Decision Analytics Journal最新文献_第9页

An exact and metaheuristic optimization framework for solving Vehicle Routing Problems with Shipment Consolidation using population-based and Swarm Intelligence 利用群体智能和蜂群智能解决货运整合车辆路线问题的精确和元智慧优化框架

Decision Analytics Journal Pub Date : 2024-09-19 DOI: 10.1016/j.dajour.2024.100517

Muhammad Khahfi Zuhanda , Hartono , Samsul A. Rahman Sidik Hasibuan , Yose Yefta Napitupulu

{"title":"An exact and metaheuristic optimization framework for solving Vehicle Routing Problems with Shipment Consolidation using population-based and Swarm Intelligence","authors":"Muhammad Khahfi Zuhanda , Hartono , Samsul A. Rahman Sidik Hasibuan , Yose Yefta Napitupulu","doi":"10.1016/j.dajour.2024.100517","DOIUrl":"10.1016/j.dajour.2024.100517","url":null,"abstract":"<div><div>The Vehicle Routing Problem with Shipment Consolidation (VRPSC) is a novel variation of the vehicle routing problem that involves multiple commodities, multiple dimensions, a fleet with different types of vehicles, and the challenge of consolidating shipments during the route. Exact algorithms have been suggested to solve the VRPSC problems. Besides exact algorithms, specific metaheuristic algorithms are employed to deliver solutions of superior quality, albeit not necessarily optimal. The Artificial Immune System (AIS) and Genetic Algorithm (GA) are the metaheuristic optimization techniques applied in this research. Genetic programming is included in evolutionary computing, while AIS is included in swarm intelligence. This research presents a vehicle routing model with product consolidation and different product dimensions. These criteria were selected due to their significant impact on the complexity of mathematical problem-solving in VRPSC. The results from applying these metaheuristic algorithms will be compared with those of exact algorithms to compare and analyse different VRPSC solution approaches.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100517"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review of the literature on deep learning approaches for cross-domain recommender systems 关于跨域推荐系统深度学习方法的文献系统回顾

Decision Analytics Journal Pub Date : 2024-09-13 DOI: 10.1016/j.dajour.2024.100518

Matthew O. Ayemowa , Roliana Ibrahim , Yunusa Adamu Bena

{"title":"A systematic review of the literature on deep learning approaches for cross-domain recommender systems","authors":"Matthew O. Ayemowa , Roliana Ibrahim , Yunusa Adamu Bena","doi":"10.1016/j.dajour.2024.100518","DOIUrl":"10.1016/j.dajour.2024.100518","url":null,"abstract":"<div><div>The increase in online information and the expanding diversity of user preferences require developing improved recommender systems. Cross-domain recommender systems (CDRS) have emerged as a favorable solution to solve issues related to cold start, data sparsity, and diversity by leveraging knowledge from the source domains. This systematic literature review delves into the latest deep learning approaches utilized for CDRS, comprehensively analyzing state-of-the-art techniques, methodologies, metrics, datasets, and applications. We systematically review selected primary studies from popular databases covering sixty-eight publications from 2019 to March 2024. The review process involved selecting relevant studies based on the predefined inclusion and exclusion criteria to ensure the inclusion of high-quality research. Key deep learning (DL) models explored include neural collaborative filtering, convolutional neural networks, recurrent neural networks, variational autoencoder, and generative adversarial networks. We also examine the hybrid models that integrate DL with traditional machine learning techniques to enhance recommendation performance. Our findings reveal that DL approaches significantly improve accuracy, cold start, and data sparsity. This review also identifies current trends and future research directions, emphasizing the potential of Artificial Intelligence (AI), transfer learning, and reinforcement learning in advancing CDRS. In our analysis, we discovered that the domains mainly utilized are movies, books, and music, respectively, and the most widely used evaluation metrics are root mean square error (RMSE) and normalized discounted cumulative gain (NDCG). Research challenges and future scope are also highlighted to assist the researchers and practitioners seeking to develop robust cross-domain recommender systems using DL techniques.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100518"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A predictive analytics model with Bayesian-Optimized Ensemble Decision Trees for enhanced crop recommendation 采用贝叶斯优化集合决策树的预测分析模型，增强作物推荐效果

Decision Analytics Journal Pub Date : 2024-09-01 DOI: 10.1016/j.dajour.2024.100516

