Decision Analytics Journal最新文献

A p-ary Choquet-based multi-criteria decision-making approach for assessing sustainability indicators in urban development 基于p-ary choquet的城市发展可持续性指标评估多准则决策方法

Decision Analytics Journal Pub Date : 2026-03-01 Epub Date: 2026-01-05 DOI: 10.1016/j.dajour.2026.100673

Beatrice Mecca , Isabella M. Lami , Matteo Brunelli

引用次数: 0

An analytical framework for modeling urban pollutant dispersion with fractional transport dynamics 基于分级输运动力学的城市污染物扩散建模分析框架

Decision Analytics Journal Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.dajour.2026.100684

Shankar Pariyar , Jeevan Kafle

{"title":"An analytical framework for modeling urban pollutant dispersion with fractional transport dynamics","authors":"Shankar Pariyar , Jeevan Kafle","doi":"10.1016/j.dajour.2026.100684","DOIUrl":"10.1016/j.dajour.2026.100684","url":null,"abstract":"<div><div>Conventional advection–diffusion models often struggle to represent pollutant dispersion in complex urban settings such as Kathmandu, Nepal, because they cannot adequately capture memory effects and anomalous diffusion generated by turbulent airflow over intricate terrain. To address this limitation, we develop a two-dimensional time-fractional advection–diffusion equation using the Caputo fractional derivative to embed temporal memory and non-classical diffusion behavior into the transport formulation. The model incorporates a fractional-order parameter that controls subdiffusive behavior and memory effects, thereby extending the classical diffusion framework. To support this formulation, Analytical solutions are derived via eigenfunction expansions involving sine functions and Mittag–Leffler terms under Dirichlet boundary conditions. For practical implementation and verification, numerical solutions are obtained using the forward-time central-space method combined with the standard fractional approximation for the fractional derivative. Simulations demonstrate that smaller values of the fractional order lead to stronger pollutant retention, whereas the classical-order case recovers the uniform spreading behavior characteristic of classical diffusion. These findings show how the fractional order mediates the transition between anomalous and classical regimes and illustrate the distinct dispersion patterns generated by fractional-order dynamics. Overall, the framework provides a flexible methodological basis for investigating pollutant transport under anomalous diffusion conditions and offers a path toward future coupling with realistic wind fields and topographic data. The present study does not undertake the representation of the full atmospheric and terrain complexity of Kathmandu but sets up a basic framework that can be extended with meteorological inputs to study dispersion processes in complex urban basins.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100684"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An expectile-based neural network approach for mixed-frequency economic forecasting 一种基于期望的神经网络混合频率经济预测方法

Decision Analytics Journal Pub Date : 2026-03-01 Epub Date: 2025-12-11 DOI: 10.1016/j.dajour.2025.100666

Wisnowan Hendy Saputra , Dedy Dwi Prastyo , Kartika Fithriasari

{"title":"An expectile-based neural network approach for mixed-frequency economic forecasting","authors":"Wisnowan Hendy Saputra , Dedy Dwi Prastyo , Kartika Fithriasari","doi":"10.1016/j.dajour.2025.100666","DOIUrl":"10.1016/j.dajour.2025.100666","url":null,"abstract":"<div><div>Timely and accurate forecasting of Gross Domestic Product (GDP) is critical for economic policymaking, yet it is complicated by the need to integrate high-frequency financial indicators with low-frequency macroeconomic data and to capture complex nonlinear dynamics. This study introduces a novel model, the Expectile Regression Neural Network for Mixed-Frequency Data Sampling (ERNN-MIDAS), designed specifically to address these challenges. Methodologically, our model advances upon existing frameworks by incorporating a fully differentiable expectile-based loss function, which enables more direct and stable parameter estimation than approximation-reliant methods. Empirically, we apply the ERNN-MIDAS model to forecast Indonesian GDP growth using quarterly data from 2001 to 2024, incorporating monthly high-frequency predictors like the Financial Stress Index. The results demonstrate that our proposed model consistently and significantly outperforms a range of competing models, including the state-of-the-art QRNN-MIDAS. Specifically, in out-of-sample tests, the ERNN-MIDAS reduces the Root Mean Square Error (RMSE) by up to 20% compared to its closest competitor under the year-on-year growth approach. This superior predictive accuracy, which is robust across both training and testing datasets, highlights the practical value of our methodological refinement and establishes the ERNN-MIDAS as a powerful and reliable tool for macroeconomic forecasting.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100666"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An adaptive graph-based method for structured learning and decision analysis 基于自适应图的结构化学习和决策分析方法

