Decision Science Applications and Models (DSAM)

Abstract submission deadline

31 October 2025

Manuscript submission deadline

31 December 2025

Viewed by

6057

Topic Information

Dear Colleagues,

The theme “Decision Science Applications and Models” aims at providing cutting-edge methodologies, models, and case studies in the area of applied decision science, thus contributing to economic, technological, environmental, and social progress. This theme seeks to explore innovative advancements and practical applications that bridge theory and practice in decision-making methodologies across various domains.

Decision Science is an interdisciplinary field that merges principles from mathematics, statistics, computer science, artificial intelligence, economics, and behavioral science to enhance decision-making processes. The topics of interest include, but are not limited to, the following ones:

• Decision-making methodologies in the digital era: Exploring novel methodologies and frameworks for effective decision-making using technological advancements.
• Mathematical models for complex decision problems: Developing and applying mathematical models to address multifaceted decision challenges across diverse domains.
• Machine learning applications in decision science: Utilizing machine learning techniques to extract insights and optimize decision-making processes.
• Data analytics and statistics for informed decision-making: Exploring the use of data analytics and statistical methods to support informed and robust decision-making.
• AI-driven decision-making advancements: Investigating the role of artificial intelligence in shaping decision strategies and outcomes.
• Economical and behavioral aspects in decision science: Understanding customers’ behavior and biases to improve decision-making models and strategies.

We welcome original research articles, reviews, case studies, and methodological papers that provide innovative applications, theoretical advancements, and practical implementations in the field of decision science. Submissions should contribute to the thematic focus of this theme and present new insights or methodologies. We also welcome original and high-quality full papers derived from extended abstracts selected in peer-review international conferences on decision science, such as the 2024 DSA Int. Summer Conference: https://decisionsciencealliance.org/ISC-2024/ 

Prof. Dr. Daniel Riera Terrén
Prof. Dr. Angel A. Juan
Dr. Majsa Ammuriova
Dr. Laura Calvet
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Computers computers	2.6	5.4	2012	15.5 Days	CHF 1800	Submit
Informatics informatics	3.4	6.6	2014	38.1 Days	CHF 1800	Submit
Information information	2.4	6.9	2010	16.4 Days	CHF 1600	Submit
Logistics logistics	3.6	6.6	2017	28.5 Days	CHF 1400	Submit
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600	Submit
Algorithms algorithms	1.8	4.1	2008	18.9 Days	CHF 1600	Submit

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (3 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Computers Informatics Information Logistics Mathematics Algorithms

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

36 pages, 524 KiB  

Open AccessArticle

Return Strategies of Competing E-Sellers: Return Freight Insurance vs. Return Pickup Services

by Qiyuan Li, Yanli Fang and Yan Chen

Mathematics 2025, 13(2), 296; https://doi.org/10.3390/math13020296 - 17 Jan 2025

Viewed by 309

Abstract

Over the past decade, return freight insurance (RFI) and return pickup services (RPSs) have emerged as dominant return service strategies in e-commerce, particularly in China’s competitive online retail market. Despite their prominence, the strategic dynamics guiding e-sellers’ choice between these services remain underexplored. [...] Read more.

Over the past decade, return freight insurance (RFI) and return pickup services (RPSs) have emerged as dominant return service strategies in e-commerce, particularly in China’s competitive online retail market. Despite their prominence, the strategic dynamics guiding e-sellers’ choice between these services remain underexplored. This study develops a game-theoretic model to analyze the equilibrium return strategies of two horizontally competing e-sellers with varying misfit probabilities. By examining four subgames, we identify the conditions under which e-sellers converge on either RFI or RPSs. Our findings revealed that highly similar or highly differentiated products typically favor RFI due to intense price competition or reduced need for service-based competition, while moderately differentiated products lead to RPS adoption as service quality becomes a key competitive lever. Additionally, competitive pressure often drives e-sellers to adopt homogenized return strategies, particularly RPSs, to maintain market position. The equilibrium outcomes are shaped by misfit probability differences, consumer hassle costs, and cost structures, offering actionable insights into optimizing return strategies in competitive e-commerce environments. These findings provide actionable insights into optimizing return service strategies in competitive e-commerce environments and contribute to the growing literature on return policies as a competitive lever in online retail markets. Full article

(This article belongs to the Topic Decision Science Applications and Models (DSAM))

►▼ Show Figures

Figure 1

30 pages, 6072 KiB  

Open AccessArticle

Simulation-Based Optimization of Truck Appointment Systems in Container Terminals: A Dual Transactions Approach with Improved Congestion Factor Representation

by Davies K. Bett, Islam Ali, Mohamed Gheith and Amr Eltawil

Logistics 2024, 8(3), 80; https://doi.org/10.3390/logistics8030080 - 9 Aug 2024

Viewed by 1741

Abstract

Background: Container terminals (CTs) have constantly administered truck appointment systems (TASs) to effectively accomplish the planning and scheduling of drayage operations. However, since the operations in the gate and yard area of a CT are stochastic, there is a need to incorporate [...] Read more.

