Decision Science Applications and Models (DSAM)

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.6	5.4	2008	17.6 Days	CHF 1800
Computers computers	5.2	9.1	2012	15.4 Days	CHF 1800
Informatics informatics	5.1	9.1	2014	32.7 Days	CHF 1800
Information information	4.3	8.2	2010	18.7 Days	CHF 1800
Logistics logistics	4.4	8.1	2017	17.1 Days	CHF 1500
Mathematics mathematics	2.3	5.4	2013	17.4 Days	CHF 2600

39 pages, 7486 KB

Open AccessArticle

A Collaborative Decision-Making Model Based on Blockchain-Driven Adaptive Consensus for Public Opinion Event Response

by Yuetong Chen, Yumei Wang, Yufu Ning, Fengming Liu and Mingrui Zhou

Computers 2026, 15(8), 517; https://doi.org/10.3390/computers15080517 - 10 Aug 2026

Viewed by 249

Public opinion event response requires not only timely decisions but also transparent and trustworthy collaboration among multiple stakeholders. To address delayed responses, fragmented collaboration, and information opacity, this study first proposes a collaborative-decision model based on blockchain for public opinion event response and [...] Read more.

Public opinion event response requires not only timely decisions but also transparent and trustworthy collaboration among multiple stakeholders. To address delayed responses, fragmented collaboration, and information opacity, this study first proposes a collaborative-decision model based on blockchain for public opinion event response and then develops a blockchain-driven adaptive consensus method to improve consensus efficiency and decision quality. In the proposed model, public opinion information is mined to identify the attribute categories and weights of response alternatives, while collaborative-decision quality is evaluated by integrating decision reliability, opinion convergence, and individual comprehensive weights derived from social network influence. On this basis, smart contracts are designed to support transparent, traceable, and automated consensus processes. The adaptive consensus method dynamically terminates the consensus process by considering public opinion crisis levels and individual consensus differentiation. A utility-maximizing feedback mechanism is further introduced to improve consensus quality, and smart contracts are used to detect the adjustment willingness of inconsistent individuals and implement an elastic incentive mechanism. Case analysis and simulation experiments verify the effectiveness and robustness of the proposed model and method, showing their potential to support trustworthy collaborative decision-making in public opinion event response under uncertain and time-sensitive conditions. Full article

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

► Show Figures

Figure 1

26 pages, 3847 KB

Open AccessArticle

Decoupling Safety Stock and Replenishment Decisions: A Data-Driven Hybrid Risk-Value Framework for Resilient Industrial Inventories

by Leonardo G. Hernández Landa, Carolina Solís Peña, Juan M. Hernández Ramos and Jania A. Saucedo Martínez

Logistics 2026, 10(7), 163; https://doi.org/10.3390/logistics10070163 - 15 Jul 2026

Viewed by 828

Abstract

Background: Traditional value-based ABC inventory classification allocates protection according to economic value, overlooking operational risk factors such as demand variability, lead time, and assembly criticality, and it couples safety-stock and replenishment-cycle decisions. Methods: We propose a Hybrid Risk-Value framework that decouples [...] Read more.

Background: Traditional value-based ABC inventory classification allocates protection according to economic value, overlooking operational risk factors such as demand variability, lead time, and assembly criticality, and it couples safety-stock and replenishment-cycle decisions. Methods: We propose a Hybrid Risk-Value framework that decouples these two decisions: stock-keeping units (SKUs) are segmented by multivariate K-means clustering on operational risk variables to set safety-stock factors (Z), while ABC economic value sets replenishment cycle coverages (d). The framework is validated through stochastic discrete-event simulation on an anonymized dataset of 200 SKUs from an automotive supplier, under base, high-demand-variability, and extended-lead-time scenarios (20 replications each), and is benchmarked against both classic ABC and a coupled ABC-XYZ policy. Results: Across all scenarios, the Hybrid framework reduces average inventory investment and total logistical cost by approximately 26–28% relative to ABC (

p < 0.001

) while maintaining the service level; stockout days remain statistically unchanged except under extended lead times. The coupled ABC-XYZ benchmark performs almost identically to ABC, indicating that the gains arise from decoupling rather than from variability-based segmentation alone. Conclusions: Decoupling safety-stock and replenishment decisions offers a capital-efficient, data-driven alternative to static financial segmentation for resilient industrial inventories. Full article

