Optimized resource-constrained method for project schedule compression

Abstract

PurposeThis paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes.Design/methodology/approachThe proposed method utilizes fuzzy set theory (FSs) for modeling uncertainties associated with activities' duration and cost and genetic algorithm (GA) for optimizing project schedule. The method has four main modules that support two optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; and decision-support module. The first optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the second uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization methods are coded in python as a stand-alone automated computerized tool to facilitate the developed method's application.FindingsThe developed method is applied to a numerical example to demonstrate its use and illustrate its capabilities. The method was validated using a multi-layered comparative analysis that involves performance evaluation, statistical comparisons and stability evaluation. Results indicated that NSGA-II outperformed the weighted optimization method, resulting in a better global optimum solution, which avoided local minima entrapment. Moreover, the developed method was constructed under a deterministic scenario to evaluate its performance in finding optimal solutions against the previously developed literature methods. Results showed the developed method's superiority in finding a better optimal set of solutions in a reasonable processing time.Originality/valueThe novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting.

Application of a Genetic Algorithm for Proactive Resilient Scheduling in Construction Projects

“…Due to its versatility and usability, the NSGA-II has become one of the standard approaches to solving various scheduling problems in the construction industry, which has been tested in different studies [16]. Some of the recent models in the construction scheduling which have been solved by NSGA-II focus on optimizing other important aspects along with the project makespan minimization, such as material procurement [17,18], crew planning [19], project prioritization [20], location and workspace congestion [21], as well as project financing [22]. Due to metaheuristics' efficiency in similar construction scheduling problems, we decided to apply the NSGA-II algorithm from the open-source framework pymoo [23] that allows customization of the algorithm to the user needs.…”

“…Designs 2022, 6, x FOR PEER REVIEW 4 of 19 [17,18], crew planning [19], project prioritization [20], location and workspace congestion [21], as well as project financing [22]. Due to metaheuristics' efficiency in similar construction scheduling problems, we decided to apply the NSGA-II algorithm from the opensource framework pymoo [23] that allows customization of the algorithm to the user needs.…”

Time-Cost Tradeoff and Resource-Scheduling Problems in Construction: A State-of-the-Art Review

“…Elkabalawy and Moselhi [23] proposed an integrated method for resource-constrained schedule compression under uncertainty with option of choosing an optimal solution from a set of non-dominated solutions. Fuzzy set theory (FSs) is used to model uncertainty in activity durations and costs, while GA is used to optimize project schedule.…”

“…Moreover, improvements provide the following; a multi-objective optimization within uncertainty that addresses TCT and allows for activity splitting and providing a smooth resource utilization; 2D Pareto front for project duration and cost to help managers in selecting the optimum solution; and a stand-alone automated tool may be used to schedule compression. The major gap of the method by Moazz Elkabalawy and Moselhi [23] is excluding the multi-resource constrained scheduling. Besides, all noncritical activities can be split, and excluding multiskilled strategy as well as activity splitting would help in solve resource-constrained problem.…”

Multi-objective Optimisation Model for Resilient Construction Scheduling under Uncertain Conditions

“…Navedeni algoritam u praktičnoj je upotrebi za rješavanje različitih problema planiranja u građevinarstvu, npr. za planiranje rasporeda radne snage [74], organizaciju radnog prostora [79], nabavu materijala [73,76], financijsko planiranje [75], ali je zastupljen i u širim područjima višeciljne optimizacije [81]. Zbog svoje primjenjivosti i dokazane efikasnosti odabran je kao konkretni način rješavanja predloženog optimizacijskog problema, uz nužnu prilagodbu pojedinih algoritamskih procesa (inicijalizacija, evaluacija,  Jednadžba (4.4) definira relativno trajanje aktivnosti 𝐷𝑃 𝑖 koje se dobije dijeljenjem trajanja 𝑑 𝑖 aktivnosti i sa sumom trajanja svih aktivnosti u projektu.…”