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Flowshop scheduling optimization for multi-shift precast production with on-time delivery
... In some mature economies, such as America, Germany, Australia, and Canada, prefabricated buildings have been widely promoted due to their reliable quality, rapid construction, environmental friendliness, and intelligent technology [5][6][7][8]. In some emerging economies, such as China, Turkey, Pakistan, and Albania, with the implementation of urbanization strategies, prefabricated buildings are increasingly valued for their excellence [9][10][11][12]. The building sector dominates energy consumption and carbon emissions in cities [13], and in China, prefabricated buildings are considered an effective way to improve the transformation and upgrading of the construction industry, achieve green progression in urban and rural construction, and realize intelligent operation throughout the entire life cycle of buildings. ...
... The curing chamber is a device with numerous stations, occupying a large space. In addition, the curing chamber requires a daily supply of high-temperature steam to maintain the normal operation of the curing process, resulting in high operating costs [9]. To fully utilize this valuable equipment and achieve more reasonable space planning, it is common for multiple production lines to share the curing chamber. ...
... At present, scholars have investigated the single-line precast production scheduling issue [9,[27][28][29][30] and the integrated scheduling issue of single-line precast production and transportation [25,26,31,32]. These studies have not addressed the cooperative scheduling issue of multiple production lines, whereas multiple lines exist in the real-world production environment [24]. ...
With the increasing popularity of prefabricated buildings, more and more precast plants have been equipped with multiple production lines to increase productivity and meet the growing market demand. Sharing equipment, human, and transportation resources is a typical feature of integrated scheduling management for precast production and transportation on multiple production lines. In response to these characteristics, this article studies the integrated scheduling optimization of multi-line production and transportation for prefabricated components. With on-time delivery and lower costs as the goals, a scheduling optimization mathematical model is established for this scenario. This article adopts the genetic algorithm to design the solution algorithm for this model, and the effectiveness of the model and algorithm is verified through an example. The results show that compared with the traditional scheduling scheme, this method can prominently reduce costs while promoting on-time delivery. The model and method can help the precast plant with multiple production lines improve efficiency and reduce costs, as well as enhancing the practicability of the precast production and transportation scheduling scheme.
... This method is particularly effective for addressing multi-objective optimisation challenges. Notable applications include balancing resource utilisation and production time [63,113,114], adapting multiple production lines to frequent changes in precast component types [115], and refining multi-shift scheduling to respond to sudden surges in orders [116]. Moreover, several innovative algorithms have demonstrated their effectiveness in optimising scheduling for the challenging task of maintaining continuous production in a fixed-station production model. ...
... Flowshop scheduling optimisation for multishift precast production with on-time delivery Scheduling optimisation AI (Genetic algorithm) A novel flowshop scheduling optimisation model for multi-shift precast production, providing a theoretical basis to adjust production schedules for time-pressured orders efficiently [116] 16 Framework for modelling operational uncertainty to optimise offsite production scheduling of precast components Scheduling optimisation AI (Genetic algorithm) A novel hybrid model that combines genetic algorithms with discrete event simulation to more accurately represent real-world precast production environments [63] 17 ...
The integration of automation technologies has improved the efficiency of industrialised construction (IC), yet a deeper understanding of their effects on the manufacturing and assembly stages remains necessary. This paper provides a systematic review of how various automation technologies influence these key stages in IC, analysing 53 articles. It explores the deployment of 22 technologies, including the Internet of Things (IoT), deep learning, digital twins, and robotics, and identifies seven benefits for IC: (1) interoperability, (2) scheduling optimisation, (3) production traceability, (4) production safety, (5) manufacturability, (6) quality assurance, and (7) con-structability. To further advance automation in IC, future research should address critical challenges, including enhancing data quality, expanding empirical testing, exploring emerging technologies in depth, and integrating fragmented workflows. This article underscores the need of strategic technology deployment to seamlessly integrate various processes in future construction practices, offering insights into the transformative potential of automation.
... Minimization of the delivery penalty, type change of precast components during production Multiple production lines Yang et al. [46] Minimization of the makespan, contract penalty, and storage cost, workstation idle time, material re-dispatch complexity and workload, and overassigned time for production emergencies Rescheduling using the overassigned time Ma et al. [47] Minimization of the total penalty cost of earliness and tardiness Process connection and blocking Dan et al. [48] Minimization of the sum of the production cost and early penalty cost and the days with two shifts Multi-shift production Dan et al. [49] Minimization of the makespan, and total penalty cost of earliness and tardiness Parallel work of serial machines Liu et al. [50] Minimization of the processing cost, transportation cost, penalty cost for the tardy quantities, and processing costs and transportation costs for demand that cannot be completed on time Multiple fabrication shops Ho et al. [51] The above existing methods, however, show limitations in dynamic environments, where factors like labor productivity and equipment availability are variable. To address these challenges, Kim et al. developed a dynamic model using discrete-time simulation methods, incorporating new rules to account for deadline uncertainties and minimize delays in real time [52]. ...
... In recent years, the increasing demand for off-site construction projects, combined with a substantial diversity of components due to varied architectural designs, has exerted significant pressure on precast production, a critical stage in OSC (Yuan et al. 2022). In practice, component factories commonly utilize mixed-flow production methods to enhance production efficiency (Dan et al. 2024). Exploiting the similarity in precast production processes, mixed-flow production methods can now produce a group of diverse components in one pallet, significantly enhancing production efficiency and effectively addressing challenges posed by component diversity (Li et al. 2018). ...
Faced with immense pressure to reduce environmental impact, off-site construction (OSC) is considered a sustainable alternative to conventional practices. However, challenged by component diversity and a significant surge in demand, deficient or empirical-based scheduling management struggles to effectively harness the potential of mixed-flow precast production to improve efficiency, instead resulting in environmental impacts, and falling short of expected benefits in OSC projects. Therefore, this study addresses the conflict between efficiency and environmental impact arising from the application of mixed-flow precast production by integrating multi-objective optimization and group technology. A multi-objective optimization framework is proposed, incorporating grouping technology for mixed-flow precast production scheduling and aiming to minimize carbon emissions and reduce tardiness/earliness penalty. The non-dominated sorting genetic algorithm II (NSGA-II), adjusted by adaptive population initialization strategy and group technology, is introduced to solve this problem, striking a balance between sustainability and penalty costs. Through a real-case analysis, the proposed approach demonstrates an average reduction of 37.5% in carbon emissions compared to rule-based scheduling methods, a 30.1% reduction compared to previous research methods, along with over 10% reduction in tardiness/earliness penalty. This study enhances environmental benefits and efficiency from a production scheduling perspective and establishes an automated, practical method, fostering low-cost, high-efficiency green production for construction component enterprises, particularly for small and medium-sized enterprises, thereby promoting sustainable development in the construction industry.