Decision Analytics Journal Pub Date : 2026-03-01 Epub Date: 2026-02-12 DOI: 10.1016/j.dajour.2026.100691

Sovan Samanta , Tofigh Allahviranloo , Leo Mrsic , Antonios Kalampakas

{"title":"An adaptive graph-based method for structured learning and decision analysis","authors":"Sovan Samanta , Tofigh Allahviranloo , Leo Mrsic , Antonios Kalampakas","doi":"10.1016/j.dajour.2026.100691","DOIUrl":"10.1016/j.dajour.2026.100691","url":null,"abstract":"<div><div>Many decision-support systems operate as networks of interacting units (e.g., hospital departments, transportation hubs, or organizational teams). In such systems, different units often use different data representations and model parameters, so direct comparison of local model states across units can be unreliable. We propose the <em>Parameter Learning Quantum Graph</em> (PLQG), a graph-based framework in which each node <span><math><mi>i</mi></math></span> has its own local parameter space <span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>i</mi></mrow></msub></math></span> and each directed edge <span><math><mrow><mo>(</mo><mi>i</mi><mo>,</mo><mi>j</mi><mo>)</mo></mrow></math></span> carries a transport map <span><math><mrow><msub><mrow><mi>T</mi></mrow><mrow><mi>i</mi><mi>j</mi></mrow></msub><mo>:</mo><msub><mrow><mi>P</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>→</mo><msub><mrow><mi>P</mi></mrow><mrow><mi>j</mi></mrow></msub></mrow></math></span> that translates parameters (or decision states) into the representation used at the receiving node. PLQG defines a connection-energy regularizer that quantifies cross-unit inconsistency <em>after</em> translation and can be combined with standard local loss functions for network-wide learning and decision analysis. The framework supports time-varying graphs and explicitly distinguishes event-driven (discrete) and continuous interactions.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100691"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytical framework for sustainability assessment under stochastic conditions 随机条件下可持续性评估的分析框架

Decision Analytics Journal Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.dajour.2026.100680

Alireza Amirteimoori , Tofigh Allahviranloo , Maryam Nematizadeh , Leo Mrsic , Sovan Samanta

{"title":"An analytical framework for sustainability assessment under stochastic conditions","authors":"Alireza Amirteimoori , Tofigh Allahviranloo , Maryam Nematizadeh , Leo Mrsic , Sovan Samanta","doi":"10.1016/j.dajour.2026.100680","DOIUrl":"10.1016/j.dajour.2026.100680","url":null,"abstract":"<div><div>Measuring sustainability as an efficient tool to achieve sustainable development and improve economic, social, and environmental aspects is always fraught with complications. In this sense, developing a suitable approach for evaluating and recognizing the strengths and weaknesses across these dimensions is paramount. Given the inherent uncertainty in data for many real-world applications, the primary aim of this paper is to present a data envelopment analysis (DEA) model for evaluating sustainability within a stochastic environment. The proposed model is non-radial and incorporates undesirable outputs, enabling the assessment of overall sustainability as well as each of the economic, social, and environmental dimensions simultaneously. This multi-dimensional evaluation capability is a key advantage of the proposed model. Additionally, the proposed model is based on input excesses and output shortfalls. Another notable advantage is the incorporation of the assumption of managerial disposability when dealing with undesirable outputs. To demonstrate the applicability of the proposed model, data from 59 diverse countries across Africa, Europe, North America, and Asia were analyzed over a 12-year period (2010–2022). The country selection was designed to capture global heterogeneity in development levels, policies, and environmental conditions, allowing for robust cross-continental comparisons. Key findings reveal that: (1) Europe achieves the highest stochastic sustainability scores, while North America performs poorest; (2) Environmental sustainability shows the most success cases globally, whereas social sustainability lags; (3) Significant trade-offs exist between economic growth and environmental protection.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100680"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A data-driven problem for integrating carbon-sensitive pricing into inventory optimization 将碳敏感定价整合到库存优化中的数据驱动问题

