scholarly journals OPTIMALITY OF CONTROL LIMIT MAINTENANCE POLICIES UNDER NONSTATIONARY DETERIORATION

1999 ◽  
Vol 13 (1) ◽  
pp. 55-70 ◽  
Author(s):  
Zvi Benyamini ◽  
Uri Yechiali
Keyword(s):  
Optimal Policy ◽  
Transition Probabilities ◽  
Stationary Problem ◽  
Control Limit ◽  
Maintenance Costs ◽  
Operation And Maintenance ◽  
Deterioration Process ◽  
Maintenance Policies ◽  
Deteriorating Systems ◽  
Limit Form

Control limit type policies are widely discussed in the literature, particularly regarding the maintenance of deteriorating systems. Previous studies deal mainly with stationary deterioration processes, where costs and transition probabilities depend only on the state of the system, regardless of its cumulative age. In this paper, we consider a nonstationary deterioration process, in which operation and maintenance costs, as well as transition probabilities “deteriorate” with both the system's state and its cumulative age. We discuss conditions under which control limit policies are optimal for such processes and compare them with those used in the analysis of stationary models.Two maintenance models are examined: in the first (as in the majority of classic studies), the only maintenance action allowed is the replacement of the system by a new one. In this case, we show that the nonstationary results are direct generalizations of their counterparts in stationary models. We propose an efficient algorithm for finding the optimal policy, utilizing its control limit form. In the second model we also allow for repairs to better states (without changing the age). In this case, the optimal policy is shown to have the form of a 3-way control limit rule. However, conditions analogous to those used in the stationary problem do not suffice, so additional, more restrictive ones are suggested and discussed.

scholarly journals Solving Channel Allocation by Reinforcement Learning in Cognitive Enabled Vehicular Ad Hoc Networks

2021 ◽  
Author(s):  
Yunfan Su
Keyword(s):  
Dynamic Programming ◽  
Reinforcement Learning ◽  
Optimal Policy ◽  
Ad Hoc ◽  
Transition Probabilities ◽  
Channel Allocation ◽  
Dynamic Programming Method ◽  
Learning Method ◽  
Time Intervals ◽  
Model Free

Vehicular ad hoc network (VANET) is a promising technique that improves traffic safety and transportation efficiency and provides a comfortable driving experience. However, due to the rapid growth of applications that demand channel resources, efficient channel allocation schemes are required to utilize the performance of the vehicular networks. In this thesis, two Reinforcement learning (RL)-based channel allocation methods are proposed for a cognitive enabled VANET environment to maximize a long-term average system reward. First, we present a model-based dynamic programming method, which requires the calculations of the transition probabilities and time intervals between decision epochs. After obtaining the transition probabilities and time intervals, a relative value iteration (RVI) algorithm is used to find the asymptotically optimal policy. Then, we propose a model-free reinforcement learning method, in which we employ an agent to interact with the environment iteratively and learn from the feedback to approximate the optimal policy. Simulation results show that our reinforcement learning method can acquire a similar performance to that of the dynamic programming while both outperform the greedy method.

scholarly journals Solving Channel Allocation by Reinforcement Learning in Cognitive Enabled Vehicular Ad Hoc Networks

2021 ◽  
Author(s):  
Yunfan Su
Keyword(s):  
Dynamic Programming ◽  
Reinforcement Learning ◽  
Optimal Policy ◽  
Ad Hoc ◽  
Transition Probabilities ◽  
Channel Allocation ◽  
Dynamic Programming Method ◽  
Learning Method ◽  
Time Intervals ◽  
Model Free

Vehicular ad hoc network (VANET) is a promising technique that improves traffic safety and transportation efficiency and provides a comfortable driving experience. However, due to the rapid growth of applications that demand channel resources, efficient channel allocation schemes are required to utilize the performance of the vehicular networks. In this thesis, two Reinforcement learning (RL)-based channel allocation methods are proposed for a cognitive enabled VANET environment to maximize a long-term average system reward. First, we present a model-based dynamic programming method, which requires the calculations of the transition probabilities and time intervals between decision epochs. After obtaining the transition probabilities and time intervals, a relative value iteration (RVI) algorithm is used to find the asymptotically optimal policy. Then, we propose a model-free reinforcement learning method, in which we employ an agent to interact with the environment iteratively and learn from the feedback to approximate the optimal policy. Simulation results show that our reinforcement learning method can acquire a similar performance to that of the dynamic programming while both outperform the greedy method.