The capabilities of artificial intelligence (AI) in managing complex problems are increasing in construction. Particularly for offsite and modular construction (OMC). However, the knowledge landscape of AI applications in OMCremains fragmented, hindering the understanding of current developments and critical areas for advancing AI-in-OMC. Therefore, this paper presents a comprehensive overview of AI applications in OMC using a mixedmethod review approach to identify key application areas of AI-in-OMC and under-researched areas. The findings reveal that the convolutional neural network (CNN) is the most prominent AI technique adopted, followed by artificial neural network (ANN). Prominent issues regarding AI-in-OMC include productivity and site safety. Further, the findings reveal patterns of different AI techniques solving similar research problems at each stage of OMC. Research areas to improve AI-in-OMC include AI-circular economy outcomes, sound and image data integration and transfer learning.
Planning problems are particularly important for the production processes of precast reinforced concrete elements. Currently used modeling of these processes is based on the flow shop problem. Flow shop models are usually used in Enterprise Resource Planning systems, which, however, may not take into account the specifics of the production of such elements. The article presents a new model for scheduling the production of reinforced concrete prefabricated elements, which is distinguished by the possibility of carrying out activities by more than one working group. An additional new constraint is the possibility of parallel performance of some works, which may occur during their production. Also, there will be an individual order of elements assumed for each of the activities. New objective functions will be considered – the sum of idle times of working groups and the total type changes of precast components. The presented scheduling model contains an NP-hard discrete optimization problem. For this reason, metaheuristics were used in the article to solve optimization problems: the simulated annealing algorithm and the tabu search algorithm. Verification of the results obtained with the use of these algorithms confirmed their high efficiency. The application of the presented scheduling model illustrates a practical case study showing the effectiveness of the used algorithms.
Because the traditional logistics transportation cost optimization algorithm of prefabricated building components has the problems of long transportation time and high transportation cost, a logistics transportation cost optimization algorithm of prefabricated building components for project management is proposed. A project management oriented prefabricated building component management system is built, the logistics and transportation process of prefabricated building components are analyzed, and the logistics vehicles of prefabricated building components under the time window are scheduled. According to the scheduling results, through the waiting unloading time cost, transportation cost, and penalty cost, the total cost objective function is built to obtain the optimal cost. The simulation results show that the proposed algorithm has the shortest logistics transportation time and the lowest transportation cost.
This research addresses the problem of assembly scheduling in crane-assisted precast construction while considering issues of building layout interference and optimal crane lifting. Traditionally, assembly scheduling and lifting path planning are treated as two separate issues due to their distinct natures. The current work introduces an approach that combines them for precast construction planning to achieve a comprehensive and cost-effective solution. A BIM4D-based Intelligent Assembly Scheduler (BIAS) is designed in conjunction with the Computer-Aided Lifting Planner developed at Nanyang Technological University, Singapore. BIM4D is the Building Information Modeling (BIM) with the time dimension (i.e., scheduling information). Our scheduler takes an inbuilt timeframe for selected precast elements from BIM4D as input and outputs the micro-schedule in terms of the assembly sequence for these precast elements. This problem is solved using multi-objective optimization. Given a group of precast elements and their BIM4D timeframe, the micro-scheduling is determined based on (1) the relative importance of the elements’ physical properties, (2) the interference (neighbouring relation) among the elements’ positions, and (3) collision-free lifting paths of the elements. A Multi-level Elitist Genetic Algorithm (MEGA) is proposed to determine the optimal sequence taking into consideration of both assembling and lifting for the elements. A case study is performed with the BIM4D data of a residential building. The results of the case study demonstrate BIAS's efficiency and effectiveness for BIM4D based construction scheduling.
The increasing number of prefabrication projects has increased the demand for precast concrete (PC) components. The production cost of PC components significantly affects the development of the precast industry and the progress of prefabrication projects. To reduce the production cost, both the delivery delay time and component storage time must be reduced. Flow-arrangement optimization is generally performed using the genetic algorithm. However, this method cannot always yield a perfect optimal solution. Moreover, the traditional optimization model does not consider the impact of the overtime hours of workers on the project costs. In this study, a mixed-integer linear programming (MILP) model was developed to optimize the production scheduling by minimizing the storage and delay times. The total delay time for the components was reduced by 55.3%, from 3.8 to 1.7 h, and the total storage time for finished components was reduced by 20.3%, from 6.4 to 5.1 h. Then, the use of the MILP model was extended to optimize the production scheduling by minimizing overtime. Finally, the feasibility and effectiveness of MILP were verified by comparing the results. The total overtime decreased by approximately 24.5%, from 11.5 to 9.3 h. It has been demonstrated that the proposed MILP model can achieve a better production sequence with less overtime. The findings of this research can be deployed in optimizing efficiency in the real-life scheduling of production sequence.
Precast components manufacturer generally operates under limited production capacity and produces products of one order which may delay another. This paper develops a precast component order acceptance and scheduling model that aims to maximize the total profit in a stochastic multiple orders environment. In that model, the increasing of the overall profit of the precast component manufacturer is achieved by using a heuristic algorithm and a dynamic order acceptance heuristic. Results of numerical examples indicate the proposed model realizes the increasing total profit in most cases comparing to accept all of the orders. Besides, this study tested three order acceptance criteria and found that the profit-based criterion is to be more stable in terms of maximum total profit. This approach is anticipated to provide support to precast component manufacturers when faced with multiple orders in long-term production.