Behnaz Motamedi, Balázs Villányi

{"title":"A predictive analytics model with Bayesian-Optimized Ensemble Decision Trees for enhanced crop recommendation","authors":"Behnaz Motamedi, Balázs Villányi","doi":"10.1016/j.dajour.2024.100516","DOIUrl":"10.1016/j.dajour.2024.100516","url":null,"abstract":"<div><p>Researchers have been working on developing new effective, reliable, and environmentally friendly crop recommendation systems. This study introduces a thorough framework for predicting crop recommendations by combining sophisticated machine learning (ML) classifiers with a multi-class classification approach. The study is intended to (1) comprehensively assess the importance of environmental alterations and soil nutrient characteristics across a variety of crop classes, (2) develop effective predictive analytics models using the fine Gaussian support vector machine (FGSVM) and coarse k-nearest neighbors (Coa-KNN) algorithms, (3) reduce the dimension of feature vectors and minimize training time (FGPCASVM-CRP) approach through principal component analysis (PCA), (4) explore and analyze a Bayesian optimized ensemble decision tree for crop recommendation prediction (BOEDT-CRP) model based on assessment specifications, and (5) compare the proposed approach with multiple ML classifiers with various hyperparameter optimization, including FGSVM, coarse Gaussian SVM (Coa-GSVM), wide neural network (WNN), trilayered neural network (TNN), Fine k-nearest neighbors (FKNN), cosine k-nearest neighbors (Cos-KNN), bagged tree ensemble (BTE), and subspace discriminant ensemble (SDE). The proposed models throughout the training and testing stages reveal outstanding results, with comparable accuracy rates of 99.5%, precision rates of 99.49% and 99.55%, recall rates of 99.49% and 98.59%, and f1-scores of 99.5% and 99.54%. The findings support the conclusion that the proposed models can significantly support farmers in intelligent crop management and harvesting, leading to enhanced production and decreased reliance on human labor.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100516"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001206/pdfft?md5=5f5e03e9e96a689f6ea7001a74227777&pid=1-s2.0-S2772662224001206-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A prescriptive analytics approach for evaluating two production systems: Simulation optimization algorithm 评估两个生产系统的规范性分析方法：模拟优化算法

Decision Analytics Journal Pub Date : 2024-09-01 DOI: 10.1016/j.dajour.2024.100513

Prashant Tiwari , David Kim , Ava Hajian , Amirehsan Ghasemi

{"title":"A prescriptive analytics approach for evaluating two production systems: Simulation optimization algorithm","authors":"Prashant Tiwari , David Kim , Ava Hajian , Amirehsan Ghasemi","doi":"10.1016/j.dajour.2024.100513","DOIUrl":"10.1016/j.dajour.2024.100513","url":null,"abstract":"<div><p>Production systems influence cost performance and carbon emissions. Environmental concerns compel companies to optimize energy efficiency in their production processes. This study explores the dilemma associated with fixed-time and fixed-lot systems during random disruptions and how these systems can improve performance. We employ simulation optimization models in business analytics using the discrete event simulation provided by the SimPy library within a Python environment. The study is based on the statistical analysis of data collected from 624,000 simulated hours. Our analysis reveals that a higher service level tilts the balance, favoring adopting a fixed-time production system in scenarios characterized by significant disruptions. A system with higher demand variability and lower standalone workstation availability (indicative of more variable production) tends to favor a fixed-time batch production approach. When workstations operate at low-capacity utilization combined with high standalone availability, the fixed-lot batch production system becomes more cost-effective. Overall, the fixed-time system demonstrates a superior capacity to accommodate higher production variability levels than the fixed-lot system. This paper contributes to the existing literature by providing simulation-optimization evidence to assess the relative efficiencies of fixed-size and fixed-time lot batch production systems. This paper considers the impact of random disruptions on operational efficiency within fixed-size lot batch production systems, highlighting the consequences of variability in lot completion times. The study also contributes to strategically selecting production systems to optimize energy usage in manufacturing processes.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100513"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001176/pdfft?md5=4fe2396960bfe74d95d0660370e931fc&pid=1-s2.0-S2772662224001176-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An exploration of the concept of constrained improvement in data envelopment analysis 数据包络分析中受限改进概念的探讨

Decision Analytics Journal Pub Date : 2024-09-01 DOI: 10.1016/j.dajour.2024.100514

Nasim Arabjazi , Pourya Pourhejazy , Mohsen Rostamy-Malkhalifeh

{"title":"An exploration of the concept of constrained improvement in data envelopment analysis","authors":"Nasim Arabjazi , Pourya Pourhejazy , Mohsen Rostamy-Malkhalifeh","doi":"10.1016/j.dajour.2024.100514","DOIUrl":"10.1016/j.dajour.2024.100514","url":null,"abstract":"<div><p>Constrained improvement refers to regulating rivalry between companies in a particular industry by defining a framework or an evaluation mechanism. Such a mechanism results in a more equitable and healthy competitive environment. The primary motivation is that the best-performing players in a particular industry improve their performance such that the rest of the contenders remain competitive. This study investigates the concept of constrained improvement from a frontier analysis perspective, develops a systematic implementation framework, and explores a novel application of sensitivity analysis in Data Envelopment Analysis (DEA). Original programming approaches are developed to discover the stability region considering a variable returns to scale. The objective is to determine the extent to which the input and output of a decision-making unit (DMU) can be improved or worsened before the configuration of the efficient frontier changes. Furthermore, the permissible change radius for the decision-making unit is identified, considering all possible change directions. The applicability of the approach is demonstrated using numerical examples.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100514"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001188/pdfft?md5=3fce8e207aba79fa112da1d52be9049f&pid=1-s2.0-S2772662224001188-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A performance and interpretability assessment of machine learning models for rainfall prediction in the Republic of Ireland 爱尔兰共和国降雨预测机器学习模型的性能和可解释性评估

Decision Analytics Journal Pub Date : 2024-09-01 DOI: 10.1016/j.dajour.2024.100515

Menatallah Abdel Azeem, Soumyabrata Dev

{"title":"A performance and interpretability assessment of machine learning models for rainfall prediction in the Republic of Ireland","authors":"Menatallah Abdel Azeem, Soumyabrata Dev","doi":"10.1016/j.dajour.2024.100515","DOIUrl":"10.1016/j.dajour.2024.100515","url":null,"abstract":"<div><p>Rainfall prediction significantly impacts agriculture, water reserves, and preparations for flooding conditions. This research examines the performance and interpretability of machine learning (ML) models for rainfall prediction in the Republic of Ireland. The study uses a brute force approach and the Leave One Feature Out (LOFO) methodology to evaluate model performance under highly correlated variables. Results reveal consistent performance across ML algorithms, with average Area Under the Curve Precision–Recall (AUC-PR) scores ranging from 0.987 to 1.000, with certain features such as atmospheric pressure and soil moisture deficits demonstrating significant influence on prediction outcomes.SHapley Additive exPlanations (SHAP) values provide insights into feature importance, reaffirming the significance of atmospheric pressure and soil moisture deficits in rainfall prediction. This study underscores the importance of feature selection and interpretability in enhancing the accuracy and usability of ML models for rainfall prediction in Ireland.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100515"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277266222400119X/pdfft?md5=17b64197d5e0eb48c92141637414cbe4&pid=1-s2.0-S277266222400119X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A generalization of the Topological Tail Dependence theory: From indices to individual stocks 拓扑尾部依赖理论的一般化：从指数到个股

Decision Analytics Journal Pub Date : 2024-08-26 DOI: 10.1016/j.dajour.2024.100512

Hugo Gobato Souto , Amir Moradi

引用次数: 0

A novel behavioral penalty function for interval goal programming with post-optimality analysis 区间目标编程的新型行为惩罚函数与后优化分析

Decision Analytics Journal Pub Date : 2024-08-22 DOI: 10.1016/j.dajour.2024.100511

Mohamed Sadok Cherif

{"title":"A novel behavioral penalty function for interval goal programming with post-optimality analysis","authors":"Mohamed Sadok Cherif","doi":"10.1016/j.dajour.2024.100511","DOIUrl":"10.1016/j.dajour.2024.100511","url":null,"abstract":"<div><p>Goal programming (GP) is a multi-objective extension of linear programming. Interval GP (IGP) is one of the earliest methods to expand the range of preferred structures in GP. The decision maker’s (DM’s) utility or preference in IGP is investigated by incorporating a widening range of underlying utility functions, commonly known as penalty functions. The basic idea of these functions is that undesirable deviations from the target levels of the goals are penalized regarding a constant or variable penalty value. The main concern with introducing the penalty functions is providing a wide range of a priori preference structures. Yet, the evaluation of how undesirable deviations are penalized based on DM’s behavioral preferences is not sufficiently addressed in the penalty function types developed in the GP literature. In real-world scenarios involving risk, the achievement levels of decision-making attributes are typically associated with the behavior of the DM. In such scenarios, the DM’s unavoidable attitude toward risk should be integrated into the decision-making process. We introduce the concept of behavioral penalty functions into the IGP approach, incorporating a risk aversion parameter tailored to the nature of each attribute to address this gap. This concept offers an innovative framework for capturing the preferences of the DMs and their various attitudes toward risk within the IGP approach. In this paper, we first introduce the concept of behavioral penalty functions. Next, we develop a behavioral utility-based IGP model. Finally, we present a portfolio selection case study to demonstrate the applicability and efficacy of the proposed procedure, followed by a post-optimality analysis and comparisons with other GP approaches.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100511"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001152/pdfft?md5=54776e19883a23fdb187347a6c1d0b14&pid=1-s2.0-S2772662224001152-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimized ensemble model for predicting average localization error of wireless sensor networks 预测无线传感器网络平均定位误差的优化集合模型

