Optimization of Prefabricated Components in Housing Modular Construction

한옥은 단순히 전통건축의 의미를 넘어서 우리 선조들의 삶에 대한 양식과 인식을 담고 있는 지금도 살아 숨 쉬는 문화유산이다. 최근 한옥이 자연친화적 건축으로 주목을 받게 되면서 한옥의 전통적 방식을 훼손하지 않으면서도 건축과정에 효율을 꾀할 수 있는 방안이 모색될 필요가 대두되었다. 이에, 본 논문에서는 건축정보모델링을 기반으로 하는 설계 지원도구를 개발하여 한옥의 설계에서 검증, 생산 공정에 이르는 전 과정을 지원하는 것을 목표로 한다. 이를 위해 먼저, 전통건축의 통시대적 고찰과 한옥의 목구조 방식에 대한 체계적인 분석을 통해 전통건축 설계에 필요한 부재들에 대한 효율적 설계 방식을 제시한다. 논문에서 제안하는 방식은 특성이 비슷한 부재들을 모아서 하나의 템플릿으로 설계하고 다양한 형태의 유사부재들을 속성 값에 따라 자유롭게 생성할 수 있도록 객체지향 방식의 표현기법을 사용하는 것이다. 이 방식은 객체지향 방식의 부재를 표현함에 있어서 연결 부재간의 상관관계를 부재와 부재간의 관련 파라미터들 사이의 결합규칙을 적용함으로써 설계의 오류를 최소화 하도록 지원할 수 있다. 또한 개발된 시스템은 서양건축 설계 위주의 CAD프로그램에 플러그 인 형태로 수행할 수 있기 때문에 전통적인 방식의 한옥설계는 물론 한옥 건축문화를 현대적 생활공간에 쉽게 접목할 있도록 고안되었다. Hanok is a cultural heritage containing our ancestor's life style intact and breathing alive with us until now. As Hanok has been concerned as a echo-friendly architecture, a new methodology for efficient construction without damaging the traditional construction process comes into request. The goal of this research is development of a architectural design tool based on the BIM(Building Information Modeling) for satisfying these demands. It will be usable to support whole process of the traditional building from digital design to production and construction. Firstly, we take a consideration of the traditional architecture reflecting the spirit of the age and suggest efficient design method for architectural components. Each components is pre-fabricated as a template representing similar components. All pre-fabricated components are designed by object-oriented concepts so, many variations for a component can be derived from the pre-fabricated component. Our method is helpful for reducing design errors because that it considers combining rule between connecting components in the template design. Moreover it is plugged in the commercial architectural CAD, so it can supports digital design not only traditional architecture but also fusion style mixed with modern architecture.

As a main carrier mode for the sustainable development of the construction industry in China, prefabricated building may lead to problems such as cost overruns, project delays, and waste of resources due to unreasonable selection of prefabricated components. Therefore, we quantitatively analyze the contribution rate of quality optimization of prefabricated components using QFD-SEM. Under the constraints of prefabrication rate, quality optimization contribution rate, and expected values of various sub-goals, we propose a multi-objective optimization method for prefabricated component combinations based on cost, duration, and carbon emissions. By using NSGA-II to solve the model, we can obtain a set of optimal Pareto solutions for prefabricated component combinations. Based on the optimal Pareto solution set, we establish a multi-objective evaluation model using simulated annealing optimization projection tracing method, and select the optimal prefabricated component combination solution according to the projected eigenvalues of the solutions. An empirical study is conducted using an eleven-story framed building in Shenzhen, Guangdong Province, China as a case study. The results show that: (1) Using this method, optimal solutions can be obtained in an unbounded solution space, with the optimal solution having advantages over both fully cast-in-place and fully prefabricated solutions. Compared to the fully cast-in-place solution, the duration and carbon emissions are reduced by 36.62% and 12.74% respectively, while compared to the fully prefabricated solution, costs are reduced by 4.15%. (2) There is a certain negative correlation between the cost of prefabricated component combinations and duration, carbon emissions, and quality optimization, while there is a certain positive correlation with the prefabrication rate. (3) The size of the optimal projection direction vector based on the optimization objectives indicates that carbon emissions have the greatest impact on the evaluation results of the solutions.

Modular construction has been reported to lead to a number of advantages when contrasted with conventional construction approaches, including time and cost savings, along with reduced environmental impacts. Given that the operations involved in modular construction take place within a controlled environment, an integral factor that impacts the productivity of works involved is the timely scheduling of the associated activities. Unlike conventional on-site construction methods, operations in modular construction take place in a sequential and linear manner, with large dependence on adequate resource allocation to specific workstations. In a bid to address the lack of mathematical optimisation methods that target the scheduling of operations in a modular factory setting, this paper proposes a novel mixed integer non-linear programming model for optimising the scheduling of modular construction activities on the shop floor of modular factories. The impact of scheduling of resources, such as overhead cranes, and the distance between stations to which activities are scheduled are factored in the proposed method. The solution approach presented for solving the scheduling problem involves the linearisation of the model to enable the use of efficient off-the-shelf solvers. A realistic case study is implemented, and an extensive computational experiment is also conducted to test the robustness and tractability of the proposed method. The performance of the model is also contrasted with a commonly adopted heuristic, with the model producing project durations reductions that are 43% less than that of the heuristic.

