Case-Based Reasoning system for mathematical modelling options and resolution methods for production scheduling problems: Case representation, acquisition and retrieval

Kocsis, Tibor; Negny, Stéphane; Floquet, Pascal; Meyer, A.; Rév, Endre

doi:10.1016/j.cie.2014.09.012

Cited by 24 publications

(10 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of SBUCs expressed by a crisp number ( j * ), the similarity SM j * ð Þ XY can be measured by using the Equations ( 2)-(4). For SBUC does not in the format of crisp number (j 0 ), the similarity between the target city and stored cities can be measured by judging whether their conversion values are the same (Ahn et al, 2014;Kocsis et al, 2014;Shen, Yan, Zhang, & Shuai, 2017). For example, conversion value for landform characteristic of hills, plains, mountains, plateaus can be defined as 100, 200, 300, 400.…”

Section: Similarity Measure Of Sbucsmentioning

confidence: 99%

A new experience mining approach for improving low carbon city development

Shuai

Liu

et al. 2020

Sustainable Development

View full text Add to dashboard Cite

Developing low carbon city (LCC) has been widely appreciated as an important strategy for sustainable development. In line with this, an increasing number of cities globally have launched low carbon practices in recent years and gained various types of experience. However, it appears that existing studies do not present methods of how to use these valuable LCC experience in solving new problems. This study therefore introduces an experience mining approach to assist decision-makers in reusing previous experience when tailoring LCC development strategies. The mining approach consists of three processes, namely, collecting historical cases which have been experiencing LCC, establishing LCC experience base, and mining similar experience cases. This study innovates the existing experience mining approach by introducing a two-step mining process with considering the perspective of problem-based urban characteristics (PBUCs) and the perspective of solution-based urban characteristics (SBUCs). The application of the introduced mining approach has been demonstrated by a case study, where Shenyang's energy structure is adopted as the target problem. The new experience mining approach provides a valuable reference for decision-makers to retrieve similar cases for improving LCC development with the consideration of city characteristics.

show abstract

Section: Similarity Measure Of Sbucsmentioning

confidence: 99%

A new experience mining approach for improving low carbon city development

Shuai

Liu

et al. 2020

Sustainable Development

View full text Add to dashboard Cite

show abstract

“…uses solution found in the past for a similar problem in the present [10][11][12][13][14]. Precedent or case base (CB) serves as a knowledge base in CBR-systems, meanwhile reasoning consists of four basic stages, also known as CBR-cycle [13]: Retrieval, Reuse, Revision and Retention.…”

Section: Models and Methodsmentioning

confidence: 99%

Case-Based Reasoning Approach for Automating Control of Gas-Compressor Unit Within Gas-Compressor Station

Kuzyakov

Glukhikh

Andreeva³

2018

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Abstract. The aspects of Case-based reasoning system operation are described. The possibility of application such system to gas-compressor units within gas-compressor stations for revealing emergencies is considered. A functional diagram of intellectual case-based decision-support system is offered. The diagram is to minimize maintenance staff operation during control of technological gas-compression process, which leads to minimizing human factor influence on the quality of decision-making in the field of control in emergency cases.

show abstract

“…Firstly, since KCs are key product features that reflect typical properties of a product, they are usually utilized to find the most useful cases to help solve a target problem by calculating the similarity of KCs in available cases. Peng et al [2], Kocsis et al [3], and Zhu et al [4] used KCs as an enabler to select a similar case during an engineering design phase. While Romli et al [5] found the optimal from all possible solutions based on KCs to support sustainable product design.…”

Section: The Application Of Kcsmentioning

confidence: 99%

“…Some enterprises and scholars used a method called key characteristic (KC) control to alleviate the above problems, such as using KCs to capture the most similar instances [2][3][4][5], or using KCs as an efficient medium for communication with suppliers [6,7]. However, at present, there is no mutual definition for a KC.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of key design characteristics for complex product adaptive design

Han

Wang

et al. 2017

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Key design characteristics (KDCs) are important information related to the product and part designs, which significantly influence on the product's functions, performances, and quality. Identifying KDCs for a complex product will help designers to focus on key design parameters during the design process and rapidly obtain design schemes based on their close relationships to the product's functions, performances, and quality. Although there are some researches on key characteristic (KC) identification, most of them are focused on key process characteristics (KPCs) and few on KDCs. There also lacks a KDC identification framework to support KDC identification with better completeness and diverse usages. Adaptive design is the most important pattern of complex product design. Therefore, this paper presents a systematic method to identify KDCs for complex product adaptive design, in which KDCs can be determined by two related phases. Firstly, a product design specification (PDS)-KDC Candidates Network (PKCN) is constructed by using existing product instance data, cluster analysis, KC flow-down, and network analysis approaches. Then, the result from the first phase is used as a basis to identify KDCs for adaptive design. Three KDC identification techniques: similarity reasoning technique, breadth-first search (BFS), and the gray relational analysis approach are applied to find out KDCs from the PKCN, which are the most sensitive to the variation of a PDS. These identified KDCs can help designers to understand the relationships between KDCs and PDS and rapidly develop a design scheme. The effectiveness and the feasibility of the proposed method are verified by a case study via the development of an electric multiple unit (EMU)'s bogie.

show abstract

Case-Based Reasoning system for mathematical modelling options and resolution methods for production scheduling problems: Case representation, acquisition and retrieval

Abstract: OATAO is an open access repository that collects the work of some Toulouse researchers and makes it freely available over the web where possible.

Cited by 24 publications

References 54 publications

A new experience mining approach for improving low carbon city development

A new experience mining approach for improving low carbon city development

Case-Based Reasoning Approach for Automating Control of Gas-Compressor Unit Within Gas-Compressor Station

Identification of key design characteristics for complex product adaptive design

Contact Info

Product

Resources

About