Management of resources in multiunit construction projects with the use of a tabu search algorithm

The paper presents the problem of optimal management of the resources in multiunit construction project. During the creation of the models of this kind of the project the flow-shop system is rarely used. Flow-shop system now is widely applied in modelling of industrial processes, computer systems. In the model presented in the paper flow shop system with teams of working groups is used. It allows its users to shorten the time of realization of multiunit construction projects in a significant way, because works in the project can be realized by teams of working groups of any cardinality. This is new model of multiunit construction project which has not been examined in field of scheduling of construction project yet. The presented optimization model of multiunit construction project with teams of working groups performing one type of work is NP-hard optimization problem. During the scheduling of such projects there was an artificial intelligence tool used, i.e. metaheuristic tabu search algorithm. Tabu search algorithm can provide solutions of very good quality. The paper also presents a calculation example of the above mentioned problem. The obtained in calculation example result is fully satisfactory. First published online: 23 Jun 201

Simulation-based optimisation using simulated annealing for crew allocation in the precast industry

Numerous different combinations of crew alternatives can be deployed within a labour-intensive manufacturing industry. This can therefore often generate a large number of possible crew allocation plans. However, inappropriate selection of these allocation plans tends to lead to inefficient manufacturing processes and ultimately higher labour allocation costs. Thus, in order to reduce such costs, more allocation systems are required. The main aim of this study is to develop a simulation-based multi-layered simulated annealing system to solve crew allocation problems encountered in labour-intensive parallel repetitive manufacturing processes. The ‘multi-layered’ concept is introduced in response to the problem-solving requirements. As part of the methodology used, a process simulation model is developed to mimic a parallel repetitive processes layout. A simulated annealing module is proposed and embedded into the developed simulation model for a better search for solutions. Also, a multi-layered dynamic mutation operator is developed to add more randomness to the searching mechanism. A real industrial case study of a precast concrete manufacturing system is used to demonstrate the applicability and practicability of the developed system. The proposed system has the potential to produce more cost-effective allocation plans, through reducing process-waiting times as compared with real industrial-based plans

Intelligent Systems Research in the Construction Industry

YesWith the increasing complexity of problems in the construction industry, researchers are investigating computationally rigorous intelligent systems with the aim of seeking intelligent solutions. The purpose of this paper is therefore to analyse the research published on ‘intelligent systems in the construction industry’ over the past two decades. This is achieved to observe and understand the historical trends and current patterns in the use of different types of intelligent systems and to exhibit potential directions of further research. Thus, to trace the applications of intelligent systems to research in the construction industry, a profiling approach is employed to analyse 514 publications extracted from the Scopus database. The prime value and uniqueness of this paper lies in analysing and compiling the existing published material by examining variables (such as yearly publications, geographic location of each publication, etc.). This has been achieved by synthesising existing publications using 14 keywords2 ‘Intelligent Systems’, ‘Artificial Intelligence’, ‘Expert Systems’, ‘Fuzzy Systems’, ‘Genetic Algorithms’, ‘Knowledge-Based Systems’, ‘Neural Networks’, ‘Context Aware Applications’, ‘Embedded Systems’, ‘Human–Machine Interface’, ‘Sensing and Multiple Sensor Fusion’, ‘Ubiquitous and Physical Computing’, ‘Case-based Reasoning’ and ‘Construction Industry’. The prime contributions of this research are identified by associating (a) yearly publication and geographic location, (b) yearly publication and the type of intelligent systems employed/discussed, (c) geographic location and the type of research methods employed, and (d) geographic location and the types of intelligent systems employed. These contributions provide a comparison between the two decades and offer insights into the trends in using different intelligent systems types in the construction industry. The analysis presented in this paper has identified intelligent systems studies that have contributed to the development and accumulation of intellectual wealth to the intelligent systems area in the construction industry. This research has implications for researchers, journal editors, practitioners, universities and research institutions. Moreover, it is likely to form the basis and motivation for profiling other database resources and specific types of intelligent systems journals in this area