Decision Analytics Journal Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.dajour.2026.100683

Maria Zemzami , Van Tien Duong , Nhan Quy Nguyen , Hong Nguyen Nguyen , Farouk Yalaoui

{"title":"A data-driven problem for integrating carbon-sensitive pricing into inventory optimization","authors":"Maria Zemzami , Van Tien Duong , Nhan Quy Nguyen , Hong Nguyen Nguyen , Farouk Yalaoui","doi":"10.1016/j.dajour.2026.100683","DOIUrl":"10.1016/j.dajour.2026.100683","url":null,"abstract":"<div><div>In the face of increasingly stringent carbon regulations and volatile energy markets, integrating environmental constraints into inventory optimization problems raises crucial questions: <em>Is it always economically justified?</em> And <em>what level of problem complexity is appropriate?</em> This paper proposes a data-driven threshold framework that helps businesses assess when and how to incorporate carbon emission constraints and whether to optimize for simpler or more advanced formulations. Three profit-maximizing inventory problems are developed with varying levels of carbon constraints and order cancellation flexibility and compared across a wide range of scenarios reflecting different demand, cost, and energy profiles. A novel feature of our approach is the integration of an <em>emission-sensitive dynamic pricing function</em>, linking production quantity and carbon impact directly to market demand. The preparation and simulation of data construct a model based on realistic operational conditions of manufacturing enterprises, incorporating input parameters such as inventory holding costs, energy consumption levels, average market demand, and standard deviation to replicate real-world scenarios. Using simulation, Principal Component Analysis (PCA), and machine learning classification, we identify distinct operational regions where each problem is most effective. Our results show that carbon constraints are not universally beneficial: in some contexts, simpler problems yield higher profits, while in others, more complex carbon-aware strategies are essential. The proposed framework provides both a scientific basis and actionable guidelines for managers looking to align profitability with environmental responsibility under uncertainty.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100683"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A computational approach to cognitive architecture with adaptive networks and entropy-driven dynamics 基于自适应网络和熵驱动动力学的认知架构计算方法

Decision Analytics Journal Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.dajour.2026.100692

Billel Arbaoui

{"title":"A computational approach to cognitive architecture with adaptive networks and entropy-driven dynamics","authors":"Billel Arbaoui","doi":"10.1016/j.dajour.2026.100692","DOIUrl":"10.1016/j.dajour.2026.100692","url":null,"abstract":"<div><div>This study presents a computational approach to cognitive architecture that integrates adaptive networks with entropy-driven dynamics to model the evolution and stabilization of mental states. The framework combines temporal–causal network modeling with entropy-based mechanisms inspired by Maxwell–Boltzmann distributions, allowing the system to balance local neural adaptability with global cognitive stability. Using a simulated environment, the model captures interactions among self-related cognitive constructs, including self-esteem, self-efficacy, and self-concept, and is examined through a case study on workplace self-confidence. The simulations reveal distinct dynamic patterns across scenarios characterized by high self-esteem, high self-efficacy, and high self-concept, reproducing theoretically consistent behaviors such as convergence toward stable cognitive states, sensitivity to feedback conditions, and a separation between short-term fluctuations and long-term stabilization. Overall, these results suggest that integrating adaptive networks with entropy-driven dynamics offers a biologically inspired and computationally efficient framework for modeling complex cognitive processes, with potential applications in intelligent forecasting, diagnostics, and adaptive decision-support systems, as well as a scalable basis for developing human-aligned cognitive architectures and supporting AI–human collaboration.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100692"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning framework for surrogate modeling and benchmarking in dynamic job shop scheduling 动态作业车间调度中代理建模和基准测试的机器学习框架

