Optimized rescheduling of multiple production lines for flowshop production of reinforced precast concrete components

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.     

Framework for modeling operational uncertainty to optimize offsite production scheduling of precast components

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.     

Process reengineering and improvement for building precast production

Current production methods for the building of precast concrete elements are gradually becoming insufficient to meet industry demands. Most precast factories incorporate fixed molding production lines to produce building structural elements such as beams, trabeculae and pillars. Improvement of current production has reached its limits. The objective of this study is to perform process reengineering so as to create more efficient precast processes. The results of the literature review and advice from 16 experts suggest that the current process used to produce a precast element, which is comprised of 16 steps in a single module, should be redesigned to include 13 steps in 3 modules. Using historical data as input, the Arena software, Version 13, is adopted to simulate both the traditional and proposed methods, and then compare the results for practicability. An overall savings of production time of 24% can be reached by changing the current/traditional method to the flow-shop type production method.     

GA-based decision support systems for precast production planning

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.     

Generalizable occupant-driven optimization model fordomestic hot water production in NZEB

The primary objective of this paper is to demonstrate improved energy efficiency for domestic hot water(DHW)production in residential buildings.This is done by deriving data-driven optimal heating schedules(used interchangeably with policies)automatically.The optimization leverages actively learnt occupant behaviour and models for thermodynamics of the storage vessel to operate the heating mechanism-an air-source heat pump(ASHP)in this case-at the highest possible efficiency.The proposed algorithm,while tested on an ASHP,is essentially decoupled from the heating mechanism making it sufficiently robust to generalize to other types of heating mechanisms as well.Simulation results for this optimization based on data from 46 Net-Zero Energy Buildings(NZEB)in the Netherlands are presented.These show a reduction of energy consumption for DHW by 20%using a computationally inexpensive heuristic approach,and 27%when using a more intensive hybrid ant colony optimization based method.The energy savings are strongly dependent on occupant comfort level.This is demonstrated in real-world settings for a low-consumption house where active control was performed using heuristics for 3.5months and resulted in energy savings of 27%(61 kW h).It is straightforward to extend the same models to perform automatic demand side management(ADSM)by treating the DHW vessel as a flexibility bearing device.     

BIM-based integrated approach for detailed construction scheduling under resource constraints

Building information modeling (BIM) has been recognized as an information technology with the potential to markedly change the Architecture, Engineering, and Construction (AEC) industry, and has drawn attention from numerous scholars within the construction domain. Despite the reported advancements pertaining to BIM in previous studies, the extended use of BIM has not yet reached its full potential. This paper thus presents a BIM-based integrated scheduling approach which facilitates the automatic generation of optimized activity-level construction schedules for building projects under resource constraints, by achieving an in-depth integration of BIM product models with work package information, process simulations, and optimization algorithms. A developed prototype system for panelized building construction as an add-on tool for Autodesk Revit is described. A case study is subsequently presented to demonstrate the methodology. Building on the existing body of research in this field, the key contribution of the proposed research is the in-depth integration of BIM product model with work package information, process simulations, and an optimization model, which provides solutions addressing the challenges of the existing practice with respect to detailed construction scheduling under resource constraints.     

Network-Wide Optimal Scheduling of Transit Systems Using Genetic Algorithms

The primary objective of any transit system is to provide a better level of service to its passengers. One of the good measures of level of service is the waiting time of passengers during their journey. The waiting time consists of an initial waiting time (the time a passenger waits to board a vehicle at his or her point of origin) and a transfer time (the time a passenger waits at a transfer station while transferring from one vehicle to another). An efficient schedule minimizes the overall transfer time (TT) of passengers transferring between different routes as well as the initial waiting time (IWT) of the passengers waiting to board the vehicle at their point of origin. This paper uses genetic algorithm (GA)—a search and optimization procedure—to find optimal/near-optimal schedules of vehicles in a transit network. The main advantage of using GA is that the transit network scheduling problem can be reformulated in a manner that is computationally more efficient than the original problem. Further, the coding aspect of GA inherently takes care of most of the constraints associated with the scheduling problem. Results from a number of test problems show that GAs are able to find optimal/near-optimal schedules with minimal computational resources.     