Due to the dynamic nature, complexity, and interactivity of production scheduling in an actual business environment, suitable combined and hybrid methods are necessary. This paper takes prefabricated concrete components as an example and develops the dynamic decision support framework based on a genetic algorithm and multiagent system (MAS) to optimize and simulate the production scheduling. First, a multiobjective genetic algorithm is integrated into the MAS for preliminary optimization and a series of near‐optimal solutions are obtained. Subsequently, considering the resource constraints and uncertainties, the MAS is used to simulate complex real‐world production environments. Considering the different types of uncertainty factors, the paper proposes the corresponding dynamic scheduling method and uses MAS to generate the optimal production schedule. Finally, a practical prefabricated construction case is used to validate the proposed model. The results show that the model can effectively address the occurrence of uncertain events and can provide dynamic decision support for production scheduling. A dynamic decision support framework based on a genetic algorithm and multiagent system (MAS) for production scheduling is proposed. A variety of agents are set up in the MAS to increase the likeness of the experimental environment to the real production environment. Dynamic scheduling methods are proposed for different uncertain factors, and the effectiveness of the proposed method is verified by experiments.
Precast concrete components production in the construction industry is a highly labor-intensive and experience oriented process. This research aims to explore the association rules of precast production procedures in order to optimize production efficiency and costs. Precast concrete production data from 2008 to 2017 in Taiwan were collected and a database on three major construction components, namely columns, girders, and beams, was established. A total of 18,854 columns, 19,558 beams, and 16,731 girders were found, and each component contains a 14-step production procedure. The Pearson correlation analysis was carried out to determine the relationship between the production procedures, and the Apriori algorithm was adopted with the confidence level and support value of the parameters set at 90% and 14%, respectively. The results revealed that the lofting process significantly influences other procedures for girder production, while for beam and column productions, item pre-installation significantly influences other procedures.
Prefabrication‐based construction has several advantages, such as short building time and superior quality. The transportation of prefabricated products from factories to construction sites has two key issues to consider. One is how to efficiently utilize the capacity of trucks in order to use as few trucks as possible, and the other is how to postpone the transportation of prefabricated products so as to pay for them as late as possible. This study proposes a mathematical programming model to optimize the transportation planning of prefabricated products to minimize the sum of trucking transportation costs and inventory holding costs. Extensive numerical experiments show that the optimal transport plan generated by the model outperforms the plan obtained by a greedy approach by 10% of cost savings, implying the effectiveness of the model for the construction industry.
Purpose
This study applied the lean approach to the reinforcement work process in the component production of industrialised building system precast concrete construction (IBSPCC). The purpose of this paper is to identify and eliminate non-value added (NVA) activities to enhance the efficiency of the production process.
Design/methodology/approach
Data were collected via a case study of six-storey precast concrete building. A mapping of the reinforcement work process was conducted based on observations using time study technique and time-lapsed video, complemented by semi-structured interviews.
Findings
Through this application, several NVA activities, such as unnecessary inventory, excessive movement and coordination issues, were identified. Production performance could be enhanced by implementing Just-in-Time, Kanban, and layout improvements, which would address NVA activities.
Research limitations/implications
Due to the complexity of the construction process, only specific process elements were observed. To map the complete process, comprehensive observation must be conducted from beginning to end, which, though worthwhile, would be very time and resource intensive.
Originality/value
This paper focusses on strategies for improving the efficiency of the IBSPCC production process in Malaysian construction by developing a conceptual framework of the lean approach for the reinforcement work process. Certain aspects in the process such as layout and inventory need to be redesigned and simplified by minimising NVA activities.
Multiskilling allows for dynamic reallocation of workers from one stage of production to another in response to bottleneck configurations and, therefore, has been advocated as a potential strategy to improve productivity in off-site construction. The aim of this research is to investigate the extent to which operational benefits associated with different resource management policies pertaining to bottleneck configurations can be achieved in off-site construction. To this end, the flow shop environment is recognized as an appropriate operational framework for modeling production dynamics. A quadratic resource allocation model was developed to expose different operational performances corresponding to different resource management strategies. Different resource management policies included no cross-training, hiring single-skilled crew, direct capacity balancing, chaining, and hiring multiskilled crew. Operational performance encompassed makespan and labor costs. Production data from a prefabrication factory based in Melbourne, Australia, were fed to the model, providing the basis for comparison of different resource management policies. Research findings contribute to resource planning and management in off-site construction.
Prefabricated construction has been widely accepted as an alternative to conventional cast-in-situ construction, given its improved performance. However, prefabricated concrete building projects frequently encounter significant delays. It is, therefore, crucial to identify key factors affecting schedule and explore strategies to minimise the schedule delays for prefabricated concrete building projects. This paper adopts the decision-making trial and evaluation laboratory (DEMATEL) model and analytic network process (ANP) method to quantify the cause-and-effect relationships and prioritise the key delay factors in terms of their importance in the Chinese construction industry. The DEMATEL model evaluates the extent to which each factor impacts other factors. The quantified extents are then converted into a prioritisation matrix through ANP. The delay factors of prefabricated construction projects are selected and categorised based on a literature review and an expert interview. Questionnaires are then implemented to collect the data. The results reveal that the issue of inefficient structural connections for prefabricated components is found to be the most significant factor and most easily affected by other delay factors. This research also suggests prioritising major delay factors, such as ‘lack of communication among participants’ and ‘low productivity’, in the Chinese construction industry during scheduling control. Overall, this research contributes an assessment framework for decision making in the scheduling management of prefabricated construction.
To enhance the level of precast production management, and to reduce the costs of precast production, a lean precast production system is developed based on the constant work-in-process (CONWIP) method. Then the performance of this system is verified using the Discrete Event Simulation (DES). A quantitative performance evaluation between the lean precast production system and the current precast production process is provided using the JaamSim simulation platform. Cases and site visits to precast concrete plant in China are combined to collect the production procedures and initial parameters involved in precast production. Through simulations, Non-value Adding (NVA) activities, as well as bottleneck workstations, are identified in precast production. The optimal number of cards to be used in the lean precast production system is also presented. Our simulation experiments show that the lean precast production system provides an effective way to control queue length and shorten the WIP and cycle time, while also maintaining throughput and saving 25.4% of labour cost.