SINGLE-MACHINE MULTIPLE-RECIPE PREDICTIVE MAINTENANCE

2013 ◽  
Vol 27 (2) ◽  
pp. 209-235 ◽  
Author(s):  
Yiwei Cai ◽  
John J. Hasenbein ◽  
Erhan Kutanoglu ◽  
Melody Liao
Keyword(s):  
Optimal Policy ◽  
Predictive Maintenance ◽  
Production Costs ◽  
Threshold Policy ◽  
Discrete Time Markov Chain ◽  
System A ◽  
Long Run ◽  
Monotonicity Result ◽  
Maintenance Policies ◽  
Discounted Costs

This paper studies a multiple-recipe predictive maintenance problem with M/G/1 queueing effects. The server degrades according to a discrete-time Markov chain and we assume that the controller knows both the machine status and the current number of jobs in the system. The controller's objective is to minimize total discounted costs or long-run average costs which include preventative and corrective maintenance costs, holdings costs, and possibly production costs. An optimal policy determines both when to perform maintenance and which type of job to process. Since the policy takes into account the machine's degradation status, such control decisions are known as predictive maintenance policies. In the single-recipe case, we prove that the optimal policy is monotone in the machine status, but not in the number of jobs in the system. A similar monotonicity result holds in the two-recipe case. Finally, we provide computational results indicating that significant savings can be realized when implementing a predictive maintenance policies instead of a traditional job-based threshold policy for preventive maintenances.

Benchmarking operation and maintenance costs of French healthcare facilities

2011 ◽  
Vol 9 (4) ◽  
pp. 266-281 ◽  
Author(s):  
Samer Sliteen ◽  
Halim Boussabaine ◽  
Orlando Catarina
Keyword(s):  
Maintenance Costs ◽  
Healthcare Facilities ◽  
Operation And Maintenance

Manage Successful Brownfield Applications

2018 ◽  
Author(s):  
Jian Sun ◽  
Kevin Blostic
Keyword(s):  
Mixed Mode ◽  
System Capacity ◽  
Signaling System ◽  
Maintenance Costs ◽  
Signaling Systems ◽  
Train Control ◽  
Operation And Maintenance ◽  
Track Circuit ◽  
Railroad Applications ◽  
New System

This paper provides a unique perspective on successful brownfield railroad applications. It presents realistic challenges and solutions when applying a turnkey solution with a replacement or an overlay system. Brownfield commissioning takes place when an existing infrastructure is to upgrade to a new system with a different technology than the incumbent one. As signaling systems are getting more and more complex, it is extremely important to maintain robustness in the system design as well as project execution, such as logistics, documentation, and issue reporting. Many transportation authorities are moving from their current train control signaling system to a new system to combat obsolescence issues, to gain better system capacity, and to lower operation and maintenance costs. This paper discusses brownfield commissioning in general, and also presents specific cases in migration from a track circuit interlocking system to a Communications Based Train Control (CBTC) system. These two systems have distinct characteristics that provide opportunities of coexistence, but also introduce difficulties in mixed-mode operations.

scholarly journals Analysis of Contracts to Build Energy Infrastructures to Optimize the OPEX

2020 ◽  
Vol 12 (17) ◽  
pp. 7232
Author(s):  
Jesus Javier Losada-Maseda ◽  
Laura Castro-Santos ◽  
Manuel Ángel Graña-López ◽  
Ana Isabel García-Diez ◽  
Almudena Filgueira-Vizoso
Keyword(s):  
Energy Sector ◽  
Maintenance Cost ◽  
Maintenance Costs ◽  
Execution Costs ◽  
Operation And Maintenance ◽  
Maximum Profit ◽  
Hydroelectric Plants ◽  
Operation And Maintenance Cost ◽  
Contractual Agreement ◽  
Energy Infrastructures

The employer (owner) of the project wants to obtain the maximum profit for the money invested and the consultant (contractor) will try to give less for that money. The regulation of their relationship is based on the contractual agreement, which in the energy sector is mainly based on the engineering, procurement, and construction (EPC) model. The objective of this work was to evaluate which factors should be included in the drafting of contracts, to minimize problems between the parties, and thus minimize execution costs and optimize operation and maintenance costs. Information and data on the integration of operability and maintainability criteria in contracts for 158 projects, with a total contract value of close to €40,000M, were analyzed. Several of those projects corresponded to wind, solar, and hydroelectric plants. The information collected the perception of the agents involved, and was classified according to the experience of the agents consulted in the operation and maintenance areas. Finally, the proposed criteria were prioritized. In general, the owner is willing to introduce these criteria in his contracts if they reduce the operation and maintenance cost by around 1–5%, while the contractor is interested in increasing his probability to be selected by 1–3%.

scholarly journals The Feasibility Analysis of Using a New Network Architecture to Solve the Tidal Effects

2012 ◽  
Vol 4 ◽  
pp. 51-55
Author(s):  
Wei Na Hou ◽  
Xian Wei Cao ◽  
Zhan Jun Liu
Keyword(s):  
Resource Utilization ◽  
Network Architecture ◽  
High Energy ◽  
Tidal Effect ◽  
Base Stations ◽  
Feasibility Analysis ◽  
Tidal Effects ◽  
Maintenance Costs ◽  
Operation And Maintenance ◽  
Construction Operation

To solve high cost, high energy and many other issues in the communication, propose a new network architecture, one of the important applications is to solve the "tidal effect", by BBUs uniform placement to achieve unified deployment of resources to get the purpose of saving computation. But its feasibility has not been verified, this paper will verify its feasibility by data from two aspects of changing the number of base stations and changing the different situations of the base stations. Be measured through simulation and analysis results show that the use of new network architecture can not only save computation, but also enhance the resource utilization and reduce construction, operation and maintenance costs.

A note on bias optimality in controlled queueing systems

2000 ◽  
Vol 37 (1) ◽  
pp. 300-305 ◽  
Author(s):  
Mark E. Lewis ◽  
Martin L. Puterman
Keyword(s):  
Optimal Policy ◽  
Queueing Systems ◽  
Control Limit ◽  
Lower Control Limit ◽  
Optimal Policies ◽  
Holding Cost ◽  
Bias Optimality ◽  
Number Of Customers

The use of bias optimality to distinguish among gain optimal policies was recently studied by Haviv and Puterman [1] and extended in Lewis et al. [2]. In [1], upon arrival to an M/M/1 queue, customers offer the gatekeeper a reward R. If accepted, the gatekeeper immediately receives the reward, but is charged a holding cost, c(s), depending on the number of customers in the system. The gatekeeper, whose objective is to ‘maximize’ rewards, must decide whether to admit the customer. If the customer is accepted, the customer joins the queue and awaits service. Haviv and Puterman [1] showed there can be only two Markovian, stationary, deterministic gain optimal policies and that only the policy which uses the larger control limit is bias optimal. This showed the usefulness of bias optimality to distinguish between gain optimal policies. In the same paper, they conjectured that if the gatekeeper receives the reward upon completion of a job instead of upon entry, the bias optimal policy will be the lower control limit. This note confirms that conjecture.

OPTIMAL PREVENTIVE-MAINTENANCE WARRANTY POLICIES FOR REPAIRABLE PRODUCTS WITH AGE-DEPENDENT MAINTENANCE COSTS

2005 ◽  
Vol 12 (02) ◽  
pp. 111-125 ◽  
Author(s):  
RUEY HUEI YEH ◽  
MING-YUH CHEN
Keyword(s):  
Optimal Policy ◽  
Preventive Maintenance ◽  
Life Time ◽  
Optimal Number ◽  
Closed Form Expression ◽  
Minimal Repair ◽  
Maintenance Costs ◽  
Special Cases ◽  
Age Dependent ◽  
The Impact

This paper develops a mathematical model to derive the optimal preventive maintenance warranty (PMW) policy for repairable products with age-dependent maintenance costs. Under a PMW, any product failures are rectified by minimal repair, and additional preventive maintenance actions are carried out within the warranty period. When the costs for preventive maintenance and minimal repair are age-dependent, the optimal number of preventive maintenance actions, corresponding maintenance degrees, and the maintenance schedule for designing a PMW policy are derived here such that the expected total warranty cost is minimized. Under some reasonable conditions, we show that there exists a unique optimal PMW policy in which the product is maintained periodically with the same preventive maintenance degree. Using this property, an efficient algorithm is provided to search for the optimal policy. Some related models developed in the literature are discussed and these models are in fact special cases of the model proposed in this paper. Furthermore, when the life-time distribution of a product is Weibull, a closed-form expression of the optimal policy is obtained. Finally, the impact of providing preventive maintenance is evaluated through numerical examples.

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