Optimized Scheduling of Repetitive Construction Projects under Uncertainty

Uncertainty is an inherent characteristic of construction projects. Neglecting uncertainties associated with different input parameters in the planning stage could well lead to misleading and/or unachievable project schedules. Many attempts have been made in the past to account for uncertainty during planning for construction projects and many tools and techniques were presented to facilitate modelling of such uncertainty. Some of the presented techniques are widely accepted and used frequently like Project Evaluation and Review Technique (PERT) and Monte Carlo Simulation, while others are more complicated and less popular, such as fuzzy set-based scheduling. Although accounting for uncertainty has been a topic of interest for more than four decades, it was rarely attempted to account for uncertainty when scheduling repetitive construction projects. Repetitive projects impose an additional challenge to the already complicated construction scheduling process that accounts for the need to maintain crew work continuity throughout project execution. This special characteristic necessitates producing scheduling techniques specifically suited to resource driven scheduling. Therefore, the main objective of this research is to produce a comprehensive scheduling, monitoring and control methodology for repetitive construction projects that is capable of accounting for uncertainties in various input parameters, while allowing for optimized acceleration and time-cost trade-off analysis. The proposed methodology encompasses three integrated models; Optimized Scheduling and Buffering Model, Monitoring and Dynamic Rescheduling Model and Acceleration Model. The first model presents an optimization technique that accounts for uncertainty in input parameters. It employs a modified dynamic programming technique that utilizes fuzzy set theory to model uncertainties. This model includes a schedule defuzzification tool and a buffering tool. The defuzzification tool converts the optimized fuzzy schedule into a deterministic one, and the buffering tool utilizes user’s required level of confidence in the produced schedule to build and insert time buffers, thus providing protection against anticipated delays affecting the project. The Monitoring and Dynamic Rescheduling Model capitalizes on the repetitive nature of these projects, by using actual progress on site to reduce uncertainty in the remaining part of the schedule. This model also tracks project progress through comparing the actual buffer consumption to the planned buffer consumption. The Acceleration Model presents an iterative unit based optimized acceleration procedure. It comprises a modified algorithm for identifying critical units of the project to accelerate. This model presents queuing criteria that accounts for uncertainty in additional cost of acceleration and for contractor’s judgment in relation to prioritizing critical units for acceleration. Moreover, this model offers six strategies for schedule acceleration and maintains crew work continuity. Together, the three developed models offer an integrated system that is capable of accounting for uncertainty in different variables through different project stages, aiming at helping managers keep repetitive construction projects on track. The presented optimization technique is automated in an Object Oriented program; coded in C# programming language. A number of case studies are analyzed and presented to demonstrate and validate the capabilities and features of the presented methodology

Gestão de projetos repetitivos com incorporação do efeito de aprendizagem: desenvolvimento de heurísticas numa análise multiobjetivo

É amplamente aceite que a produtividade do Homem na execução de tarefas repetitivas aumenta à medida que as mesmas vão sendo efetuadas sucessivamente. Daqui se depreende o porquê de ser muito comum ouvir-se a célebre expressão de que “é a prática que leva à perfeição”. Na gestão de projetos, é costume fazer-se a alusão a esta convicção natural designando-a por efeito de aprendizagem. Reconhecendo a sua importância, esta dissertação terá como questão central o problema da gestão de projetos repetitivos, num contexto em que a possibilidade dos mesmos serem executados em paralelo coexiste com a possibilidade de colher os benefícios resultantes do efeito de aprendizagem. De facto, entrar em linha de conta com o fator aprendizagem poderá contribuir decisivamente para melhorar as estimativas de duração e custo inerentes à execução de vários projetos repetitivos sucessivamente, beneficiando a precisão dos processos de orçamentação e calendarização e, em última instância, promovendo a competitividade negocial das empresas junto dos seus parceiros de negócio/clientes. Este último aspeto torna-se essencial seja qual for a estratégia de negócio que a empresa prossiga. Sendo claro o interesse deste tema, para concretizar o objetivo desta investigação, foi utilizado um novo modelo de programação matemática multiobjetivo, desenvolvido por Gomes da Silva & Carreira (2016), que considera explicitamente a possibilidade de analisar os trade-offs estratégicos entre tempo, custo e qualidade, incidindo simultaneamente sobre o efeito de aprendizagem. Neste modelo, o gestor de projetos terá de determinar o número de equipas que irá executar cada atividade dos vários projetos repetitivos. Esta decisão implica, naturalmente, consequências diretas nas três dimensões referidas anteriormente e é da sua interação tipicamente conflituante que advém a complexidade deste problema. Devido à complexidade do modelo, foram desenvolvidas e aplicadas quatro heurísticas que têm por base algumas regras de prioridade, através das quais se pretendeu gerar aproximações à fronteira de Pareto do problema. As heurísticas foram posteriormente implementadas em dois exemplos específicos, de modo a ilustrar a sua aplicação, e foi possível verificar a sua relevância e capacidade para gerarem uma boa aproximação da fronteira de Pareto. Assim sendo, é necessária investigação adicional, no sentido de averiguar se os resultados aqui alcançados se mantêm válidos para outro tipo de redes e parâmetros