Decision Analytics Journal Pub Date : 2024-08-20 DOI: 10.1016/j.dajour.2024.100510

Isaac Kofi Nti, Sidharth Sankar Rout, Jones Yeboah

{"title":"An optimized ensemble model for predicting average localization error of wireless sensor networks","authors":"Isaac Kofi Nti, Sidharth Sankar Rout, Jones Yeboah","doi":"10.1016/j.dajour.2024.100510","DOIUrl":"10.1016/j.dajour.2024.100510","url":null,"abstract":"<div><p>Wireless sensor networks (WSNs) are widely utilized in various applications due to their compact size, cost-effectiveness, and ease of deployment. Nonetheless, one of the biggest problems in WSNs is getting a reasonable estimate of the average location error of a node at the setup in the least amount of time. Wireless sensor networks can undergo changes over time due to various external and internal factors, such as environmental conditions, network congestion, hardware failures, or software updates. When these changes occur, the network may require redesigning, which can incur significant expenses. Traditional WSNs approaches, on the other hand, have been explicitly programmed, which makes it hard for networks to respond dynamically. Therefore, machine learning (ML) techniques can be used to respond appropriately in such scenarios. In this work, we proposed an optimized ML ensemble model for (i) identifying the critical network parameters for node localization when setting up wireless sensor networks with the accuracy needed in a short amount of time and (ii) predicting the average localization error of wireless sensor networks. We used the random forest algorithm with optimized hyperparameters from different optimization techniques to predict average localization error (ALE) using independent features like node density, anchor ratio, transmission range, and iterations.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100510"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001140/pdfft?md5=6b942c3ed78c4a9da78b61d1b79fa86c&pid=1-s2.0-S2772662224001140-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A simulation-based Digital Twin for smart warehouse: Towards standardization 基于仿真的智能仓库数字双胞胎：实现标准化

Decision Analytics Journal Pub Date : 2024-08-17 DOI: 10.1016/j.dajour.2024.100509

Zakka Ugih Rizqi , Shuo-Yan Chou , Winda Nur Cahyo

{"title":"A simulation-based Digital Twin for smart warehouse: Towards standardization","authors":"Zakka Ugih Rizqi , Shuo-Yan Chou , Winda Nur Cahyo","doi":"10.1016/j.dajour.2024.100509","DOIUrl":"10.1016/j.dajour.2024.100509","url":null,"abstract":"<div><p>Cyber–physical systems are developed to meet the need to improve process flexibility, optimality, and transparency through Digital Twin (DT). Unfortunately, the application of DT is still limited in practice, and there is no standard way to achieve integration. An Asset Administration Shell (AAS) appears as a promising concept for realizing DT in a standard manner. A literature review shows that most studies only reached static DT and only considered a few specific assets, especially unmovable ones. This study contributes to DT development by proposing 3D-based computer simulation technology as dynamic DT based on the AAS Framework. The proposed concept enables DT to conduct dynamic monitoring, optimization, and direct controlling. A smart warehouse in Taiwan is used to verify the proposed concept. Assets considered include Automated Guided Vehicles (AGVs), Operator, Conveyor, Forklift, and Storage Rack. AAS structure, simulation model built in FlexSim software, and system integration architecture have been constructed. Two advantages of using simulation for DT are demonstrated: multi-objective simulation–optimization for AGV capacity planning and creating dynamic dashboards. Based on the proposed concept, industry 4.0 scenarios can be integrated comprehensively.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100509"},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001139/pdfft?md5=837afc2d20f92e8bcf3d40b38cfacc99&pid=1-s2.0-S2772662224001139-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0