive activities on crew work interruptions. The introduced new float allows for calculating the required crew productivity rate that minimizes crew work interruptions without delaying successor activities and without impacting the optimized project duration. It safeguards against assignment of unnecessary costly resources.

Scenario‐oriented repetitive project scheduling optimization

“…Objective reduction is realized by assigning a weight to each optimization objective and then using an evolutionary algorithm for problem‐solving. In recent years, the development of nature‐inspired algorithms, including harmony search (Geem, 2010; Siddique & Adeli, 2015), simulated annealing (Anagnostopoulos & Kotsikas, 2010; Siddique & Adeli, 2016a), particle swarm optimization (Aminbakhsh & Sonmez, 2017; Hossain et al., 2019), weighted‐sum multi‐objective GA (Agrama, 2014; Arabpour Roghabadi & Moselhi, 2020; Salama & Moselhi, 2019), gravity search (Siddique & Adeli, 2016b), bacteria foraging (J. Wang et al., 2018), and spider monkey optimization (Akhand et al., 2020), provides solid support for this method. Multi‐objective evolutionary algorithm. Examples include multi‐objective genetic algorithm (MOGA) (Altuwaim & El‐Rayes, 2021; H.‐G.…”

“…In recent years, the development of nature-inspired algorithms, including harmony search (Geem, 2010;Siddique & Adeli, 2015), simulated annealing (Anagnostopoulos & Kotsikas, 2010; Siddique & Adeli, 2016a), particle swarm optimization (Aminbakhsh & Sonmez, 2017;Hossain et al, 2019), weighted-sum multi-objective GA (Agrama, 2014;Arabpour Roghabadi & Moselhi, 2020;Salama & Moselhi, 2019), gravity search (Siddique & Adeli, 2016b), bacteria foraging (J. Wang et al, 2018), and spider monkey optimization (Akhand et al, 2020), provides solid support for this method.…”

Review of Optimization Dynamically Applied in the Construction and the Application Potential of ICT

“…Bakry et al use fuzzy set theory for modelling uncertainties and then optimize the repetitive construction project schedule [91]. In [92], the uncertainties associated with the crew productivity rate and quantity of work are expressed by fuzzy set theory. In [93], fuzzy sets are used to consider the effects of time and cost uncertainties on construction works.…”

“…Mathematic model [82] duration and cost predetermined project scheduling GA [83] task duration predetermined project scheduling SBO [84] cost-time function predetermined project scheduling GA [85] space-time float predetermined optimize the duration LP [86] quality, cost and schedule function predetermined schedule cost optimization GA [87] weights between time, cost, quality and resources predetermined project Scheduling PSO [88] geometries of facilities predetermined facilities layout GA [89] resources uncertainties predetermined project Scheduling PSO Fuzzy theory [90] task duration, material supply predetermined material supply chain LP [91] work quantities, crews' productivities and costs predetermined project Scheduling GA [92] crew productivity predetermined project Scheduling GA [93] time and cost predetermined project Scheduling GA [94] human bias and uncertainty predetermined resource-allocation SBO [95] equipment failure rate predetermined project Scheduling DP-based GA [96] interaction cost and operating cost of facilities predetermined project Scheduling PSO [97] transportation cost of facilities predetermined project Scheduling DP-based PSO…”

Non-Sequential Linear Construction Project Scheduling Model for Minimizing Idle Equipment Using Constraint Programming (CP)

“…Repetitive projects can be divided into linear and non-linear projects according to the linear geometric pattern [12]. In terms of linear projects, this type has repetitive units-a sequence of construction activities [13].…”