The architecture, engineering and construction industry has long dealt with problems such as schedule and budget overruns, quality and safety issues, and low productivity. Off-site construction, which is a hybrid of manufacturing and construction, has significant potential to address industry’s endemic problems. However, off-site construction has been criticized for replicating the traditional subcontracting approach and therefore fragmented practice in the construction industry. The current research focuses on process integration and cross-training of multi-skilled resources as a solution to the aforementioned problem. To identify optimal process integration architectures for off-site construction, production data of three off-site manufacturers were analyzed by using a hybrid of fuzzy theory and the technique for order of preference by similarity to ideal solution. Findings reveal that process integration architectures transferring excess capacity from under-utilized to over-utilized resources in direct or indirect pathways are preferable in terms of satisfying decision criteria such as time and cost of cross-training, skill transferability, compliance with network logic, and safety considerations. The study contributes to the off-site construction literature by providing insight into dynamics of using multi-skilled resources. It also contributes to practice by developing a customizable and user-friendly framework for off-site production managers in order to identify the optimal process integration architecture in their own production scenarios.
The permutation flowshop problem is a classic machine scheduling problem where n jobs must be processed on a set of m machines disposed in series and where each job must visit all machines in the same order. Many production scheduling problems resemble flowshops and hence it has generated much interest and had a big impact in the field, resulting in literally hundreds of heuristic and metaheuristic methods over the last 60 years. However, most methods proposed for makesan minimisation are not properly compared with existing procedures so currently it is not possible to know which are the most efficient methods for the problem regarding the quality of the solutions obtained and the computational effort required. In this paper, we identify and exhaustively compare the best existing heuristics and metaheuristics so the state-of-the-art regarding approximate procedures for this relevant problem is established.
The current approach in practice to produce the flowshop schedules of precast production is the dispatching rule technique, which does not guarantee optimal schedules. Some researchers have developed models for a single production line to optimize the scheduling, in which Genetic Algorithm (GA) is used to obtain the solution. However the models cannot be used for multiple production lines. Moreover, some optimization constraints and objectives were missing in the models, such as avoiding frequent type change of precast components during production. Both issues hinder their work to be applied to real precast plants. To overcome the problem, this paper proposes a Flowshop Scheduling Model of Multiple production lines for Precast production (MP-FSM) and develops a corresponding optimization approach to facilitate optimized scheduling by using GA. The approach was validated preliminarily by comparing with traditional scheduling approaches. The results demonstrated that optimized schedules can be obtained by using the proposed approach.
Two different large-scale industrial structures that were destroyed by fires in 2011 and 2012 in Turkey were examined. The structural framework in the first structure was constructed as prefabricated concrete, while a prefabricated concrete–steel (hybrid) was the structural framework in the second. The post-fire performance of the structures was evaluated according to the structural system properties of the buildings and tests on concrete core and steel reinforcement samples obtained from the structures. Since the fire durations of the two buildings examined were slightly different, in addition to the structural framework differences, material test results were comparatively interpreted for the two structures. The concrete used in the building exposed to the longer fire duration suffered a substantial loss in mechanical properties, the concrete cover in the reinforced concrete elements failed completely, and there was a significant loss in the yield strength of the steel reinforcement. In the second structure, which experienced a fire of shorter duration, although the concrete cover had failed similarly to the first case, there was not a substantial loss in the mechanical properties of the reinforcement and concrete. It was observed that damage to the steel structural framework in the second structure (a prefabricated concrete–steel hybrid) triggered damage in the prefabricated concrete system. The precautions that need to be taken in terms of fire, especially in industrial structures, were evaluated based on either the load-carrying system or the section, and suggestions for improvements are made.
There are two alternatives for production organization in precast factories-namely, the comprehensive method and the specialized method. Production scheduling under the specialized alternative has been found to be a difficult optimization problem if heterogeneous elements are involved. A flow shop sequencing model that incorporates actual constraints encountered in practice is proposed for this difficult case of precast production scheduling. The model is solved using a genetic algorithm (GA). The traditional minimize makespan and the more practical minimize tardiness penalty objective functions are optimized separately, as well as simultaneously using a normalized weighted GA. Comparisons between the GA and classical heuristic rules show that the GA can obtain good schedules for the model, giving a family of solutions that are at least as good as those produced by the use of heuristic rules.
Network analysis in geospatial information system (GIS) provides strong decision support for users in searching optimal route, finding the nearest facility and determining the service area. Searching optimal path is an important advanced analysis function in GIS. In present GIS route finding modules, heuristic algorithms have been used to carry out its search strategy. Due to the lack of global sampling in the feasible solution space, these algorithms have considerable possibility of being trapped into local optima. This paper addresses the problem of selecting route to a given destination on an actual map under a static environment. The proposed solution uses a genetic algorithm (GA). A customized method based on a genetic algorithm has been proposed in this paper. In single pair path algorithms we have tested for Iterative algorithm, Dijkstra algorithm, Best First A* Algorithms and the results were shown.
Construction projects frequently face cost overruns during the construction phase. Thus, a proactive approach is essential for monitoring project costs and detection of potential problems. In construction management, Estimate at Completion (EAC) is an indicator for assisting project managers in identifying potential problems and developing appropriate responses. This study utilizes weighted Support Vector Machine (wSVM), fuzzy logic, and fast messy Genetic Algorithm (fmGA) to handle distinct characteristics in EAC prediction. The wSVM is employed as a supervised learning technique that can address the features of time series data. The fuzzy logic is aimed to enhance the model capability of approximate reasoning and to deal with uncertainty in EAC prediction. Moreover, fmGA is utilized to optimize model's tuning parameters. Simulation results show that the new developed model has achieved a significant improvement in EAC forecasting.
A general mathematical formulation is presented for the scheduling of construction projects and is applied to the problem of highway construction scheduling. Repetitive and nonrepetitive tasks, work continuity constraints, multiple-crew strategies, and the effects of varying job conditions on the performance of a crew can be modeled. An optimization formulation is presented for the construction project scheduling problem, with the goal of minimizing the direct construction cost. The nonlinear optimization is then solved by the neural dynamics model developed recently by Adeli and Park. For any given construction duration, the model yields the optimum construction schedule for minimum construction cost automatically. By varying the construction duration, one can solve the cost-duration trade-off problem and obtain the global optimum schedule and the corresponding minimum construction cost. The new construction scheduling model provides the capabilities of both the critical path method (CPM) and linear scheduling method (LSM) approaches. In addition, it provides features desirable for repetitive projects, such as highway construction, and allows schedulers greater flexibility. It is particularly suitable for studying the effects of change order on the construction cost. This research provides the mathematical foundation for development of a new generation of more general, flexible, and accurate construction scheduling systems.
