Journal of Mathematics

Data-Driven Operations Research in Supply Chain Management


Publishing date
01 Mar 2022
Status
Closed
Submission deadline
05 Nov 2021

Lead Editor
Guest Editors

1University of Shanghai for Science and Technology, Shanghai, China

2Shanghai University, Shanghai, China

3Singapore Management University, Singapore

This issue is now closed for submissions.
More articles will be published in the near future.

Data-Driven Operations Research in Supply Chain Management

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Operations research widely applies existing scientific and technological knowledge and mathematical methods to solve specific problems in supply chain management and provides a basis for decision-makers to choose the best decision. The basic methods of operations research include mathematical methods, statistical methods, computer science methods, etc. In particular, optimization methods are very essential methods. In recent years, operations research has been continuously innovated and developed. New models, new theories, and new methods have emerged in the research. They have provided solutions for various complex supply chain management problems. For instance, they have solved complex supply chain management problems in terms of linear and nonlinear, continuous, and discrete, certainty and uncertainty systems.

Supply chain management research has become widely popular. However, there are still some interesting and challenging problems in technology and methods that are worth further exploring. Data-driven operations research is one of the most popular trends in current academic research. Due to the lack of a rigorous theoretical system, there is no unified definition. Data-driven is to use data as a means of production of extracted features through scientific methods and apply them to problems to be solved. Data-driven methods have certain applicability and advantages in the research of supply chain management. Therefore, there is a need for more scholars to conduct research and drive innovation in supply chain management by using operations research from a data-driven perspective.

The aim of this Special Issue is to bring together original research and review articles discussing the latest developments in data-driven operations research in supply chain management. We welcome submissions that present new ideas and discuss the future of operations research in supply chain management. Research including novel mathematical theories, methods, and applications addressing challenges in data-driven operations research within supply chain management is encouraged.

Potential topics include but are not limited to the following:

  • Operations research and regression in supply chain management
  • Operations research and clustering in supply chain management
  • Operations research and classification in supply chain management
  • Operations research and outlier detection in supply chain management
  • Data-driven supply chain management
  • Emergency management in supply chain management
  • Data envelopment analysis in supply chain management
  • Group decision-making analysis in supply chain management
  • Emergency facilities location and material scheduling in supply chain management
  • Multi-criteria decision analysis and application in supply chain management
  • Robust optimization in supply chain management
  • Stochastic optimization in supply chain management
  • Fuzzy programming in supply chain management

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 4191301
  • - Research Article

Resource Configuration Efficiency and Influencing Factors of Elderly Care Services Based on a Data-Driven DEA-Tobit Approach

Dongqing Luan | Ziqing Zhao | Yanxi Xie
  • Special Issue
  • - Volume 2021
  • - Article ID 3087066
  • - Research Article

Two-Stage Robust Optimization Model for Uncertainty Investment Portfolio Problems

Dongqing Luan | Chuming Wang | ... | Zhijie Xia
  • Special Issue
  • - Volume 2021
  • - Article ID 8038672
  • - Research Article

Simulation Study on the Evolutionary Game Mechanism of Collaborative Innovation in Supply Chain Enterprises and Its Influencing Elements

Jue-Ping Xie | Huai-Ying Lei
  • Special Issue
  • - Volume 2021
  • - Article ID 6593218
  • - Research Article

A Network Evolution Model of Credit Risk Contagion between Banks and Enterprises Based on Agent-Based Model

Pei Mu | Tingqiang Chen | ... | Meng Liu
  • Special Issue
  • - Volume 2021
  • - Article ID 3218798
  • - Research Article

Differences in the Values of the Senior Management Team, Antirisk Ability, and Innovation Performance by the Data-Driven Approach: Evidence from 841 Listed Companies in China

Guangyin Tong
  • Special Issue
  • - Volume 2021
  • - Article ID 7477314
  • - Research Article

Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing

Bingjie He | Qiaorong Dai | ... | Jinxiu Xiao
  • Special Issue
  • - Volume 2021
  • - Article ID 1891679
  • - Research Article

Optimal Administrative Response to Selfish Behaviors in Urban Public Management: The Role of Zero-Determinant Strategies

Ai Zhong Shen | Xiang Gao | Xiao Ping Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 2235049
  • - Research Article

Evaluation of Vegetable Circulation Efficiency and Analysis of Influencing Factors in Henan Province

Xueqiang Guo | Bingjun Li
  • Special Issue
  • - Volume 2021
  • - Article ID 9059213
  • - Research Article

Can the Implied Information of Options Predict the Liquidity of Stock Market? A Data-Driven Research Based on SSE 50ETF Options

Hairong Cui | Jinfeng Fei | Xunfa Lu
  • Special Issue
  • - Volume 2021
  • - Article ID 7588559
  • - Research Article

A Joint Optimization Model of Production Scheduling and Maintenance Based on Data Driven for a Parallel-Series Production Line

Kai Zhu
Journal of Mathematics
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.