Problem Definition: Many production systems deteriorate over time as a result of load and stress caused by production. The deterioration rate of these systems typically depends on the production ra...

/pdf/condition-based-production-planning-adjusting-production-i1kfxfr79i.pdf

Condition-Based Production Planning: Adjusting Production Rates to Balance Output and Failure Risk

Machines used in production facilities deteriorate as a result of load and stress caused by production, and thus they eventually require maintenance to stay in an operating condition. The deterioration rate of a machine typically depends on the production rate, implying that we can control its deterioration, and thereby the maintenance planning, by dynamically adjusting the production rate. The main contribution of this thesis is to integrate the two research fields of production planning and condition-based maintenance. We introduce and explore the benefits of condition-based production and maintenance policies that exploit the relation between production and deterioration to improve the trade-off between the opposing targets of having high production outputs and low maintenance costs. The obtained insights are not only interesting from a theoretical point of view, but are also practically relevant due to the ongoing developments in the fields of sensor equipment and the internet of things. These developments enable operators to remotely monitor the deterioration of equipment and to control its usage in real-time, thereby allowing to implement automated condition-based production policies.

/pdf/condition-based-production-and-maintenance-decisions-4ct407a3bc.pdf

Condition-based production and maintenance decisions

We consider a model consisting of two fluid queues driven by the same background continuous-time Markov chain, such that the rates of change of the fluid in the second queue depend on whether the first queue is empty or not: when the first queue is nonempty, the content of the second queue increases, and when the first queue is empty, the content of the second queue decreases.We analyze the stationary distribution of this tandem model using operator-analytic methods. The various densities (or Laplace–Stieltjes transforms thereof) and probability masses involved in this stationary distribution are expressed in terms of the stationary distribution of some embedded process. To find the latter from the (known) transition kernel, we propose a numerical procedure based on discretization and truncation. For some examples we show the method works well, although its performance is clearly affected by the quality of these approximations, both in terms of accuracy and run time.

Stationary distributions for a class of Markov-modulated tandem fluid queues

This study focuses on the operational and resource-constrained condition-based cleaning planning problem of integrated production and utility systems under uncertainty. For the problem under consideration, a two-stage scenario-based stochastic programming model that follows a rolling horizon modeling representation is introduced; resulting in a hybrid reactive-proactive planning approach. In the stochastic programming model, all the binary variables related to the operational status (i.e., startup, operating, shutdown, under online or offline cleaning) of the production and utility units are considered as first-stage variables (i.e., scenario independent), and most of the remaining continuous variables are second-stage variables (i.e., scenario dependent). In addition, enhanced unit performance degradation and recovery models due to the cumulative operating level deviation and cumulative operating times are presented. Terminal constraints for minimum inventory levels for utilities and products as well as maximum unit performance degradation levels are also introduced. Two case studies are presented to highlight the applicability and the particular features of the proposed approach as an effective means of dealing with the sophisticated integrated planning problem considered in highly dynamic environments.

/pdf/a-rolling-horizon-stochastic-programming-approach-for-the-13ktqt37cz.pdf

A rolling horizon stochastic programming approach for the integrated planning of production and utility systems

We consider a model consisting of two fluid queues driven by the same background continuous-time Markov chain, such that the rates of change of the fluid in the second queue depend on whether the first queue is empty or not. We analyse this tandem model using operator-analytic methods.

/pdf/analysis-of-tandem-fluid-queues-2mnlmdthct.pdf

Analysis of tandem fluid queues

Stochastic model for maintenance in continuously deteriorating systems

We construct a generalized reward matrix Z ( s ) which is an extension of the fluid generator Q ( s ) of a stochastic fluid model (SFM). We consider the generators that are projections of Z ( s ) including the generator Q ( s ), and discuss the application of the resulting generators in different contexts. One such projection, Z + ( s ), is used to define a matrix M , which records the expected number of crossings of level 0. We use the elements of M and its physical interpretation to derive an explicit equation for the matrix Ψ , which is a key building block to many other performance measures. Work on algorithmic techniques to efficiently compute M are in progress.

On the generalized reward generator for stochastic fluid models: a new equation for Ψ

We construct a generalised reward matrix Z( s ). which is an extension of the fluid generator Q(s) of a stochastic fluid model (SFM). We classify the generators that are projections of Z( s ), including the generator Q(s), and discuss the application of the resulting generators in different contexts. As one application example, for the case with nonzero mean drift, we derive a new Riccati equation for the key matrix Ψ, which records the probabilities of the first return to the original level. The Riccati equation has the form Ψ + ΨM-+Ψ = M+-, where parameters M+- and M-+ are block matrices in the matrix M, which records the expected number of visits to the original level, before the unbounded fluid drifts to ± ∞. Finally, we derive the explicit form Ψ = M+- (I + M--)-1.

/pdf/generalised-reward-generator-for-stochastic-fluid-models-2kvohyodd8.pdf

Generalised reward generator for stochastic fluid models

We apply physical interpretations to construct algorithms for the key matrix G of discrete-time quasi-birth-and-death (dtQBD) processes which records the probability of the process reaching level (...

/pdf/construction-of-algorithms-for-discrete-time-quasi-birth-and-5dhgmv5pi4.pdf

Construction of algorithms for discrete-time quasi-birth-and-death processes through physical interpretation

We apply physical interpretations to construct algorithms for the key matrix &#119814; in discrete-time quasi-birth-and-death (dtQBD) and its &#120015;-transform &#119814;(&#120015;), motivated by the work on stochastic fluid models (SFMs) in [13]. In this methodology, we first write a summation expression for &#119814;(&#120015;) by considering a physical interpretation similar to that of an algorithm in [13]. Next, we construct the corresponding iterative scheme, and prove its convergence to &#119814;(&#120015;). In particular, here we consider the physical interpretation of Algorithm 1 for &#120511;(&#119904;) in [13], and use a similar physical interpretation for &#119814;(&#120015;) partitioned into three sections, each expressed in terms of matrices analogous to block matrices in the fluid generator &#119824;(&#119904;) in stochastic fluid models.

/pdf/construction-of-algorithms-for-discrete-time-quasi-birth-and-d8xdutmeb6.pdf

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Aviva Samuelson

Author Tools

Chat about Author

Papers

Stochastic model for maintenance in continuously deteriorating systems

On the generalized reward generator for stochastic fluid models: a new equation for Ψ

Generalised reward generator for stochastic fluid models

Construction of algorithms for discrete-time quasi-birth-and-death processes through physical interpretation

Construction of algorithms for discrete-time quasi-birth-and-death processes through physical interpretation