This work is focused on the application of evolutionary algorithms to solve very complex real-world problems. For this purpose
a Genetic Algorithm is designed to solve the Train Timetabling Problem. Optimizing train timetables on a single line track
is known to be NP-hard with respect to the number of conflicts in the schedule. This makes it difficult to obtain good solutions
to real life problems in a reasonable computational time and raises the need for good heuristic scheduling techniques. The
railway scheduling problem considered in this work implies the optimization of trains on a railway line that is occupied (or
not) by other trains with fixed timetables. The timetable for the new trains is obtained with a Genetic Algorithm (GA) that
includes a guided process to build the initial population. The proposed GA is tested using real instances obtained from the
Spanish Manager of Railway Infrastructure (ADIF). The results of the computational experience, point out that GA is an appropriate
method to explore the search space of this complex problems and able to lead to good solutions in a short amount of time.
The increasing complexity of solving crew allocation problems in a number of labour-intensive industries has led them to require more sophisticated and innovative allocation systems to satisfy such requirements. The aim of this study is to develop an innovative crew allocation system that can efficiently allocate possible crews of workers to precast concrete labour-intensive repetitive processes in order reduce the allocation cost and achieve a better flow of work. As a part of the methodology used in developing Crew Allocation System "SIMSA_Crew", process simulation is used to model and imitate all production processes involved and Simulated Annealing is then developed to be embedded within the simulation model for a rapid and intelligent search. A Dynamic Mutation operator is developed to add more randomness to the searching mechanism for solutions through solution space. The results showed that adopting different combinations of crews of workers had a substantial impact on reducing and minimising production cost.
Optimal location for prefabricated building industrial park (PBIP) can significantly reduce the logistic cost, delivery time and environmental pollution of prefabricated and modular construction. PBIP site selection problems can be more complex than other location selection problems when considering the goals of the PBIP at a manager level and a costumer level. There is little to no research that has examined the bi-level model for PBIP site selection. Thus, this paper addresses this issue by constructing a quantitative bi-level programming model which considers the economic, traffic, and environmental constrains. Subsequently, combining the genetic algorithm and Partan Frank–Wolfe algorithm, a hybrid heuristic algorithm is constructed to calculate the model. An empirical analysis is then carried out in the case of Chongqing, China. The results clearly demonstrate the applicability of the model. Furthermore, the paper discusses the trade-off between transport costs and environmental benefits, and the impact of transport radius on transport schemes. The proposed model contributes to solving location–allocation problems. It considers road congestion and traffic distribution by combining the location–allocation model with the user equilibrium model. The model can aid planners in making more robust decisions for setting up new PBIPs, thus contributing to the sustainable development of prefabricated and modular construction.
Abstract video:
https://alperkanyilmaz.com/decision-making-using-artificial-intelligence-in-civil-engineering/
The conceptual design decisions have the largest influence on a building project’s safety, value, and environmental impact; hence they are commonly assigned to a “senior engineer” to make use of his/her experience. However, the senior engineers can be biased towards solutions inside their area of expertise, which often prevents them from finding the best solutions among alternatives that must consider complex inter-related, and multi-disciplinary parameters. The engineering community could benefit from a rapid and high-quality decision-making method or tool to increase the speed and quality of its high-impact design choices. There are valuable studies in the literature exploiting Artificial Intelligence (AI) to improve the structural design process; however, most of them focus on the final design stage (e.g., Building Information Modeling), and the rest requires an existing project database (e.g., architectural drawings, already decided material types) to propose a small number of initial design alternatives. In this article, we present the development and validation of a genetic algorithm tool based on Non-dominated Sorted Genetic Algorithm II (NSGA-II) that can be used to analyse a wide range of safe, economical and low-CO2 options for the conceptual design of buildings. The design space starts from a design brief (with only the information about the site characteristics and project objectives). The solutions are explored with the material, grid size, floor type, lateral resistance, and foundation system variables. In a short computational time (< 2 min per run), users are provided with a Pareto graph of a large set of feasible solutions (in terms of cost, embodied CO2 emissions and free space) that an engineer would not be typically able to evaluate within a traditional conceptual design process. For future applications, the methodology presented in this paper is flexible to include more engineering materials (e.g., timber, masonry, structural glass), complex architectural forms and merge other disciplines in decision making (e.g., building physics construction management, fire safety).
Purpose
For prefabricated building construction, improper handling of the production scheduling for prefabricated components is one of the main reasons that affect project performance, which causes overspending, schedule overdue and quality issues. Prior research on prefabricated components production schedule has shown that optimizing the flow shop scheduling problem (FSSP) is the basis for solving this issue. However, some key resources and the behavior of the participants in the context of actual prefabricated components production are not considered comprehensively.
Design/methodology/approach
This paper characterizes the production scheduling of the prefabricated components problem into a permutation flow shop scheduling problem (PFSSP) with multi-optimization objectives, and limitation on mold and buffers size. The lean construction principles of value-based management (VBM) and just-in-time (JIT) are incorporated into the production process of precast components. Furthermore, this paper applies biogeography-based optimization (BBO) to the production scheduling problem of prefabricated components combined with some improvement measures.
Findings
This paper focuses on two specific scenarios: production planning and production rescheduling. In the production planning stage, based on the production factor, this study establishes a multi-constrained and multi-objective prefabricated component production scheduling mathematical model and uses the improved BBO for prefabricated component production scheduling. In the production rescheduling stage, the proposed model allows real-time production plan adjustments based on uncertain events. An actual case has been used to verify the effectiveness of the proposed model and the improved BBO.
Research limitations/implications
With respect to limitations, only linear weighted transformations are used for objective optimization. In regards to research implications, this paper considers the production of prefabricated components in an environment where all parties in the supply chain of prefabricated components participate to solve the production scheduling problem. In addition, this paper creatively applies the improved BBO to the production scheduling problem of prefabricated components. Compared to other algorithms, the results show that the improved BBO show optimized result.
Practical implications
The proposed approach helps prefabricated component manufacturers consider complex requirements which could be used to formulate a more scientific and reasonable production plan. The proposed plan could ensure the construction project schedule and balance the reasonable requirements of all parties. In addition, improving the ability of prefabricated component production enterprises to deal with uncertain events. According to actual production conditions (such as the occupation of mold resources and storage resources of completed components), prefabricated component manufacturers could adjust production plans to reduce the cost and improve the efficiency of the whole prefabricated construction project.