A multi-objective GA-based optimisation for holistic Manufacturing,transportation and Assembly of precast construction

Resource scheduling of construction proposals allows project managers to assess resource requirements, provide costs and analyse potential delays. The Manufacturing, transportation and Assembly (MtA) sectors of precast construction projects are strongly linked, but considered separately during the scheduling phase. However, it is important to evaluate the cost and time impacts of consequential decisions from manufacturing up to assembly. In this paper, a multi-objective Genetic Algorithm-based (GA-based) searching technique is proposed to solve unified MtA resource scheduling problems (which are equivalent to extended Flexible Job Shop Scheduling Problems). To the best of the authors' knowledge, this is the first time that a GA-based optimisation approach is applied to a holistic MtA problem with the aim of minimising time and cost while maximising safety. The model is evaluated and compared to other exact and non-exact models using instances from the literature and scenarios inspired from real precast constructions.     

Developing a production management modelling approach for precast concrete building products

Production management in this research is concerned with three key decisions: demand forecast, production scheduling and stock forecast. These three decisions are very much interrelated and cannot be made in isolation. Previous studies of precast concrete industry activities concluded that production management is fragmented. For example, production planning is done in isolation from demand forecasting and from stock forecasting. This has contributed to poor production management performance in terms of resource utilization and over-stocking. This paper goes beyond traditional production management theories and practices and develops a model to integrate all aspects of production management. The main objective is to develop an integrated production management model for the make-to-stock sector of precast concrete building products, in order to help production managers make better planning decisions and explore alternative options. The model is a factory simulator which examines and evaluates the effect of several managerial strategies on production planning and stock forecasting before actual production commences. It uses different measures of performance which facilitate the choice of planning strategies under various demands and factory conditions.     

SCHEDULING WITH CO-EVOLVING RESOURCE AVAILABILITY PROFILES

Existing resource allocation models schedule activities by balancing resource requirement with resource availability using the resource available profile as a constraint to guide the scheduling process. The given resource available profile, however, may not be the best configuration for the project. Consequently, the resource available profile may have to be modified to produce improved resource utilization. With each modification the activities have to be rescheduled to meet resource availability constraints. A model is presented herein which is able to generate activity schedules that is optimal with respect to the resource available profile that is configured interactively with the schedule for minimum resource idling. The entire project is subdivided into separate resource contracts. The model then employs genetic algorithm (GA) to determine the start times of the activities and simultaneously self-adjust the resource hiring levels and end-dates for each contract in order to minimize the objective function. Results show that the model can provide schedules and corresponding resource available profiles that yield considerable savings in idle resources even in multi-resource problems.     

Optimizing the utilization of multiple labor shifts in construction projects

Evening and night shifts are often used in construction projects to accelerate schedules despite their negative impacts on construction cost and productivity. In order to minimize these negative impacts, this paper presents a multi-objective optimization model for scheduling multiple labor shifts in construction projects. The optimization model incorporates (1) an initialization module that initializes the scheduling optimization computations; (2) a scheduling module that develops practical multiple shift schedules and evaluates the impacts of decision variables on project performance; and (3) a multi-objective genetic algorithm module that searches for and identifies optimal/near optimal tradeoffs among minimizing project duration, reducing cost, and minimizing labor utilization in evening and night shifts. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the planning and scheduling of multiple shifts in construction projects while complying with all job logic and labor availability constraints.     

A hybrid optimization mechanism for constructing a dynamic simulation system—An operational behavior analysis of a moving scaffolding system

Standardized production processes in the construction industry can reduce uncertainty, cost and time. The Movable Scaffolding System (MSS), a new bridge construction technique, facilitates automated operations at a high rate. In an MSS, highly repetitive mechanical operations make issues of supply and allocation of machinery and raw materials extremely important. We use Simprocess, a computer simulation software package, to construct hierarchically standardized models of MSS bridge engineering operations (MSSBEO), which encompass bridge superstructure and substructure production, procurement, storage and transmission of raw materials, and utilization of nonconsumable resources such as machines, tools and human resources. We also present a novel dynamic simulation model with a novel fuzzy inference-based optimization mechanism for multiobjective optimization that can identify the optimal resource-allocation strategy under dual goals of minimized time and cost in the MSSBEO model. We analyze the MSSBEO model using the proposed optimization system and use a case study to verify the accuracy and applicability of the proposed system. Through the human decision-making module, optimized resources allocation strategies can be quickly searched to meet actual conditions and enhance the academic value and application of the optimized resource combinations. The most suitable resource combination for an actual situation is generated by applying the proposed optimization module with the fuzzy inference mechanism. After model testing, the optimized resource combinations obtained using the proposed optimization module enhance the utilizations of each resource and reduce the overstocking of materials in the simulation to achieve the goals of reduced cost, efficient work schedules and enhanced productivity.     

Improving workflow and resource usage in construction schedules through location-based management system (LBMS)

Critical Path Method (CPM), a planning and controlling technique, is widely used in the construction industry. However, CPM is criticized for its lack of workflow and inability to schedule continuous resource usage. Location-Based Management System (LBMS) fill these gaps and has been implemented in many construction projects. We propose that LBMS will improve schedules and project performance, addressing CPM’s main shortcomings. This study is composed of three case studies. CPM schedules were analyzed and were improved using LBMS tools. The resulting schedules show improved workflows, crew balancing, resource usage and had fewer interruptions, without affecting project duration. Furthermore, LBMS schedules were optimized with only a few scheduling operations and fewer planning elements. The computational benefit of LBMS increases with the number of locations and tasks in a schedule. Project managers will benefit from a simpler scheduling process and better resource flow.     

Finance‐based CPM/LOB scheduling of projects with repetitive non‐serial activities

Projects of repetitive non‐serial activities constitute a major category of construction projects which can be scheduled more conveniently using the line of balance (LOB) technique. Generally, scheduling activities such that the expenditures are always in balance with the available cash is a must to devise financially feasible schedules. The objective is to integrate a CPM/LOB model for a project of repetitive non‐serial activities with a cash flow model and utilize the integrated model to devise financially feasible schedules. The genetic algorithms (GAs) technique is employed to maximize the profit at the end of the project under the constraints of available cash. The optimization of the integrated models was demonstrated using an example project of 15 activities carried out at five units. The CPM/LOB model was validated against the results of a dynamic programming model in the literature and further by conducting a sensitivity analysis of the results of the integrated model. Finally, the model offers an effective financial planning tool for projects of repetitive non‐serial activities.     

Developing a precast production management system using RFID technology

Radio Frequency Identification (RFID) is characterized with superior predispositions, including the recognition of multiple markers, communication ranges from five to six meters, and a storage database for thousands of data files. These unique features resolve data storage and record-reviewing difficulties, prevent repetitive data entries, and facilitate instant feedbacks.The precast industry is a management-intensive sector, for which pivotal issues include methods relevant to the instant provision of acquired management information to executive managers, materials of precast concrete products, quality control inspection and inventory and transportation management information, and convenient data entry for frontline personnel.It is anticipated to integrate usage of Personal Digital Assistants (PDA) and the application of Radio Frequency Identification (RFID). Using RFID tag and reader to collect the information, and then transmit the multi-faceted, mobilized information such as the production quantity, the materials quantity, quality control inspection and inventory and transportation management information to the manager office or the site worker via the PDA and wireless Internet.This precast production management system which has been developed by the author and utilized in precast production management encompasses inspection of incoming materials, production process inspection, molds inspection, specimen strength feedback, and logistic and receiving management. The mobilized information frame elaborated in this proposal can, to certain degree, be regarded as a reference for the construction of a mobilized precast industry management system.     