Decision Analytics Journal Pub Date : 2026-03-01 Epub Date: 2026-01-03 DOI: 10.1016/j.dajour.2025.100669

Guilherme Monteiro Soares , António A.C. Vieira , Yannik Zeiträg , José Rui Figueira

{"title":"A machine learning framework for surrogate modeling and benchmarking in dynamic job shop scheduling","authors":"Guilherme Monteiro Soares , António A.C. Vieira , Yannik Zeiträg , José Rui Figueira","doi":"10.1016/j.dajour.2025.100669","DOIUrl":"10.1016/j.dajour.2025.100669","url":null,"abstract":"<div><div>In dynamic job shop scheduling, simulation has long been a crucial tool for analyzing complex, time-dependent production systems under uncertainty and variability, both as a standalone decision-making tool and as a component of more complex simulation–optimization methods. However, the computational demands of simulation models of complex systems may limit their practical use, especially when rapid or large-scale experimentation is required. With this in mind, the objective of this paper is twofold. First, it proposes a machine learning-based surrogate model framework that replicates scheduling dynamics and generates synthetic data, reflecting decision-making events under various dispatching rules. This surrogate model is designed to approximate the outputs of simulation models in dynamic job shop scheduling scenarios, hence significantly reducing computational effort while maintaining accuracy. Second, it benchmarks multiple supervised learning algorithms to evaluate their capability to surrogate simulation outputs effectively, considering both predictive performance and computational efficiency (in terms of both training time and time to predict). By enabling faster performance evaluation of scheduling strategies, this approach enhances simulation-driven scheduling analysis and optimization, particularly benefiting traditional heuristic and metaheuristic methods that rely heavily on extensive simulation runs. A key contribution of this work is the comprehensive benchmarking and fine-tuning of ten supervised learning algorithms. We evaluated their predictive accuracy in approximating simulation results and their computational efficiency during model training and during prediction. Our experimental analysis identifies random forests as the most effective surrogate model with an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.91.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100669"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A data-driven entropy-based approach to analyzing power shifts in organizational decision-making 一种基于数据驱动熵的方法来分析组织决策中的权力转移

Decision Analytics Journal Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.dajour.2026.100678

Maximilian Schröer

引用次数: 0

A multi-objective open shop model for optimizing scheduling in automotive repair shops 汽车维修车间调度优化的多目标开放车间模型

Decision Analytics Journal Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.dajour.2026.100682

Mohammad Behbahani , Reza Izadbakhsh , Hamidreza Izadbakhsh

{"title":"A multi-objective open shop model for optimizing scheduling in automotive repair shops","authors":"Mohammad Behbahani , Reza Izadbakhsh , Hamidreza Izadbakhsh","doi":"10.1016/j.dajour.2026.100682","DOIUrl":"10.1016/j.dajour.2026.100682","url":null,"abstract":"<div><div>Warranty and after-sales services have become increasingly critical due to their significant cost implications for both manufacturers and consumers. Efficient scheduling in automobile repair shops plays a key role in enhancing customer satisfaction while minimizing operational inefficiencies. This study addresses the daily repair scheduling problem by formulating it as a bi-objective open shop scheduling model. The objectives are to minimize (i) both the total flow time of cars and (ii) the idle and overtime costs of repair stations. We first develop a deterministic Mixed-Integer Linear Programming (MILP) model to determine the assignment and sequencing of repair procedures across specialized stations under eligibility and capacity constraints. To solve larger instances where the MILP becomes computationally expensive, we propose a modified Non-dominated Sorting Genetic Algorithm II (NSGA-II) that uses a station-sequence chromosome, feasibility-aware decoding with conflict-repair (time-shifting) to eliminate inter-station overlaps for each car, and problem-specific crossover/mutation operators. The approach is validated on synthesized test instances and a real-world case study from a major automotive repair center in Iran. Results show that, relative to the manual schedule, the proposed method can reduce total flow time by approximately 42% and idle/overtime costs by 47%, demonstrating the value of optimization-based scheduling for automotive after-sales service operations.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100682"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0