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

► Show Figures

Graphical abstract

27 pages, 880 KB

Open AccessReview

Artificial Intelligence and Machine Learning in FinTech: From Predictive Analytics to Optimization Approaches

by Basel Abudari, Majsa Ammouriova and Angel A. Juan

Information 2026, 17(7), 634; https://doi.org/10.3390/info17070634 - 28 Jun 2026

Viewed by 695

Abstract

Artificial intelligence (AI) and machine learning (ML) are increasingly important in financial technology (FinTech) applications involving large datasets, uncertainty, and complex decision-making. First, this paper presents a review of AI- and ML-based approaches in FinTech from 2010 to 2025, with particular emphasis on [...] Read more.

Artificial intelligence (AI) and machine learning (ML) are increasingly important in financial technology (FinTech) applications involving large datasets, uncertainty, and complex decision-making. First, this paper presents a review of AI- and ML-based approaches in FinTech from 2010 to 2025, with particular emphasis on the relationship between predictive analytics and optimization-based decision-making. The review identifies two major research streams: (i) predictive AI/ML models for financial forecasting, stock price prediction, risk management, and fraud detection and (ii) optimization approaches for constrained financial decision problems, including portfolio optimization, asset–liability management, and risk-based decision-making. These two streams have largely evolved independently, which creates challenges in real financial environments, where uncertainty in predictions directly affects decision quality. Secondly, the paper also provides a decision-oriented perspective on how AI/ML-based predictions can support optimization under uncertainty and practical financial constraints. It highlights the role of uncertainty-aware optimization, simulation-based methods, and hybrid approaches such as simheuristics in improving the robustness of financial decision-making. Finally, the paper identifies open research directions toward integrated financial decision-support frameworks that combine predictive analytics, optimization, and simulation to address dynamic and uncertain FinTech environments. Full article

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

► Show Figures

Figure 1

30 pages, 7646 KB

Open AccessEditor’s ChoiceArticle

Development of an Integrated Optimization Model for Container Relocation and Truck Appointment Scheduling (TAS)

by Yosi Agustina Hidayat and Fariz Affandi Harahap

Logistics 2026, 10(6), 128; https://doi.org/10.3390/logistics10060128 - 4 Jun 2026

Viewed by 928

Abstract

Background: The rise in global throughput has created major challenges for container terminals and depots, especially in managing limited storage space and unsynchronized truck pickup schedules from different companies. These conditions complicate the organization, retrieval, and relocation of containers, especially when target [...] Read more.

Background: The rise in global throughput has created major challenges for container terminals and depots, especially in managing limited storage space and unsynchronized truck pickup schedules from different companies. These conditions complicate the organization, retrieval, and relocation of containers, especially when target containers are located in the middle of stacks. This study aims to develop an integrated optimization model that combines Truck Appointment Scheduling (TAS) and Restricted Block Relocation Problem (RBRP) to minimize container relocations, coordinate pickup schedules, and improve operational efficiency. Methods: An integer programming model is formulated to integrate relocation and scheduling decisions by considering practical operational constraints, including maximum stack capacity, queue length, crane movement capacity, relocation validity, prevention of redundant movements, and efficient slot utilization. Two solution schemes are evaluated, simultaneous RPRP-TAS approaches and sequential TAS-then-RBRP approaches, to minimize relocations. Results: The results show that the simultaneous approach produces fewer relocation and more stable than the sequential approach. Sensitivity analysis also confirms that the simultaneous scheme is more robust to variations in the number of containers and crane movement capacity, while maintaining comparable pickup time-shift performance. Conclusions: The simultaneous integration of RBRP and TAS provides coordinated, practical, and efficient decision support for depot and terminal operations. Full article

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

► Show Figures

Figure 1

16 pages, 259 KB

Open AccessArticle

Candidate SCOR-Linked Financial Proxies: Exploratory Evidence from a 12-Firm Panel Using SCOR_E Ratio Analysis of Supply Chain Efficiency

by Juan Roman

Logistics 2026, 10(4), 70; https://doi.org/10.3390/logistics10040070 - 25 Mar 2026

Viewed by 1197

Abstract

Background: Many SCOR performance measures rely on internal operational data, which limits empirical work using public information. Methods: This study evaluates a small set of publicly auditable, SCOR-linked ratios (SCOR_E) in a panel of 12 publicly traded firms across four sectors from 2000 [...] Read more.

