A novel approach for product makespan prediction in production life cycle

Chang, Jiantao; Kong, Xianwen; Yin, Lei

doi:10.1007/s00170-015-7093-3

Cited by 8 publications

(6 citation statements)

References 27 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their method improved the accuracy of the job cycle prediction with long operation time. Chang et al [14] analyzed the historical data of the product life cycle to construct a prediction model of back propagation neural network, which could dynamically improve the weight value and threshold value. In addition, adaptive immune algorithm was used for the optimizing forecast of makespan for an aviation company.…”

Section: A Scheduling In Cloud Manufacturing Para-digmmentioning

confidence: 99%

“…The definition of makespan is shown in (13). (14) and (15) illustrate that each operation of each sub-task can only be assigned to one machine, and each machine can process only one operation of the sub-task at the same time. Each operation in each sub-task can only be assigned to one location of machine that is indicated in (16).…”

Section: B Edge-side Scheduling Optimization Modelmentioning

confidence: 99%

See 1 more Smart Citation

Study on Edge-Cloud Collaborative Production Scheduling Based on Enterprises With Multi-Factory

Zou

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The depth development and widespread application of edge intelligence technology based on the Internet of Things has led to edge-cloud collaboration and related research. In recent years, with the rapid development of the Internet of Things and the formation of super-city groups, the management characteristics of enterprises with multiple manufacturing plants served for headquarters have become increasingly obvious. The problem of order dynamic fluctuations caused by personalized customization requirements has become more prominent, which makes it impossible to do global long-period prediction or real-time short-period response relied solely on the cloud or edge. Therefore, this paper proposes a production system scheduling framework under the edge-cloud collaborative paradigm based on the dynamic fluctuation of orders under these background, and builds an edge-cloud collaborative scheduling model, which guarantees real-time distributed scheduling at the edge. It enabled the cloud to periodically predict the total completion time of production tasks at the headquarters based on the value-added data uploaded by the edge, and to support more accurate and efficient scheduling at the edge based on the prediction results. Finally, an example analysis proved the rationality of the scheduling mechanism and the effectiveness of the scheduling model. The proposed method can provide a certain reference for task scheduling in the edge-cloud collaborative production paradigm. INDEX TERMS Production scheduling, edge-cloud collaboration, edge computing, Internet of Things.

show abstract

Section: A Scheduling In Cloud Manufacturing Para-digmmentioning

confidence: 99%

Section: B Edge-side Scheduling Optimization Modelmentioning

confidence: 99%

Study on Edge-Cloud Collaborative Production Scheduling Based on Enterprises With Multi-Factory

Zou

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In general, a project leader aims to minimize the makespan to reduce the time cost, resource consumption, and human resources as well as to maximize the profit and customer satisfaction. Such makespan minimization problems are common in many industries, such as the semiconductor industry [60], aviation industry [8], building industry [20], and design industry [29]. In these industries, makespan minimization effectively reduces their costs and increases customer satisfaction.…”

Section: Introductionmentioning

confidence: 99%

A makespan minimization problem for versatile developers in the game industry

Su¹,

Wang

2022

RAIRO-Oper. Res.

View full text Add to dashboard Cite

Today, the development of a modern video game draws upon multiple areas of expertise. Moreover, its development cost could be as high as tens of millions of dollars. Consequently, we should carefully schedule its jobs so as not to increase the total cost. However, project leaders traditionally treat developers alike or even schedule all the jobs manually. In this study, we consider a versatile-developer scheduling problem. The objective is to minimize the makespan of a game project. We propose a branch-and-bound algorithm (B&B) to generate the optimal schedules for small problem instances. On the other hand, an imperialist competitive algorithm (ICA) is proposed to obtain approximate schedules for large problem instances. Lastly, computational experiments are conducted to show the performances of both algorithms. When the problem size is small (e.g. n <= 12), B&B can generate the optimal schedules within 5 seconds. For some large problem instances (e.g., n = 600), near-optimal schedules can be obtained by ICA within 10 minutes. The final results imply that both algorithms converge quickly and are of high solution quality.

show abstract

“…By extending the technology of IoT to the field of aircraft overhaul, real-time data can be collected and processed for status monitoring, which provides a promising opportunity to predict makespan. For example, Chang et al [9] proposed self-adaptive immune genetic algorithm-dynamic back propagation neural network model to predict makespan. Raj and Jain [10] proposed an adaptive neuro-fuzzy inference system to predict makespan for a flexible manufacturing system.…”

Section: Introductionmentioning

confidence: 99%

An FSPCA-BP algorithm and its application to aircraft overhaul makespan prediction problem

Jia

Huang³

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The implementation strategy for meticulous production management of aircraft overhaul is facing barriers, such as the lack of an effective method that can achieve accurate prediction of makespan in the whole aircraft overhaul process. In this paper, a novel prediction method for makespan of aircraft overhaul by extending feature selection principal component analysis (FSPCA) and backpropagation (BP) neural network (FSPCA-BP) is proposed. Firstly, the FSPCA algorithm is developed to achieve feature selection and dimension reduction simultaneously. Then, the principal components factors obtained from the FSPCA algorithm are used as the input vectors for the BP neural network to predict the makespan of aircraft overhaul. Finally, the proposed method is demonstrated and tested in an application scenario of a partner company and is further compared with PCA-BP and BP approaches, respectively. The result shows that the FSPCA-BP approach has higher accuracy than PCA-BP and BP approaches in aircraft overhaul makespan prediction. Finally, the managerial implications of the proposed method for shop-floor departments are discussed in detail.

show abstract

A novel approach for product makespan prediction in production life cycle

Cited by 8 publications

References 27 publications

Study on Edge-Cloud Collaborative Production Scheduling Based on Enterprises With Multi-Factory

Study on Edge-Cloud Collaborative Production Scheduling Based on Enterprises With Multi-Factory

A makespan minimization problem for versatile developers in the game industry

An FSPCA-BP algorithm and its application to aircraft overhaul makespan prediction problem

Contact Info

Product

Resources

About