Scheduling Management Methods Research Articles

Two-sided resource-constrained assembly line balancing problem: a new mathematical model and an improved genetic algorithm

Two-sided assembly lines are typically employed in the production of medium and large-sized products with the aim of reducing the length of the assembly line, enhancing assembly efficiency and consequently reducing the time required for product assembly. However, traditional Two-sided assembly lines lack effective resource scheduling management methods in production scheduling, which results in low productivity and high resource costs. In order to address this issue, we propose a new two-sided resource-constrained assembly line balancing problem (TRCLBP) model. The model takes the minimum number of workstations and the minimum assembly cost as its objective function and proposes an improved genetic algorithm (I-GA) to solve it. A three-layer chromosome initialization method is proposed for the assembly tasks and resource decisions, which effectively improves the diversity and quality of the initial population. Furthermore, the algorithm employs strategies such as matching crossover and redistributing variants to ensure rapid convergence of the populations and to prevent them from falling into local optimums. Finally, the efficacy of the model and algorithm proposed in this paper is validated through a comprehensive analysis of arithmetic case studies and enterprise engineering examples. This analysis reveals a reduction of approximately 18 % in the total cost of assembly. Furthermore, the model enables enterprises to make informed decisions regarding the optimal allocation of resources, thereby reducing production costs and improving the efficiency of assembly operations during periods of expansion.

A construction schedule management method of large-scale construction project based on BIM model

In order to overcome the problems of long response time and small number of manageable indicators existing in traditional construction project schedule management methods, a new construction schedule management method based on BIM model is designed in this paper. The construction progress data acquisition and decoding module circuit is set to complete the construction progress data acquisition, and the K-means algorithm is used to preprocess the construction progress data. First, the construction project progress is decomposed; then, the large-scale construction project is divided into different progress management levels by the WBS analysis method; a functional information module is established; the construction project progress data is imported into the BIM model; and finally, the BIM information function management of the method is realised. The experimental results show that the proposed method has low response time and multiple schedule management indicators, and the shortest response time of the proposed method is only 1.1 s.

A construction schedule management method of large-scale construction project based on BIM model

A construction schedule management method of large-scale construction project based on BIM model

A construction schedule management method of large-scale construction project based on BIM model

A construction schedule management method of large-scale construction project based on BIM model

Intelligent Construction Optimization Control of Construction Project Schedule Based on the Fuzzy Logic Neural Network Algorithm

At present, the field of construction engineering is limited by various situations, such as complex construction environments and many uncertain factors. Therefore, on the basis of the engineering network diagram, this paper proposes a construction project schedule management method based on the fuzzy logic neural network algorithm. By building a neural network, a large amount of historical data is input, and computers are allowed to calculate the key routes, thus predicting the construction period, and a construction project in a city is taken as an example for simulation experiments. The traditional construction period management scheme expects a construction period of 55 days. The planned construction period optimized by the project management technology integrating fuzzy logic neural network algorithm is 55 days, which is 2 days less than the traditional construction project schedule management technology and will not cause construction period delay. The simulation results show that this algorithm is more accurate and more efficient in calculating the key lines when dealing with large-scale projects, which can help the construction unit to quickly find the optimal strategy and effectively reduce the construction delay and capital loss caused by uncertainty factors.

Analysis of Project Schedule Management Method Based on Critical Chain Method

Analysis of Project Schedule Management Method Based on Critical Chain Method

TIME REDUCTION EFFECT OF THE ENHANCED TACT METHOD FOR HIGH-RISE RESIDENTIAL BUILDINGS

The finish work on high-rise residential buildings is performed simultaneously with mechanical and electrical construction work, which results in frequent work interference and delay. This significantly hinders efficient schedule management. Critical Path Method (CPM) is inefficient when applied to high-rise residential building projects in which work is repeatedly carried out for each floor. Line-of-Balance (LOB) is widely used for its effectiveness in managing repetitive work. LOB was developed into TACT and eTACT (enhanced-TACT) to combine heterogeneous works con­tinuously. In particular, the eTACT schedule management method has an advantage in that it is capable of systemati­cally connecting detailed construction, mechanical and electrical construction projects using a work planning template. This study evaluates the time reduction effect of the eTACT method for a high-rise residential building. A comparative analysis of data on 102 cases of non-applied projects and 44 cases of projects using the eTACT method over a period of 10 years is presented to verify its effectiveness. The result shows that finish work time was reduced by 25% or about 53 days on average.

프로세스 개선을 위한 프로세스 관리 환경

지식 기반 사회가 구축되면서 처리해야 할 업무 프로세스의 규모가 커지고 분석할 정보의 양이 증가함으로 인해 프로세스 관리와 개선에 대한 필요성이 높아지고 있다. 이에, 각 기업은 비즈니스 모델에 적합한 프로세스 모델을 조직 환경에 구축하고자 노력하고 있다. 본 논문에서는 기업의 생존 전략을 성공적으로 실현할 수 있도록 프로세스 관리 및 개선 환경을 제안하고 이를 구현한다. 프로세스 관리 및 개선 방법으로 프로세스 개선 방법론인 6시그마 프로세스를 적용하여 프로세스 상에서 생성되는 프로세스 정보 및 구조의 상호작용을 파악할 수 있게 하였다. 이를 기반으로 기업은 가치사슬 관점에서 베스트 프랙틱스 프로세스를 전개할 수 있게 된다. 그리고 프로세스 수행 활동에 대한 데이터 측정을 위해 PSP의 일정 관리 기법을 도입하여 프로세스 수행의 주체인 업무 담당자의 역량에 대한 정확한 데이터의 수집과 반영이 가능하도록 했다. 이를 기반으로 개인은 프로세스의 정확한 수행과 예방적 결함 관리가 가능하게 되어 업무 생산성을 향상시킬 수 있게 된다. As the knowledge-based society has been constructed, the size of work process that has to be done grows bigger and the amount of the information that has to be analyzed increases. So each company is trying to construct more conformable process models in business model. This study helps to understand the interaction between Process data and structure which are collected on the profess by appling six Sigma as a process improving methodology. On Six Sigma, businesses tn practice Best Practice Process with the view of Value Chain To measure data for the process performing action, introducing Schedule management method makes it possible to collect and reflect accurate data of workers' ability. By this method, efficiency in production will be improved because workers we able to perform the process correctly and preliminary management for defects.