Originality/value
The value of this article is to provide details of the procedures and resource constraints from the perspective of the precast components supply chain, which is closer to the actual production process of prefabricated components. In addition, developing the production scheduling for lean production will be in line with the concept of sustainable development. The proposed lean production scheduling could establish relationships between prefabricated component factory manufacturers, transportation companies, on-site contractors and production workers to reduce the adverse effects of emergencies on the prefabricated component production process, and promote the smooth and efficient operation of construction projects.
The production scheduling of precast concrete (PC) is essential for successfully completing PC construction projects. The dispatching rules, widely used in practice, have the limitation that the best rule differs according to the shop conditions. In addition, mathematical programming and the metaheuristic approach, which would improve performance, entail more computational time with increasing problem size, let alone its models being revised as the problem size changes. This study proposes a PC production scheduling model based on a reinforcement learning approach, which has the advantages of a general capacity to solve various problem conditions with fast computation time and good performance in real-time. The experimental study shows that the proposed model outperformed other methods by 4–12% of the total tardiness and showed an average winning rate of 77.0%. The proposed model could contribute to the successful completion of off-site construction projects by supporting the stable progress of PC construction.
Precast production scheduling is a key issue in prefabricated construction. Process connection and blocking are two characteristics of the flowshop production of precast components; however, they have received little attention in research on production scheduling, which may prevent actual production from following optimized scheduling schemes. In this study, an optimization model for the production scheduling of precast components was established considering process connection and blocking for minimizing the total penalty cost of earliness and tardiness. A solution for the model was designed and applied to a practical case for validation. The results indicate that the improved scheduling scheme can reduce the penalty cost and facilitate on-time delivery of precast components under two scenarios: tardiness partially allowed and no tardiness allowed. The proposed model and method can help precast plants to improve efficiency and reduce cost in addition to enhancing the practicality of precast production scheduling schemes.
Purpose
This research aims to propose a comparative environmental analysis of conventional and prefabricated construction techniques utilizing a building information modelling (BIM) technique.
Design/methodology/approach
A set of indicators are selected to assess the environmental emissions throughout the construction life cycle, based on BIM platform. An existing project involving ten apartment buildings in Shanghai is selected as a case study.
Findings
The results reveal that prefabricated construction demonstrates environment-friendly performance with some exceptions of acidification and mineral resource consumption. Environmental impacts can also be further reduced by increasing the projected area ratio and percentage of project prefabrication.
Originality/value
Overall, the proposed method can be used to identify relevant environmental merits and for decision-making of appropriate construction techniques in building construction projects.
With increasing developments in the Information Technology (IT) outsourcing industry, many enterprises outsource IT services to reduce costs. However, the schedule risk of IT outsourcing (ITO) projects may result in enormous economic losses for an enterprise. In this paper, the principal–agent theory is used to control the schedule risk of ITO projects. A two-level mathematical model is built to describe the decision process of the client and vendors. With an increase to the number of subprojects and activities, the scale of the problem will become very large. The resulting optimization is an NP hard problem with continuous domain. Therefore, a genetic algorithm (GA) is designed to solve the proposed model. Experiments are performed to test the ability of the proposed algorithm. Some insights from simulation analysis – the principal–agent theory and two-level mathematical model – are suitable for describing the cooperative relationship between principle and agent. By comparing with ant colony optimization and simulated annealing, the proposed GA shows strong optimization abilities for convergence, reliability, and efficiency, which is a good tool for this kind of optimization problem. The near-optimal plan reduced the schedule risk of the project remarkably, which is the scientific quantitative proposal for the decision maker. This study provides practitioners insights on relationships of schedule risk and ITO projects, and the design model and algorithms of this paper provides practitioners effective potential method to reduce the schedule risk of ITO projects in their operations. However, the uncertain characteristics of key and multiple factors should be considered in future work. Stochastic Programming and the Monte Carlo Simulation Method are two potential tools for dealing with uncertain factors. Additionally, the proposed GA could potentially be improved in terms of convergence. The advantages of other intelligent algorithms could be applied to the GA in order to improve its searching ability, such as the Taboo mechanism.
Precast concrete structures (PCs) are widely used in the construction industry to reduce project delivery times and improve quality. On-time delivery of PCs is critical for successful project completion because the processes involving precast concrete are the critical paths in most cases. However, existing models for scheduling PC production are not adequate for use in dynamic environments where construction projects have uncertain construction schedules because of various reasons such as poor labor productivity, inadequate equipment, and poor weather. This research proposes a dynamic model for PC production scheduling by adopting a discrete-time simulation method to respond to due date changes in real time and by using a new dispatching rule that considers the uncertainty of the due dates to minimize tardiness. The model is validated by simulation experiments based on various scenarios with different levels of tightness and due date uncertainty. The results of this research will contribute to construction project productivity with a reliable and economic precast concrete supply chain.
Purpose
The purpose of this paper is to investigate the process optimization of a precast concrete component production line by using value stream mapping.
Design/methodology/approach
This paper is an empirical focused on of lean production theory and value stream mapping. The data in the case study were collected in real time on-site for each process during the production process of a prefabricated exterior wall.
Findings
The results of the current value stream map indicate that the main problems of the current production process are related to equipment, technology and organization. The equipment problems include simple demolding and cleaning tools and the lack of professional transfer channels. The technology problems include the lack of a marking mechanism and pipeline exit mechanism. There is a lack of standard operating procedures and incomplete process convergence. A comparison and analysis of the current value stream and the future value flow indicate that optimizations of the process flow, the production line layout, and the standard operating procedures have shortened the delivery cycle, reduced the number of workers, improved the operator’s operating level and balanced the production line.
Practical implications
The results of this study provide practitioners with a clear understanding of the optimization of the precast concrete component production and represent a method and basis for the process optimization of a factory production line; the approach is suitable for process optimization in other areas.
Originality/value
This research represents an innovative application of lean production theory and value stream mapping in a complex production line of precast concrete components and thereby fills the gap between the theory and practice of the optimization of a precast concrete component production line.