Developing Crew Allocation System for the Precast Industry Using Genetic Algorithms

Abstract: The Precast Concrete Industry (PCI) is one of the major contributors to the national economy and can be categorized as labor‐intensive industry. It is currently experiencing shortcomings in terms of delivery products at a competitive cost and time. This is mainly due to the inefficiencies associate with planning and scheduling of skilled operators within crew configurations. This article presents a new strategy for efficient allocation of crews of workers in the precast concrete industry using Genetic Algorithms‐based simulation modeling. The aim of this study is to develop a crew allocation system that can efficiently allocate possible crews of workers to precast concrete labor‐intensive repetitive processes. Genetic algorithms (GAs) have been developed to solve this type of problem. Process mapping methodologies were used to identify and document the processes involved in producing precast components. Then process simulation was used to model and simulate all these processes and GAs were tailored to be embedded with the simulation model for a better search of promising solutions. GA operators were designed to suit this type of allocation problem. “Class Interval” selection strategy was developed to give a greater opportunity for the promising chromosomes to be chosen for further investigation. Dynamic crossover and mutation operators were developed to add more randomness to the search mechanism. The results showed that adopting different combinations of crews of workers had a substantial impact on reducing the process throughput time, minimizing resources cost, and achieving the required operatives utilization.     

Development of a schedule-workspace interference management system simultaneously considering the overlap level of parallel schedules and workspaces

Schedule-workspace interference is generated when workspaces that share parallel schedules and are physically adjacent to one another exist simultaneously. When workspace interference is generated, securing work performance safety is difficult and constructability can deteriorate due to increased collision risk between resources. The objective of this study is to realize an active simulation system based on building information modeling (BIM) after constructing a genetic algorithm (GA) process for an alternative schedule that minimizes the simultaneous interference level of the schedule-workspace. To accomplish this task, the impact factor of workspace interference, which simultaneously considers schedule overlap and adjacency, was analyzed. From the impact factor, an optimized algorithm based on a location-constraint GA that can minimize workspace interference is suggested. The GA visually simulates the optimization level of the execution schedule compared to the initial plan through interlock with four-dimensional (4D) computer-aided design (CAD). A 4D CAD system that can analyze workspace interference by a GA was developed, and for the developed algorithm and system, a case verification was attempted for a railroad construction project. The results show how a simple visualization-oriented BIM system can be extended to an active schedule management system equipped with decision-making functions of workspace analysis.     

基于多层编码遗传算法的两阶段装配式建筑预制构件生产调度优化

随着装配式建筑技术的发展和应用,多种类、多数量的预制混凝土构件生产进度优化问题随之产生,并且日益成为行业市场提升生产效率和竞争力的关键因素。在对比分析预制构件生产与一般制造业区别的基础上提出预制构件厂生产参数的量化途径,采用基于多层编码遗传算法解决预制构件的生产调度优化问题;提出两阶段的优化模式从而实现优化算法准确性的提升,使得预制构件生产企业有效优化生产管理、提升资金使用效率从而实现增效降本。     

基于BIM技术的智能化住宅部品生产作业计划与控制系统功能需求分析

本文分析基于BIM技术的智能化住宅部品生产计划与控制系统的功能需求。首先,建立现行住宅部品生产作业计划与控制过程模型;其次,在总结现行管理模式中所存问题的基础上,基于BIM等新信息技术所带来的可能性,提出改进的过程模型;最后,基于所提出的新模型,分析基于BIM技术的智能化住宅部品生产作业计划与控制系统功能需求。依据上述方法,本研究共提出了37项功能需求,为进一步进行系统设计和开发奠定了基础。