About AORPMAdvances in Operation Research and Production Management (AORPM) is an open-access, peer-reviewed academic journal hosted by Center of Management Case Studies, Beijing University of Technology and published by EWA Publishing. AORPM is published irregularly. AORPM present latest theoretical and methodological discussions to bear on the scholarly works covering operation, applied mathematics and project management. Situated at the forefront of the interdisciplinary fields of operation research and production management, this journal seeks to bring together the scholarly insights centering on management, statistics, mathematical analysis, artificial intelligence and relevant subfields that trace to the discipline of operation, production management, project management, and combined fields of the aforementioned. AORPM is dedicated to the gathering of intellectual views by scholars and policymakers. The articles included are relevant for scholars, policymakers, and students of operation, management, and otherwise interdisciplinary programs.For more details of the AORPM scope, please refer to the Aim&Scope page. For more information about the journal, please refer to the FAQ page or contact info@ewapublishing.org. |
Aims & scope of AORPM are: ·Operation Research ·Reliability Engineering ·Management Science & Engineering ·Mathematical Sciences & Statistics ·Industrial Engineering ·Logistical Management & Engineering ·Artificial Intelligence |
Article processing charge
A one-time Article Processing Charge (APC) of 450 USD (US Dollars) applies to papers accepted after peer review. excluding taxes.
Open access policy
This is an open access journal which means that all content is freely available without charge to the user or his/her institution. (CC BY 4.0 license).
Your rights
These licenses afford authors copyright while enabling the public to reuse and adapt the content.
Peer-review process
Our blind and multi-reviewer process ensures that all articles are rigorously evaluated based on their intellectual merit and contribution to the field.
Editors View full editorial board

Glasgow, United Kingdom
anil.fernando @strath.ac.uk

Beijing, China
txw@bjut.edu.cn

Beijing, China
lycnbb@126.com

St Andrews, UK
bk78@st-andrews.ac.uk
Latest articles View all articles
The Olympic Games, organized by the International Olympic Committee, is the largest summer comprehensive games in the world, and its medal list has attracted much attention. The Olympic Games is a dynamic and complex system, and it is of extensive and far-reaching practical significance to establish a scientific and accurate prediction model for the competition results and to reveal the rules of medals. In this regard, this paper will address the following issues. For Problem 1, we first used Machine learning algorithms and Random Forest models. The goodness-of-fit index was used to judge the advantages and disadvantages of Random Forest, Logistic regression and XGBoost, and secondly, we predicted the number of medals won by each country and the number of medals won by each country in 2028, and with the help of the correlation analysis and the systematic clustering algorithm, we came up with the intrinsic connection between the host country, the amount of project changes and the amount of medal changes. For problem 2, we firstly adopt Bayesian Changepoint Detection monitoring model. We use Bayesian Changepoint Detection monitoring to determine the location of the effect point of "great coaches", then we use the factor of coach's contribution rate to determine the influence of coaches in national programs, and at the end of the question, we have conducted case studies on China, England and Brazil, and verified the reasonableness of the model by combining with the real situation in history. For question 3, we first summarized the model above, provided insights related to the Olympic medal count, and explained how each type of insight informs the Olympics. The host country's home field effect and international economic power were analyzed, and we thus made recommendations to the Olympics on infrastructure development, logistical experience, and so on, in order to provide for the next Olympic Games in Los Angeles, USA.
Modern marketing strategies have transformed through the combined power of Artificial Intelligence (AI) and Business Intelligence (BI) which improve customer segmentation and personalize marketing activities. This research examines how AI recommendation systems alongside BI tools influence marketing performance through customer interaction and conversion metrics. The research shows how AI and BI technologies produce effective marketing initiatives by analyzing consumer behavior data from transaction histories, browsing patterns, and social media activities. The study shows major enhancements in essential performance metrics including click-through rates and conversion rates with increased customer satisfaction when businesses implement AI-based systems over traditional marketing techniques. The research indicates that businesses using BI tools to implement AI-based customer segmentation achieve better conversion rates across different consumer demographics. Organizations that utilize both AI and BI systems can develop market advantages by improving customer targeting methods and enhancing their advertising approaches. The study offers important information that helps businesses boost their marketing performance while keeping pace with changing consumer behaviors in a competitive environment.
The application of artificial intelligence (AI) is of significant importance in driving the innovation and development of enterprises. This paper explores how AI applications affect enterprise innovation performance from a micro-level perspective. Based on the Resource-Based View (RBV) and Dynamic Resource-Based View (DRBV), the study empirically tests the impact of AI technology application on the innovation performance of manufacturing enterprises using data from A-share listed manufacturing companies between 2015 and 2023. The research results show that: (1) The application of AI significantly enhances the innovation performance of manufacturing enterprises, and this effect remains significant across various robustness tests. This suggests that the application of AI is a key driver for the efficient utilization of production factors, improving corporate competitiveness and economic growth. Manufacturing enterprises should actively adopt AI technologies to enhance their innovation capabilities and facilitate the conversion of innovation outcomes into economic benefits. (2) Innovation and R&D resources play a significant mediating role in the process by which AI applications enhance innovation performance, with the mediating effect of R&D personnel allocation being the strongest, while the mediating effect of R&D funding allocation is relatively weaker. This finding provides new insights into optimizing the allocation of innovation resources, particularly emphasizing the irreplaceable role of human capital in technological innovation. By optimizing human resource allocation, enterprises can further enhance the marginal benefits of AI applications and promote the continuous development of innovation capabilities. This study provides a theoretical foundation and practical insights for empowering manufacturing industry innovation through AI technology.
With developments in the express delivery industry, statistics indicate that by 2023, the number of parcels exceeded 132.07 billion. The efficiency of couriers in delivering packages has become a service issue for many companies. This thesis, grounded in the courier path problem, draws on the Traveling Salesman Problem (TSP) to construct a delivery route planning model. This model uses an annealing algorithm for solving. It views each destination as a point and sets a variable x to 0 (if the edge is not retained) or 1 (if retained) for any point-to-point edge. Experimental results from the model's solution reveal that the method significantly reduces delivery path length, enhances efficiency, and cuts down time and cost. The approach not only enhances competitiveness and customer satisfaction for food delivery enterprises but also offers valuable insights for optimizing urban logistics distribution.
Volumes View all volumes
2024
Volume 2July 2024
Find articlesAnnouncements View all announcements
Advances in Operation Research and Production Management
We pledge to our journal community:
We're committed: we put diversity and inclusion at the heart of our activities...
Advances in Operation Research and Production Management
The statements, opinions and data contained in the journal Advances in Operation Research and Production Management (AORPM) are solely those of the individual authors and contributors...
Indexing
The published articles will be submitted to following databases below:
