Applied Stochastic Models in Business and Industry最新文献

{"title":"A Comprehensive Degradation Modeling Comparison From Statistical to Artificial Intelligence Models for Curing Oven Chains","authors":"Hasan Misaii,&nbsp;Amélie Ponchet Durupt,&nbsp;Hai Canh Vu,&nbsp;Nassim Boudaoud,&nbsp;Patrick Leduc,&nbsp;Yun Xu,&nbsp;Arnaud Caracciolo","doi":"10.1002/asmb.2930","DOIUrl":"https://doi.org/10.1002/asmb.2930","url":null,"abstract":"<div>\u0000 \u0000 <p>The limitations of physics-based models and the constraints posed by data-driven models have motivated the development of fusion models for degradation modeling. These fusion models are designed to overcome the shortcomings inherent to either type of these models when used in isolation. In reliability analysis, particularly for highly reliable systems or units, the available datasets often exhibit small sample sizes. In such instances, the amount of data may not suffice for training powerful data-driven models, which typically require large datasets. Additionally, physics-based models may fail to capture all relevant information present in the data. This article focuses on addressing small sample-size datasets related to highly reliable systems, exploring various statistical and machine learning models tailored for such datasets, from statistical and AI models to fusion models. Furthermore, to address the challenges of using these models in isolation, a combination approach is presented involving employing simple data-driven models accompanied by essential data preprocessing and a physics-based model. This combination enables the models to capture the majority of pertinent information within the data. Also, a time-windowed multilayer perceptron is adapted to the dataset, showing that a meticulously prepared artificial neural network model might surpass the performance of some robust data-driven and even fusion models.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Redundancy Allocation for Series and Parallel Systems: A Copula-Based Approach","authors":"Ravi Kumar,&nbsp;T. V. Rao,&nbsp;Sameen Naqvi","doi":"10.1002/asmb.2928","DOIUrl":"https://doi.org/10.1002/asmb.2928","url":null,"abstract":"<div>\u0000 \u0000 <p>The allocation of redundant components to a system is a common method for enhancing the system's lifetime. This study explores the optimal allocation of redundancies in series and parallel systems with two components by assuming components and redundancies are dependent. That is, we perform the stochastic comparisons of the series (parallel) systems in the case of two redundancies at the component level. Specifically, we examine the stochastic comparisons across three scenarios: (i) components (and redundancies) have dependent lifetimes but are independent of each other, and components (redundancies) have identical marginal distributions in the two generated systems; (ii) components (and redundancies) have dependent lifetimes and are independent of each other, but the marginal distributions of components (redundancies) are different in the two generated system; and (iii) components and redundancies are interdependent and the marginals of the components (redundancies) in the two generated systems are same. In this study, we model the dependency using the concept of copula and perform the desired stochastic comparisons using generalized distorted distribution functions. Furthermore, we demonstrate our findings through various examples and counterexamples. Finally, we provide a simulation-based study and a real data analysis to illustrate our findings.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minimising by Simulation-Based Optimisation the Cycle Time for the Line Balancing Problem in Real-World Environments","authors":"Luis A. Moncayo–Martínez,&nbsp;Naihui He,&nbsp;Elias H. Arias–Nava","doi":"10.1002/asmb.2925","DOIUrl":"https://doi.org/10.1002/asmb.2925","url":null,"abstract":"<div>\u0000 \u0000 <p>In the context of Industry 4.0, a production line must be flexible and adaptable to stochastic or real-world environments. As a result, the assembly line balancing (ALB) problem involves managing uncertainty or stochasticity. Several methods have been proposed, including stochastic mathematical programming models and simulations. However, programming models can only incorporate a few sources of uncertainty that result in impractical or unfeasible solutions to implement due to overlooked complexities, while simulation is only used to test solutions from deterministic approaches or adjust parameters without maintaining their optimum value. The proposed methodology uses a deterministic mathematical model to minimize the cycle time, followed by the simulation to measure the impact of selected sources of uncertainty on the cycle time. Finally, the optimum value of the stochastic parameters is computed using simulation-based optimization to maintain the average cycle time close to the deterministic one. A real-life assembly line balancing problem for a motorcycle manufacturing company is solved to test the proposed methodology. The sources of uncertainty are the tasks' stochastic processing times, inter-arrival time, the number of workers in each station, and the speed of the material handling system. Results show that the average cycle time is above 2.7% from the deterministic value computed by the programming model when the inter-arrival time is set to 270 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>±</mo>\u0000 </mrow>\u0000 <annotation>$$ pm $$</annotation>\u0000 </semantics></math> 60 s; the processing times are allowed to increase or decrease by 3 s; the material handling system's speed is 1.5 m/s; and the number of workers in cells is between 4 and 6, with a speed of 2 m/s. The reader can download the source code and the simulation model to apply the methodology to other instances.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Thinking in Reliability and Risk Analysis: An Overview of Nozer D. Singpurwalla's Work","authors":"Refik Soyer,&nbsp;Fabio Spizzichino","doi":"10.1002/asmb.2927","DOIUrl":"https://doi.org/10.1002/asmb.2927","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, an overview of Nozer Singpurwalla's work in reliability and risk analysis is provided. Rather than presenting a chronological review of his work, the emphasis is given to those areas of his research which better reflect Nozer's scientific personality.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Normal Deviation of Gamma Processes in Random Environment With Applications","authors":"Nikolaos Limnios","doi":"10.1002/asmb.2929","DOIUrl":"https://doi.org/10.1002/asmb.2929","url":null,"abstract":"<div>\u0000 \u0000 <p>We consider gamma processes of homogeneous type, which live in a random environment or media represented by a pure jump Markov process. The aim of this paper is to approximate such gamma processes by a diffusion. Since gamma processes are increasing, the diffusion approximation requires an average approximation first. This averaged process will serve as an equilibrium to the initial gamma process. We present two main results: averaging and normal deviation. An application for degradation systems in reliability modeling is discussed.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Probability Functions for Scenarios Arising From Multi-State Series and Multi-State Parallel Systems","authors":"Leena Kulkarni,&nbsp;Sanjeev Sabnis,&nbsp;Sujit K. Ghosh","doi":"10.1002/asmb.2922","DOIUrl":"https://doi.org/10.1002/asmb.2922","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;Consider multi-state series and multi-state parallel systems consisting of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;N&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ N $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; independent components each. It is assumed that (i) each component and both the systems take values in the set &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;{&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;}&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left{0,1,2right} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, (ii) each system and each component start out in state 2, the perfect state, and they make the transition to state 1, depending upon system configuration, and, eventually, each system enters state 0, the failed state. This multi-state nature of components and systems leads to &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;N&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ N $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; scenarios under which each of the systems makes the transition from state 2 to state 1, and eventually to state 0. The joint probability function for times spent in state 2 and state 1 is obtained based on these &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;N&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ N $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; scenarios for each of the systems. It is interesting to note that by merely changing set &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;{&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;}&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left{0,1right} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; of a standard binary series (parallel) system to a set &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;{&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;}&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left{0,1,2right} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; of a multi-state series (multi-state parallel) system, renders expression of the joint probability function of system spending times in state 2 and state 1 of a multi-state series (multi-state parallel) system is quite complex as compared to the univariate survival probability of the binary series (parallel) system being in the functioning state. As a proof of concept, graphical comparison between these analytical joint probability functions and joint e","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Classical Inference of a Flexible Semi-Parametric Class of Distributions Under a Joint Balanced Progressive Censoring Scheme","authors":"Dhrubasish Bhattacharyya,&nbsp;Debasis Kundu","doi":"10.1002/asmb.2924","DOIUrl":"https://doi.org/10.1002/asmb.2924","url":null,"abstract":"<div>\u0000 \u0000 <p>The paper deals with the estimation procedures for the proportional hazard class of distributions under a two-sample balanced joint progressive censoring scheme. The baseline hazard function is assumed to be piecewise constant, instead of any specific form. This adds flexibility to the proposed model, and the shape of the underlying hazard function is completely data-driven. Since the complicated form of the likelihood function does not yield closed-form estimators, we propose a variant of the Expectation-Maximization algorithm, known as the Expectation Conditional Maximization (ECM) algorithm, for obtaining maximum likelihood estimates of the model parameters. This leads to explicit expressions for the iterative constrained maximization steps of the algorithm. An extension to the case when the cut points are unknown has also been considered for dealing with problems involving real data. Simulation results and illustrations using real data have also been presented.