Flowshop production is adopted as the major type of production of reinforced precast concrete components and it has higher requirements on shop floor schedules than other types, especially that from rescheduling. However, up to now, very few approach for the optimization of the shop floor rescheduling has been proposed in spite of its vital importance. This research proposes an approach for optimizing shop floor rescheduling of multiple production lines for flowshop production of reinforced precast concrete components. The approach comprehensively utilizes the over-assigned time, which is the difference value between the assigned production time and the estimated one of a production step for a precast component to deal with production emergencies. Meanwhile, it keeps the adjustment of schedules at minimum to avoid massive material re-dispatch. First of all, the optimization objectives and constraints of optimized shop floor rescheduling of multiple production lines for flowshop precast production are analyzed and a mathematic model is thus formulated. Then, the solver of the model is established by using genetic algorithm. Finally, the approach is validated by case studies. It is concluded that the approach contributes to the effective and efficient optimized rescheduling of multiple production lines for flowshop precast production.
Production scheduling plays a crucial role in the prefabricated construction productivity and on-time delivery of precast components (PCs). However, previous studies mainly focused on the static scheduling of single production line without considering the demand variability in practice. To achieve dynamic production planning, a Two-level Rescheduling Model for Precast Production with multiple production lines is developed to minimise the rescheduling costs based on genetic algorithm, from the two levels of (1) selection of production line and (2) rescheduling of jobs based on PCs’ priority. Further, two scenarios of different and shared mould types are investigated to represent real-world production environments. Finally, a real case study is conducted to test the validity of proposed rescheduling model. 58.1 and 48.5% cost savings are achieved by comparison to no response to changes and heuristic rescheduling methods, respectively. This research contributes to the precast production theory by expanding the insight into dynamic rescheduling with multiple production lines. The methodology will promote the on-time delivery of PCs and enhance the dynamic precast production management.
Off-site Construction (OSC) is an alternative to conventional construction. However, China lags behind developed countries in terms of its OSC development. The chief goal of this study is to demonstrate the critical driving forces and solutions that promote OSC development. A total of 21 hypotheses are established to present the interrelations among eight driving forces. Based on 176 valid responses from developers in mainland China, a structural equation model is built to explore the key solutions and the critical driving forces. Most driving forces, such as Pursuit of Sustainable Competitiveness (PSC), Governmental Policies and Regulations (GPR), Technological Innovation (TI), Corporate Social Responsibility (CSR), and Construction Market Demand (CMD), can affect a Corporation’s Willingness and Behavior (CWB) in adopting OSC through two mediating factors, namely, Economic Benefits (EB) and social and environmental benefits. The findings highlight five main driving solutions to promote OSC, namely, “PSC → EB → CWB,” “PSC → TI → EB → CWB,” “GPR → TI → EB → CWB,” “GPR → CSR → CWB,” and “GPR → CMD → CWB.” Governments and project clients are also identified as important players in the development of OSC in China. These results provide a valuable reference for the government to understand the underlying interrelationships among the driving forces and key solutions to the development of OSC in China.
Current precast production scheduling methodologies have limited applicability in practice due to the neglect of real-world production circumstances. To improve, a two-hierarchy simulation-GA hybrid model for precast production (TSGH_PP) is developed to (1) specialize the operations of precast production according to their characteristics, (2) incorporate the uncertainty in processing time in practice, and (3) model the process-waiting time on the flow of work based on the genetic algorithm and discrete event simulation. In the proposed model, the trade-off can be achieved between the conflicting goals of the on-time delivery of precast components and minimum production cost, and the production resources configuration is optimized to cut down resource waste. Finally, a real case study is conducted to test the validity of TSGH_PP approach. The developed model fills the gap in simulation system design and methodology for precast production, and increases the applicability of precast production scheduling methods in real construction projects.
Production scheduling plays a key role in the prefabricated construction productivity and on-time delivery of precast components (PCs). However, the processes before and after the production of PCs were ignored in previous scheduling studies. These operations account for a large proportion of the PCs' processing time in practice. To ensure the accurate calculation of PC's completion time and its on-time delivery, this study integrates the mold manufacturing, PC storing, and transportation processes to modify the traditional production-scheduling model from the perspective of the whole PC supply chain. Further, the three scenarios of daytime, night, and all-day transportation are analyzed to conform to the practice. Based on the genetic algorithm, two case studies are conducted to test the validity of the proposed scheduling model. The modified schedule could achieve 17.7, 35.7, and 15.4% cost savings in the three scenarios, respectively. The methodology will enhance the feasible production scheduling and promote on-time delivery of PCs.
It may be possible to reduce uncertainty in construction projects by adopting the prefabrication method. In this method, components are produced in factories and transported to the construction site to satisfy installation demands. For successful and effective prefabrication, the project designer and precaster must develop an integrated plan to manage the available resources in a way that satisfies design flexibility, production constraints, and installation demands. Configuring individual building elements and forming building components or modular units will result in employing a higher degree of prefabrication for higher productivity and ease of construction. The production of such complex configurations requires complex molds. To achieve optimization of resources and costs for the precast production of complex configurations, two new ideas have been adopted: namely prefabrication configuration and component groups; these are incorporated into the mixed integer linear programming (MILP) model. Moreover, an integrated plan is developed to efficiently utilize complex molds in production platform by using a mold adaptability matrix. Based on these concepts, an MILP optimization model is developed to adopt appropriate molds and create an optimal production plan. The model is validated by using two examples with different scenarios. The results show that employing the idea of prefabrication configuration and component grouping in production planning for prefabricated structures can reduce total costs by up to 13% compared to the existing planning approach. The developed model should help prefabrication manufacturers better manage their resources and possibly expand their production capacity. (C) 2013 American Society of Civil Engineers.
Recently government agencies have started to utilize innovative contracting methods that provide incentives for improving construction quality. These emerging contracting methods place an enormous pressure on the contractors to improve construction quality. For a general contractor, which subcontracts most tasks of a project and invites a number of bids, choosing an appropriate bid which satisfies the time, cost and quality of construction project is complex and challenging. To solve this problem involving conflicting objectives, a fuzzy clustering-based genetic algorithm (FCGA) approach is proposed in this paper. A case study of highway construction is used to demonstrate the applicability of the proposed approach. A comparative study is conducted over three test cases involving varying dimensions and complexities to test performance of the proposed FCGA against existing approaches. Results reveal that the FCGA is capable of generating better Pareto front than other existing approaches.