Background: Container terminals (CTs) have constantly administered truck appointment systems (TASs) to effectively accomplish the planning and scheduling of drayage operations. However, since the operations in the gate and yard area of a CT are stochastic, there is a need to incorporate uncertainty during the development and execution of appointment schedules. Further, the situation is complicated by disruptions in the arrival of external trucks (ETs) during transport, which results in congestion at the port due to unbalanced arrivals. In the wake of Industry 4.0, simulation can be used to test and investigate the present CT configurations for possible improvements. Methods: This paper presents a simulation optimization (SO) and simulation-based optimization (SBO) iteration framework which adopts a dual transactions approach to minimize the gate operation costs and establish the relationship between productivity and service time while considering congestion in the yard area. It integrates the use of both the developed discrete event simulation (DES) and a mixed integer programming (MIP) model from the literature to iteratively generate an improved schedule. The key performance indicators considered include the truck turnaround time (TTT) and the average time the trucks spend at each yard block (YB). The proposed approach was verified using input parameters from the literature. Results: The findings from the SO experiments indicate that, at most, two gates were required to be opened at each time window (TW), yielding an average minimum operating cost of USD 335.31. Meanwhile, results from the SBO iteration experiment indicate an inverse relationship between productivity factor (PF) values and yard crane (YC) service time. Conclusions: Overall, the findings provided an informed understanding of the need for dynamic scheduling of available resources in the yard to cut down on the gate operating costs. Further, the presented two methodologies can be incorporated with Industry 4.0 technologies to design digital twins for use in conventional CT by planners at an operational level as a decision-support tool. Full article

(This article belongs to the Topic Decision Science Applications and Models (DSAM))

►▼ Show Figures

Figure 1

23 pages, 1989 KiB  

Open AccessArticle

Optimization of Obstructive Sleep Apnea Management: Novel Decision Support via Unsupervised Machine Learning

by Arthur Pinheiro de Araújo Costa, Adilson Vilarinho Terra, Claudio de Souza Rocha Junior, Igor Pinheiro de Araújo Costa, Miguel Ângelo Lellis Moreira, Marcos dos Santos, Carlos Francisco Simões Gomes and Antonio Sergio da Silva

Informatics 2024, 11(2), 22; https://doi.org/10.3390/informatics11020022 - 19 Apr 2024

Cited by 4 | Viewed by 2210

Abstract

This study addresses Obstructive Sleep Apnea (OSA), which impacts around 936 million adults globally. The research introduces a novel decision support method named Communalities on Ranking and Objective Weights Method (CROWM), which employs principal component analysis (PCA), unsupervised Machine Learning technique, and Multicriteria [...] Read more.

This study addresses Obstructive Sleep Apnea (OSA), which impacts around 936 million adults globally. The research introduces a novel decision support method named Communalities on Ranking and Objective Weights Method (CROWM), which employs principal component analysis (PCA), unsupervised Machine Learning technique, and Multicriteria Decision Analysis (MCDA) to calculate performance criteria weights of Continuous Positive Airway Pressure (CPAP—key in managing OSA) and to evaluate these devices. Uniquely, the CROWM incorporates non-beneficial criteria in PCA and employs communalities to accurately represent the performance evaluation of alternatives within each resulting principal factor, allowing for a more accurate and robust analysis of alternatives and variables. This article aims to employ CROWM to evaluate CPAP for effectiveness in combating OSA, considering six performance criteria: resources, warranty, noise, weight, cost, and maintenance. Validated by established tests and sensitivity analysis against traditional methods, CROWM proves its consistency, efficiency, and superiority in decision-making support. This method is poised to influence assertive decision-making significantly, aiding healthcare professionals, researchers, and patients in selecting optimal CPAP solutions, thereby advancing patient care in an interdisciplinary research context. Full article

(This article belongs to the Topic Decision Science Applications and Models (DSAM))

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-3