Modular construction offers several advantages, such as safe working conditions, fast construction, and high-quality end products. The project scheduling of modular construction should consider the time involved in the factory production, transportation, and erection processes of the units given limited resources (e.g., the number of modular units, available transportation, and workers) and project restrictions (e.g., the sequences of manufacturing production and on-site installation). However, current modular construction project scheduling techniques and previous academic approaches cannot deal with the overall project processes and production/on-site installation sequences simultaneously. This is one reason few mass-production multiple projects exist in the manufacturing industry. Moreover, it can lead to inappropriate project management guidelines and fail to minimize the overall project duration. This paper suggests a model for modular construction projects scheduling using a genetic algorithm. The results show that implementing both overall project processes and project requirements is an efficient way to develop a modular construction project schedule. The proposed optimization tool will help project managers allocate resources efficiently and reduce the completion time of multiple projects.

With a view to the procurement relationship between prefabricated component (PC) production enterprise and its upstream raw material supplier, an MRP-guided collaborative optimization model for order quantity in supply chain was constructed based on the thought of just-in-time (JIT) procurement by following the BOM constraint and MRP principle of PC production. The traditional butterfly optimization algorithm (BOA) was improved by combining Levy flight strategy, mixed mutation operation and simplex method (SM), thus proposing the LMSMBOA algorithm. In a practical case, the model was solved using the improved algorithm, which was then compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm and unimproved BOA. The results reflect that the LMSMBOA algorithm can obtain more outstanding raw material procurement schemes by solving the collaborative material procurement model in supply chain between PC production enterprises and upstream raw material suppliers.

Holistic LCA evaluation of the carbon footprint of prefabricated concrete stairs

In order to solve the problem of carbon emission of prefabricated building components, combined with the standardization results of prefabricated components in China, three calculation methods are put forward according to the availability of data, and the carbon emission database of standard prefabricated components is formed. And the standardization of prefabricated component design is extended to the standardization of carbon emission calculation. By calculation and analysis, the equations of the ratio of steel content to carbon emission per unit volume and carbon emission per unit volume of reinforced concrete are obtained. The proposed calculation method of carbon emissions is more simple and universal, and the analysis results can provide reference for related research.

Digital twin technology plays a crucial role in completing the virtual mapping of the physical workshop, enabling production monitoring and process optimization. New energy batteries are a form of green energy with advantages such as high energy density, environmental protection, and beneficial safety. The assembly line for new energy batteries is essential in efficiently obtaining battery packs of diverse varieties and high quality. This paper presents three assembly design schemes for square lithium iron phosphate battery packs as an example. By comparing the characteristics of different schemes, scheme C was selected as the preferred option for conducting three-dimensional modeling of the assembly line. This paper utilizes digital twin technology to create a digital representation of the battery pack assembly process. The process flow of the designed production line can be tested. The loading time, equipment idle time, fault time, blocking waiting time, material shortage waiting time, etc., of each workstation in the production line can be accurately calculated. Optimize the production process flow, reduce resource occupation, and save a large amount of human and material resources.

This experiment was centred on the concept of using pectin gel to manufacture fairy tofu in order to determine the ideal parameters for the creation of natural fairy tofu. Based on the single-factor test, The Box-Behnken central combination design approach was employed to investigate the optimal production process of fairy tofu. According to the findings, there were substantial changes (P<0.05) in the qualitative attributes of tofu at varied material-to-liquid ratios, drying temperatures (the leaves of Premna microphylla Turcz) and grinding temperatures during the production process. The optimal process parameters were: material-to-liquid ratio of 1:60 (g:g), drying temperature of 20 °C and a grinding temperature of 90 °C. The yield of fairy tofu produced under this process parameter was 46.02 g/g, which was 55.33% higher than the yield of fairy tofu produced by the unoptimized process. It indicated that the optimized process conditions for the production of pure natural fairy tofu are feasible and can produce a high-yield and high-quality of fairy tofu. This study provides a theoretical basis for the reuse of the leaves of Premna microphylla Turcz. and lays the foundation for the large-scale production of fairy tofu.