Prefabrication has been developed since the 1970s. The technologies have been further developed and improved for the past thirty years. The successful implementation of quality control and construction efficiency has been addressed with support from the public sector. The technologies however did not receive attention from the private sector since prefabrication requires dimensional coordination and standardization in the designs. This situation has changed from 2002 as the Hong Kong government promotes incentives schemes, i.e. gross floor area concessions for private developers to encourage them to adopt prefabrication techniques. This paper discusses and evaluates the best practice of prefabrication implementation in the Hong Kong public and private sectors using two leading case studies. Their adoption of prefabrication, construction methods and cost effectiveness are investigated. Discussions on effective implementation for the sectors have also been explored. The findings provide ameliorated understanding on the best practice of the implementation of prefabrication and provide courage for further improvement and implementation for the industry.
This paper discusses the process of eliciting scheduling knowledge from a simulation model and the development of a dynamic modelling approach to the scheduling process in the precast concrete industry. Due to the problems associated with eliciting scheduling knowledge from an ‘expert’ in the precast industry or perhaps in most of the manufacturing industries, simulation is used to complement human knowledge in this paper. Such knowledge will be used for online support to advise production schedulers and for further development of the simulation model by incorporating the knowledge in the model and making it more dynamic. The paper suggests that dynamic selection of scheduling rules during real-time operation has been recognised as a promising approach to the scheduling process in the precast industry. For this strategy to work effectively, sufficient knowledge is required to enable the model to predict the most effective scheduling rule to meet current factory status. The paper concludes that if the knowledge rules are used effectively, they could be a considerable managerial tool for exploring and improving managerial practices. Recommendations have been made regarding the development of a more realistic and practical scheduling system.
The precast industry is a supplier of building materials to the construction industry. It is capital intensive and the instability of construction demand makes investment in the industry a high risk undertaking. As a result, a highly structured and efficient scheduling system is required to maximise the utilisation of resources and minimise the waste associated with them.The main objective of this paper is to develop a scheduling model, using the job shop scheduling approach, in order to help production managers to make better planning decisions, and to explore alternative options. The model is a computerised factory simulator which comprises scheduling rules and the factory's attributes. These rules have been developed in this research to mimic the decision making process of a production scheduler.A number of experiments were conducted using the model as a test bench to evaluate the performance of the scheduling rules under different measures of performance and factory conditions. The work also examined the effect of sales fluctuations on the performance of the model. It was concluded that it is feasible and beneficial to model the industry using the simulation modelling approach in which there is no scheduling rule superior to others under any factory conditions.
The production planning process in prefabrication plant involves the following decisions: what elements should be produced, on which facilities and when. The planning should be performed in such a manner that the costs dependent on these decisions will be at a minimum. A distinction is made here between short and long production series. The first involves specific orders, and the other a continuous demand for standard elements. The paper is arranged in the following fashion. Firstly, some general principles of production planning are discussed with reference to a series of similar elements produced on a single mould. This case is later expanded to production of different types of elements in large series, produced on one or several moulds. Finally, the most general case, that of all types of series, long and short, produced on a system of several moulds, is explored. The main features of an information system within which the production planning takes place are also presented.
There are two alternatives for production organisation in precast factories, namely the comprehensive method and the specialised method. Production scheduling under the specialised alternative has been found to be a difficult optimisation problem if heterogeneous elements are involved. A flow shop sequencing model is developed for this kind of production scheduling that considers the constraints encountered in actual practice. The model is optimised using a genetic algorithm (GA) approach. The results are compared with those obtained using classical heuristic rules in two examples that involve the objective of minimising the makespan or the total tardiness penalty. The comparison shows that the GA can obtain good schedules for the model, giving a family of solutions that are at least as good as those produced by the heuristic rules.
Appropriate production plans can produce effective resource utilization and minimize waste. However, most precast fabricators currently propose production plans depending on the rule of thumb, resulting in squandered resources and postponed delivery. Computerized scheduling techniques provide more precise outcomes than manual scheduling. The objective of this study is to develop GA-based Decision Support Systems (GA-DSS) to assist production managers in arranging production plans. This research first establishes a flowshop sequencing model based on the current production status by considering the buffer sizes between production stations. A multiple objective genetic algorithm is then applied to search for solutions with minimum makespan and tardiness penalties. The GA-DSS performance is verified using two examples. The results demonstrate that the proposed system can offer appropriate production plans. By taking buffer sizes into consideration more reasonable and feasible production sequences can be achieved.
The goal of production scheduling is to achieve a profitable balance among on-time delivery, short customer lead time, and maximum utilization of resources. However, current practices in precast production scheduling are fairly basic, depending heavily on experience, thereby resulting in inefficient resource utilization and late delivery. Moreover, previous methods ignoring buffer size between stations typically induce unfeasible schedules. Certain computational techniques have been proven effective in scheduling. To enhance precast production scheduling, this research develops a multi-objective precast production scheduling model (MOPPSM). In the model, production resources and buffer size between stations are considered. A multi-objective genetic algorithm is then developed to search for optimum solutions with minimum makespan and tardiness penalties. The performance of the proposed model is validated by using five case studies. The experimental results show that the MOPPSM can successfully search for optimum precast production schedules. Furthermore, considering buffer sizes between stations is crucial for acquiring reasonable and feasible precast production schedules.
Bespoke precast concrete products are particularly designed and custom made for a construction project. The production planning of these products is complicated that considers important concerns, i.e. the reliability of the product delivery programme, the short lead-time competitiveness, and the effective utilization of purpose-built precast moulds. The planning has a high impact on the success of the production. The characteristics of the bespoke precast production are formulated with the flowshop scheduling technique so that an effective production plan can be arranged to meet these concerns. Genetic algorithm is used in the scheduling optimization. Its multi-objective function includes total flowtime, total machine idle time, and total tardiness and earliness. After the model formulation, sensitivity analyses are conducted on the three model's parameters namely the number of mould availability, the processing time changes, and the weighting of the multi-objective function. The proposed model is anticipated to support the planners to arrange economic and efficient production plans. Also, it can be used to determine the suitable number of moulds, the accuracy of the processing time estimation, and the weighting strategy of the multi-objective optimization.
Improved biogeography-based optimization algorithm for lean production scheduling of prefabricated components
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Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model
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