Optimization of construction time-cost trade-off analysis using genetic algorithms

Abstract

In the management of a construction project, the project duration can often be compressed by accelerating some of its activities at an additional expense. This is the so-called time-cost trade-off (TCT) problem, which has been studied extensively in the project management literature. TCT decisions, however, are complex and require planners to select appropriate resources for each project task, including crew size, equipment, methods, and technology. As combinatorial optimization problems, finding optimal decisions is difficult and time consuming considering the number of possible permutations involved. In this paper, a practical model for TCT optimization is developed using the principle of genetic algorithms (GAs). With its robust optimization search, the GAs model minimizes the total project cost as an objective function and accounts for project-specific constraints on time and cost. To maximize its benefits, the model has been implemented as a VBA macro program. This automates TCT analysis and combines it with standard resource-management procedures. Details of the proposed TCT model are described and several experiments conducted to demonstrate its benefits. The developments made in this paper provide guidelines for designing and implementing practical GA applications in the civil engineering domain.Résumé : Dans la gestion d'un projet de construction, la durée du projet peut souvent être comprimée en accélérant quelques unes des activités à un coût supplémentaire. Ceci est le problème de compromis entre temps et coût (timecost trade-off, TCT), qui a été beaucoup étudié dans les publications de gestion de projet. Les décisions TCT sont cependant complexes et requièrent que les planificateurs choisissent les ressources appropriées pour chaque tâche du projet, y compris la taille de l'équipe, l'équipement, les méthodes et les techniques. Faire la décision optimale pour ces problèmes d'optimisation combinatoire est difficile et prends du temps, vu le nombre de permutations possibles. Dans cet article, un modèle pratique d'optimisation TCT est développé en utilisant le principes des algorithmes génétiques (AG). Avec sa recherche d'optimisation robuste, le modèle AG minimise le coût total du projet en tant que fonction objective et tient compte de contraintes de coût et de temps spécifiques au projet. Pour maximiser ses avantages, le modèle a été mis en place par un programme macro VBA. Celui-ci automatise l'analyse CTC combinée à des procédures de gestion de ressources standard. Les détails du modèle CTC proposé sont décrits et de nombreuses expériences sont effectuées afin de démontrer ses avantages. Les développements faits dans cet article fournissent des directives pour la conception et la mise en place d'applications AG pratiques dans le domaine du génie civil.

A review of methods and algorithms for optimizing construction scheduling

“…In addressing the shortcomings of Li and Love (1997), Hegazy (1999a) developed an approach that integrated GA and the commercial scheduling software Microsoft Project 4.1 to deal with construction time-cost trade-off scheduling problem. Using the CPM engine and other functions such as resource leveling embedded in the software, resource availability is considered during the evolutionary computation process.…”

“…Construction project scheduling has received a considerable amount of attention over the last 20 years (eg, Shanmuganayagam, 1989;Adeli and Karim, 1997;Hegazy, 1999a;Zhang et al, 2006d;Fang, 2012). A plethora of methods and algorithms have been developed to address specific scenarios or problems, particularly significant practical issues such as:…”

Automated Layout Planning of Climbing Formwork System Using Genetic Algorithm

“…Genetic algorithms employ random search for locating the globally optimal solution through parallel processing [15], and represent schemes that provide alternatives for layout plans in this study. Applying principles of genetic algorithm involves five primary aspects: (1) setting the chromosome structure, (2) deciding the evaluation criteria (objective function), (3) generating an initial population of chromosome for initial solutions, (4) selecting an offspring generation mechanism as a process to generate new potential solutions by mutation, and (5) schematizing the results [16].…”

Multiobjective Optimization Using Genetic Algorithms in Time-Cost Construction Project Scheduling Problem

“…They are algorithms developed to find an acceptable near optimum solution. Heuristic methods are usually algorithms easy to understand which can be applied to larger problems and typically provide acceptable solutions (Hegazy [26]). However, they have lack mathematical consistency and accuracy and are specific to certain instances of the problem (Fondahl [19]; Siemens [22]) are some of the research studies that have utilized heuristic methods for solving TCO problems.…”