Background: Many SCOR performance measures rely on internal operational data, which limits empirical work using public information. Methods: This study evaluates a small set of publicly auditable, SCOR-linked ratios (SCOR_E) in a panel of 12 publicly traded firms across four sectors from 2000 to 2022. Using firm- and year-fixed-effects panel models, the paper examines whether these candidate proxies show pre-specified directional associations within firms and whether the same ratios are associated with operating margin in parallel models. Instrumental-variable (IV) specifications are reported only as sensitivity analyses, and nearly all are weak by the paper’s reported first-stage diagnostics. Results: Accordingly, most findings are interpreted as associative rather than causal. After false-discovery-rate adjustment and weak-instrument-robust inference, only four firm–proxy pairs meet the paper’s detection criterion; all remaining estimates are treated as non-robust. Conclusions: The contribution is therefore narrow: this is a constrained exploratory screening exercise showing which candidate mappings survive the paper’s inferential filters in this sample and which do not. The results do not establish a validated cross-industry scorecard, a scalable benchmarking framework, or a basis for policy claims. Full article

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

33 pages, 3658 KB

Open AccessArticle

Personalized Canine Diet Generation Using Machine Learning and Constraint Optimization

by Aliya Kalykulova, Kuanysh Bakirov, Aruzhan Shoman, Kadyrzhan Makangali and Gulzhan Tokysheva

Informatics 2026, 13(3), 34; https://doi.org/10.3390/informatics13030034 - 25 Feb 2026

Viewed by 2321

Abstract

The growing demand for customized pet diets highlights the shortcomings of commercial dog foods designed for all breeds, especially when it comes to addressing breed-specific diseases, metabolic disorders, and health risks. This research presents the development and evaluation of a hybrid system for [...] Read more.

The growing demand for customized pet diets highlights the shortcomings of commercial dog foods designed for all breeds, especially when it comes to addressing breed-specific diseases, metabolic disorders, and health risks. This research presents the development and evaluation of a hybrid system for formulating wet canine food recipes. The system combines data on ingredients, veterinary feeds, and breed-related diseases; the architecture includes a recommendation module for ingredient selection and a linear programming block for recipe optimization, considering veterinary nutrient restrictions. The evaluation of the system included automatic classification of foods by specialization, visual analysis of recipe clustering, and comparison of formulas obtained by different models. The average precision of label recovery was 85.4% for TF-IDF and 88.2% for the E5 model. A comparison of ingredient extraction methods showed that machine learning produces more stable recipes, while the statistical approach provides greater variability. The developed system demonstrates potential for automating recipe creation, filling in missing data, and developing veterinary decision support platforms aimed at personalized diet selection based on the physiological needs of animals. Full article

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

► Show Figures

Figure 1

21 pages, 6553 KB

Open AccessArticle

Analyzing Key Factors for Warehouse UAV Integration Through Complex Network Modeling

by Chommaphat Malang and Ratapol Wudhikarn

Logistics 2026, 10(2), 28; https://doi.org/10.3390/logistics10020028 - 23 Jan 2026

Viewed by 1486

Abstract

Background: The integration of unmanned aerial vehicles (UAVs) into warehouse management is shaped by a broad spectrum of influencing factors, yet practical adoption lagged behind its potential due to scarce quantitative models of factor interdependencies. Methods: This study systematically reviewed academic [...] Read more.

Background: The integration of unmanned aerial vehicles (UAVs) into warehouse management is shaped by a broad spectrum of influencing factors, yet practical adoption lagged behind its potential due to scarce quantitative models of factor interdependencies. Methods: This study systematically reviewed academic literature to identify key factors affecting UAV adoption and explored their interrelationships using complex network and social network analysis. Results: Sixty-six distinct factors were identified and mapped into a weighted network with 527 connections, highlighting the multifaceted nature of UAV integration. Notably, two factors, i.e., Disturbance Prediction and System Resilience, were found to be isolated, suggesting they have received little research attention. The overall network is characterized by low density but includes a set of 25 core factors that strongly influence the system. Significant interconnections were uncovered among factors such as drone design, societal factors, rack characteristics, environmental influences, and simulation software. Conclusions: These findings provide a comprehensive understanding of the dynamics shaping UAV adoption in warehouse management. Furthermore, the open-access dataset and network model developed in this research offer valuable resources to support future studies and practical decision-making in the field. Full article