Mathematical models have been proven to be a key factor in optimizing production processes in recent years. However, in the case of biochemical processes the design is usually done using heuristics, since these systems show complex internal regulation mechanisms and strongly nonlinear behavior. This makes it difficult to find an appropriate model. In those cases, where a structured biochemical model has been successfully identified, the yield of the process can be increased significantly. Obtaining a suitable production model is usually a difficult and time consuming task, especially for biochemical systems. In this contribution the concept for an automation tool is presented which starts with the few noisy measurements of initial experiments to perform a model evolution from run to run. Thereby, the first unstructured model candidates are used for an optimal production-orientated process design whose realization will provide additional information about the dynamic behavior within the production area, thus, leading to new and improved model candidates. Due to the difficult measurement situation in biochemical processes many different model candidates may show a similar fit to the data why it is unwise to focus on one model candidate for process design, only. Furthermore, the use of more than one model candidate for the design procedure represents a kind of robustness for the planning. This cyclic procedure enables an optimal production design corresponding to the available measurement information at any time.

In an environment of constantly growing market dynamics and associated increasing complexity of company structures and their production processes, manufacturing companies are forced to adapt to this environment. Information technology is thereby a key for manufacturing companies to regain sovereignty over their own production processes. Digital networking via their own company and as well, the overall supply chain, can only succeed if digital planning reflects reality as accurately as possible and if production control can react to deviations in real time. In essence, this leads to a development of process control towards process regulation. While long-term production and resource planning is usually mapped by Enterprise Resource Planning (ERP) systems, detailed planning, including short-term deviations and real-time data at the production level, is increasingly supported by Manufacturing Execution Systems (MES) at the production control level. However, in order to bring the underlying system concepts into line with Industry 4.0 efforts in a standardized manner, mutual functional integration within the framework of interoperable production planning and control is of crucial importance. For this purpose, studies were carried out in particular into cause-effect relationships. Thus, the overarching research objective is a valid design model to increase the controllability of production planning and control systems (PPC) in the context of Industry 4.0.

The modular construction business model in Indonesia remains fragmented and lacks end-to-end integration, which hinders its efficiency and scalability. This study aims to compare the existing business model with an integrated ideal model, while identifying value chain inefficiencies caused by industry fragmentation. The research method involves mapping the Business Model Canvas elements and analyzing the value chain of modular construction. The findings reveal inefficiencies due to the separation of design and production processes, limited partner manufacturing facilities, and the absence of national standards. This study recommends vertical and horizontal integration strategies to enhance coordination, standardization, and overall efficiency of the national modular construction system in response to sustainably growing large-scale demand.

Production system take an important role in industries, especially in manufacturing industries. This role determine the keys of successful company. Production process is an activity which produce finished product from raw material that involve machine, energy, and technique knowledge. Production process is real activity and can be seen by human being. The problems those always be faced in indutries management’s are the arrangement of production schedule, such lack of inventory or overstock once the settlement of production process isn’t on time. Production planning and control is activity to determine what product that will be produced, how many product that will be produced and how many labors needed in production processes. By using production planning and control’s method, those problems can be minimalized. Aggregate planning is one of production planning a.nd control’s method. By using this method, production planning could be done by using unit of replacement product so that the output of this planning isn’t declared in individual product. So, the output of aggregate planning isn’t planning in form of individual product but aggregate’s product. There are some strategies on aggregate planning such as pure strategy on aggregate planning and mixed strategy on aggregate planning. In this research, method of aggregate planning that used is optimization approach by linier rule. This method used to make long term planning and middle term planning. Long term planning consists of product forecasting and aggregate planning. The middle term planning consist of master production schedule and rough cut capacity planning. The result of this research is capacity needed and capacity available to determine which work center is drum and which isn’t. Conclusion of this research that capacity of each work center in perioad january to december 2018 is non drum. This indicate the good scheduling in capacity planning.

Develops a linear programming model for integrated production planning based on the practice of a major Canadian steel making company. Considers the entire planning activity in the company as an integrated process involving a number of closely related sub‐functions, such as raw material purchasing, semi‐finished product purchasing and production, and capacity allocation, as well as finished product production and distribution. The mathematical programming model takes into account production costs, product throughput rates, customer demands, sales prices and facility capacities for optimal production planning. Presents a numerical example based on realistic system structure and practical planning data to illustrate the model. Computation results and analysis show that the integrated methodology is a feasible and practical approach for steel production planning.

To compete with alternative production methods, sheet metal working firms need to improve continuously. Improvement efforts do not solely focus on the production processes, but also on other aspects of the production chain. Production planning is one of those aspects that need to be optimized. The presented research focuses on production planning optimization for sheet metal shops with a cutting stage and a bending stage. The combination of the production plans of the individual processes does not result in a globally optimal production plan. Consequently, both processes need to be integrated for production planning. In this paper, an integer programming formulation is presented for the multiple-machine two-stage sheet metal shop production planning problem. Numerous real-life test cases are used to benchmark the approach against the current way of planning. To limit the computational time, a dedicated variable neighborhood search procedure is presented.