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Predictive Modeling of Chinese Yam Shape Through Bayesian Linear Modeling and Key Diameter Modification","authors":"Haifeng Zhang,&nbsp;Koki Kyo,&nbsp;Mitsuru Hachiya,&nbsp;Hideo Noda","doi":"10.1002/asmb.2921","DOIUrl":"https://doi.org/10.1002/asmb.2921","url":null,"abstract":"<p>In the development of devices for cutting Chinese yams into chunks for use as seeds, accurately measuring the yam's shape with a simple mechanism is crucial. In our prior study, we introduced a statistical approach for predicting the shape of a Chinese yam based on its key diameters. This method involves organizing sample data, estimating diameters at discrete points along the central axis, and constructing a predictive model based on these estimated diameters. However, the initial predictive model relied on separate regression models for each point, potentially leading to instability. In this article, we enhance our previous approach by incorporating a new step that refines the estimation of regression coefficients through Bayesian linear modeling methods. This modification allows for the simultaneous estimation of regression coefficients, ensuring greater stability in the reconstructed model. Additionally, we modify the method for locating key diameters. To validate the performance of the enhanced approach, we apply it to a set of samples and compare the output of the reconstructed model with that of our initial method. The results demonstrate improved stability and performance, highlighting the efficacy of the refined modeling technique.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.2921","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent Activation Limited Failure Models, Stochastic Ordering and Identifiability","authors":"Qi Jiang,&nbsp;Sanjib Basu","doi":"10.1002/asmb.2920","DOIUrl":"https://doi.org/10.1002/asmb.2920","url":null,"abstract":"<p>Limited failure or cure rate models provide generalization of lifetime models which allow the possibility of subjects or units to be cured or be failure-free. While modeling and analysis of such models are extensively studied, we study the important question of identifiability of these models. We discuss the latent and hierarchical activation cure models and establish a series of results on stochastic ordering within these models. We also establish a series of results on identifiability of these models under various conditions. Further, we demonstrate multiple cases where there models are not identifiable and illustrate the potential difficulty with these models in a simulation study.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.2920","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Machine Learning Based on Functional ANOVA Framework: Algorithms and Comparisons","authors":"Linwei Hu,&nbsp;Vijayan N. Nair,&nbsp;Agus Sudjianto,&nbsp;Aijun Zhang,&nbsp;Jie Chen,&nbsp;Zebin Yang","doi":"10.1002/asmb.2916","DOIUrl":"https://doi.org/10.1002/asmb.2916","url":null,"abstract":"<div>\u0000 \u0000 <p>In the early days of machine learning (ML), the emphasis was on developing complex algorithms to achieve best possible predictive performance. To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known to have limitations. Recently, with the recognition in regulated industries that interpretability is also important, researchers are studying algorithms that compromise on small increases in predictive performance in favor of being more interpretable. While doing so, the ML community has rediscovered the use of low-order functional ANOVA (fANOVA) models that have been known in the statistical literature for some time. This paper starts with a description of challenges with post hoc explainability. This is followed by a brief review of the fANOVA framework with a focus on models with just main effects and second-order interactions (called generalized additive models with interactions or GAMI = GAM + Interactions). It then provides an overview of two recently developed GAMI techniques: Explainable Boosting Machines or EBM and GAMI-Net. The paper proposes a new algorithm that also uses trees, as in EBM, but does linear fits instead of piecewise constants within the partitions. We refer to this as GAMI-linear-tree (GAMI-Lin-T). There are many other differences, including the development of a new interaction filtering algorithm. The paper uses simulated and real datasets to compare the three fANOVA ML algorithms. The results show that GAMI-Lin-T and GAMI-Net have comparable performances, and both are generally better than EBM.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}