Research on Reasoning concerning Emergency Measures for Industrial Project Scheduling Control

Xiaokang Han,Wenzhou Yan,Mei Lu,Valeria Vignali

doi:10.1155/2021/5595354

Xiaokang Han, Wenzhou Yan + Show 2 more

Open Access

https://doi.org/10.1155/2021/5595354

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Abstract

Industry is an important pillar of the national economy, and industrial projects are the most complex and difficult to manage and control in the construction industry; thus, the resource scheduling control of industrial projects is one of the core issues for industrial construction projects. The performance rate of the contract time periods of previous industrial construction projects has been very low. In scheduling control based on case-based reasoning (CBR), the goal is to implement preventive measures by referring to existing scheduling control cases and control the scheduling of resources through reasoning on emergency measures to prevent scheduling control deviations. In this paper, the rough set approach is used to represent the case feature information in a case reasoning model for industrial project scheduling control, attribute reduction is used to determine the weights of the feature attributes in the rough set representation, and the similarity between cases is calculated for case retrieval. The accuracy of the rough-set-based similarity calculation is verified through matrix similarity calculations and a visual analysis of the all closeness centrality and weighted all degree centrality of the corresponding complex network; thus, similar cases of industrial project scheduling control are identified. To verify the applicability and effectiveness of the proposed methodology, a typical coal chemical general contract project case is carried out. The rough set comprehensive similarity results were 0.733, 0.621, 0.536, 0.614, 0.559, 0.950, 0.708, 0.546, 0.733, 0.664, 0.526, and 0.743, and the matrix similarity results were 0.417, 0.583, 0.417, 0.417, 0.417, 0.833, 0.417, 0.500, 0.417, 0.500, 0.333, and 0.500. The results showed that the case retrieval accuracy of traditional matrix similarity is not as high as the rough set comprehensive similarity, so X 6 is the most similar case to the target case Y. Case retrieval results indicate that the proposed methodology can provide a good similar case selection strategy with project managers, and the final required preventive measures for the target case can be found. Based on the identified similar cases, preventive measures for scheduling control are formulated to effectively prevent scheduling deviations of industrial projects.

Highlights

With the development of science and technology, industrial construction projects are becoming increasingly large and complex
Looking for similar past projects is crucial to project management; it can help project managers learn from past experiences and avoid past mistakes. e urgent problem of industrial project is scheduling control for success. erefore, a new method of industrial project scheduling control based on reasoning concerning emergency measures is needed to ensure the contract time period performance of industrial projects
For improving the current case-based reasoning approach, especially the solution revision part, this paper proposes a learning-based adaptation strategy by fully making use of the hidden information in the case base

Summary

Introduction

With the development of science and technology, industrial construction projects are becoming increasingly large and complex. E reasoning model combines rough set theory and casebased reasoning to solve the problems of feature information representation and case similarity calculation. E proposed reasoning model for emergency measures in industrial project scheduling control is divided into four steps: feature information representation, case retrieval, case recommendation, and case base maintenance. A record of industrial project scheduling control event information will contain various types of feature information; it is necessary to determine a method of calculating the similarity in accordance with the way in which these feature attributes are expressed. In this model, the main feature expression types are numerical and symbolic.

Matrix Similarity Calculation and Complex Network

C11 Scope of total time deviation