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

► Show Figures

Figure 1

22 pages, 997 KB

Open AccessArticle

A Decentralized Bilevel Interactive Fuzzy Approach for Socially Sustainable Agri-Food Supply Chain Management

by César J. Vergara-Rodríguez, Jairo R. Montoya-Torres and José Ruiz-Meza

Mathematics 2026, 14(2), 250; https://doi.org/10.3390/math14020250 - 9 Jan 2026

Cited by 2 | Viewed by 1215

Abstract

Agri-food supply chain management (ASCM) involves hierarchical structures in which actors make autonomous decisions and pursue objectives that may conflict with one another, thereby hindering coordination and limiting the understanding of how these decisions affect overall chain performance. This study proposes a decentralized [...] Read more.

Agri-food supply chain management (ASCM) involves hierarchical structures in which actors make autonomous decisions and pursue objectives that may conflict with one another, thereby hindering coordination and limiting the understanding of how these decisions affect overall chain performance. This study proposes a decentralized bilevel mixed-integer linear programming model (BLDPP) for ASCM, solved using an interactive fuzzy decision-making approach that integrates membership functions with multi-objective programming. The model was validated through a case study conducted on an agri-food supply chain in Colombia. The results show that the interactive fuzzy approach enabled the development of a planning scheme that achieved a 94% satisfaction level among all decision-makers, demonstrating its effectiveness in harmonizing potentially conflicting interests. Additionally, the resulting planning incorporated up to 99% of the total productive capacity of small producers into the purchasing plan, supporting their inclusion in the chain. These findings indicate that both the proposed management model and its solution approach offer a robust alternative for advancing toward socially sustainable management of agri-food supply chains. Full article

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

► Show Figures

Figure 1

15 pages, 1263 KB

Open AccessArticle

Optimizing Petroleum Products Distribution Centers Using GFA and AnyLogistix Simulation: A Case Study

by Moqbel S. Jaffal, Amjad B. Abdulghafour, Omar Ayadi and Faouzi Masmoudi

Logistics 2025, 9(2), 63; https://doi.org/10.3390/logistics9020063 - 25 May 2025

Cited by 1 | Viewed by 4433

Abstract

Background: The Petroleum Products Distribution Company in Anbar Governorate is responsible for securing and distributing petroleum products to various sectors, including transportation, agriculture, industry, and households, through over 100 gas stations. The company has faced significant challenges due to the destruction of [...] Read more.

Background: The Petroleum Products Distribution Company in Anbar Governorate is responsible for securing and distributing petroleum products to various sectors, including transportation, agriculture, industry, and households, through over 100 gas stations. The company has faced significant challenges due to the destruction of its infrastructure caused by past conflicts. These challenges have necessitated strategic decisions to design an efficient distribution network. Methods: This study aimed to assist the company in selecting the optimal location for a distribution center by evaluating four potential locations. Three of the proposed locations were suggested by the company: Ramadi, Habbaniyah, and Haqlaniyah. The fourth location, referred to as the GFA DC location, was determined through a greenfield analysis (GFA) experiment using AnyLogistix software (version 3.2.1. PLE) ALX. The simulation experiment in ALX was conducted using product data, fuel station locations, order quantities, distribution center data, and transportation and emissions data. Results: The simulation results, taking into account both practical and regulatory constraints, indicated that the Ramadi location was the most suitable for establishing the new distribution center. Conclusions: Based on the analysis, the study concluded that the Ramadi location was the optimal site for building the petroleum products distribution center in Anbar Governorate, offering a solution that aligns with the company’s goals of improving distribution efficiency and overcoming existing logistical challenges. Full article

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

► Show Figures

Figure 1

36 pages, 524 KB

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

Cited by 4 | Viewed by 2041

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 KB

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

Cited by 15 | Viewed by 8476

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 KB

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 10 | Viewed by 3979

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

Topic Menu

Topic Editors

Decision Science Applications and Models (DSAM)

Topic Information

Keywords

Participating Journals

Published Papers (12 papers)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI