451 research outputs found
Statistical Inference for a Virtual Age Reliability Model
During the lifetime of a system, repairs may be performed when the system fails. It is most common to assume either perfect repair or minimal repair. However, a repair actually will sometimes be between minimal repair and perfect repair, which is called imperfect repair. The Kijima type I virtual age model can be used to model these types of repairable systems. This model contains a parameter which reflects the restoration level after each repair.
This thesis considers statistical inference for the Kijima type I model, which deals with repairable systems that can be restored to the operating state through system replacement or repair after the system fails. We present Bayesian analysis for the Kijima type I virtual age model, including consideration of the system's overall time to failure if a given number of repairs is possible. We use both Bayesian analysis, which specifies a single prior distribution, and a robust Bayesian analysis approach. A set of prior distributions is used in robust Bayesian analysis in order to deal with uncertainty regarding prior knowledge of the Kijima type I model parameters in a flexible way and to enhance the objectivity of the analysis in an imprecise Bayesian framework by computing predictive posterior distribution bounds for the reliability function of the system.
Finally, we discuss the use of the developed methods to decide about optimal replacement. Optimal replacement is the methodology of replacing a system component at the most advantageous or efficient moment to increase its performance and minimize overall expected costs. Two policies are introduced with cost functions based on time and number of failures to make a decision on optimal replacement time or optimal number of failures of the system under the Kijima type I model using the Weibull distribution. These policies illustrate how the Bayesian and robust Bayesian analysis can be used for inferences about the optimal replacement and the expected total cost
Optimal maintenance of multi-component systems: a review
In this article we give an overview of the literature on multi-component maintenance optimization. We focus on work appearing since the 1991 survey "A survey of maintenance models for multi-unit systems" by Cho and Parlar. This paper builds forth on the review article by Dekker et al. (1996), which focusses on economic dependence, and the survey of maintenance policies by Wang (2002), in which some group maintenance and some opportunistic maintenance policies are considered. Our classification scheme is primarily based on the dependence between components (stochastic, structural or economic). Next, we also classify the papers on the basis of the planning aspect (short-term vs long-term), the grouping of maintenance activities (either grouping preventive or corrective maintenance, or opportunistic grouping) and the optimization approach used (heuristic, policy classes or exact algorithms). Finally, we pay attention to the applications of the models
Evaluating maintenance policies by quantitative modeling and analysis
International audienceThe growing importance of maintenance in the evolving industrial scenario and the technological advancements of the recent years have yielded the development of modern maintenance strategies such as the condition-based maintenance (CBM) and the predictive maintenance (PrM). In practice, assessing whether these strategies really improve the maintenance performance becomes a funda-mental issue. In the present work, this is addressed with reference to an example concerning the stochastic crack growth of a generic mechanical component subject to fatigue degradation. It is shown that modeling and analysis provide information useful for setting a maintenance policy
Spare parts planning and control for maintenance operations
This paper presents a framework for planning and control of the spare parts supply chain inorganizations that use and maintain high-value capital assets. Decisions in the framework aredecomposed hierarchically and interfaces are described. We provide relevant literature to aiddecision making and identify open research topics. The framework can be used to increasethe e¿ciency, consistency and sustainability of decisions on how to plan and control a spareparts supply chain. This point is illustrated by applying it in a case-study. Applicability of theframework in di¿erent environments is also investigated
Optimisation of temporal networks under uncertainty
A wide variety of decision problems in operations research are defined on temporal networks,
that is, workflows of time-consuming tasks whose processing order is constrained by precedence
relations. For example, temporal networks are used to formalise the management of projects,
the execution of computer applications, the design of digital circuits and the scheduling of
production processes. Optimisation problems arise in temporal networks when a decision maker
wishes to determine a temporal arrangement of the tasks and/or a resource assignment that
optimises some network characteristic such as the network’s makespan (i.e., the time required
to complete all tasks) or its net present value.
Optimisation problems in temporal networks have been investigated intensively for more than
fifty years. To date, the majority of contributions focus on deterministic formulations where all
problem parameters are known. This is surprising since parameters such as the task durations,
the network structure, the availability of resources and the cash flows are typically unknown
at the time the decision problem arises. The tacit understanding in the literature is that the
decision maker replaces these uncertain parameters with their most likely or expected values
to obtain a deterministic optimisation problem. It is well-documented in theory and practise
that this approach can lead to severely suboptimal decisions.
The objective of this thesis is to investigate solution techniques for optimisation problems in
temporal networks that explicitly account for parameter uncertainty. Apart from theoretical
and computational challenges, a key difficulty is that the decision maker may not be aware
of the precise nature of the uncertainty. We therefore study several formulations, each of
which requires different information about the probability distribution of the uncertain problem
parameters. We discuss models that maximise the network’s net present value and problems
that minimise the network’s makespan. Throughout the thesis, emphasis is placed on tractable
techniques that scale to industrial-size problems
Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals
Tesis por compendioDespite the continuous evolution in computers and information technology, real-world
combinatorial optimization problems are NP-problems, in particular in the domain of
planning and scheduling. Thus, although exact techniques from the Operations Research
(OR) field, such as Linear Programming, could be applied to solve optimization problems,
they are difficult to apply in real-world scenarios since they usually require too much computational
time, i.e: an optimized solution is required at an affordable computational time.
Furthermore, decision makers often face different and typically opposing goals, then resulting
multi-objective optimization problems. Therefore, approximate techniques from
the Artificial Intelligence (AI) field are commonly used to solve the real world problems.
The AI techniques provide richer and more flexible representations of real-world (Gomes
2000), and they are widely used to solve these type of problems. AI heuristic techniques
do not guarantee the optimal solution, but they provide near-optimal solutions in a reasonable
time. These techniques are divided into two broad classes of algorithms: constructive
and local search methods (Aarts and Lenstra 2003). They can guide their search processes
by means of heuristics or metaheuristics depending on how they escape from local optima
(Blum and Roli 2003). Regarding multi-objective optimization problems, the use of AI
techniques becomes paramount due to their complexity (Coello Coello 2006).
Nowadays, the point of view for planning and scheduling tasks has changed. Due to
the fact that real world is uncertain, imprecise and non-deterministic, there might be unknown
information, breakdowns, incidences or changes, which become the initial plans
or schedules invalid. Thus, there is a new trend to cope these aspects in the optimization
techniques, and to seek robust solutions (schedules) (Lambrechts, Demeulemeester, and
Herroelen 2008).
In this way, these optimization problems become harder since a new objective function
(robustness measure) must be taken into account during the solution search. Therefore,
the robustness concept is being studied and a general robustness measure has been developed
for any scheduling problem (such as Job Shop Problem, Open Shop Problem,
Railway Scheduling or Vehicle Routing Problem). To this end, in this thesis, some techniques
have been developed to improve the search of optimized and robust solutions in
planning and scheduling problems. These techniques offer assistance to decision makers
to help in planning and scheduling tasks, determine the consequences of changes, provide
support in the resolution of incidents, provide alternative plans, etc.
As a case study to evaluate the behaviour of the techniques developed, this thesis focuses
on problems related to container terminals. Container terminals generally serve
as a transshipment zone between ships and land vehicles (trains or trucks). In (Henesey
2006a), it is shown how this transshipment market has grown rapidly. Container terminals
are open systems with three distinguishable areas: the berth area, the storage yard,
and the terminal receipt and delivery gate area. Each one presents different planning and
scheduling problems to be optimized (Stahlbock and Voß 2008). For example, berth allocation,
quay crane assignment, stowage planning, and quay crane scheduling must be
managed in the berthing area; the container stacking problem, yard crane scheduling, and
horizontal transport operations must be carried out in the yard area; and the hinterland
operations must be solved in the landside area.
Furthermore, dynamism is also present in container terminals. The tasks of the container
terminals take place in an environment susceptible of breakdowns or incidences. For
instance, a Quay Crane engine stopped working and needs to be revised, delaying this
task one or two hours. Thereby, the robustness concept can be included in the scheduling
techniques to take into consideration some incidences and return a set of robust schedules.
In this thesis, we have developed a new domain-dependent planner to obtain more effi-
cient solutions in the generic problem of reshuffles of containers. Planning heuristics and
optimization criteria developed have been evaluated on realistic problems and they are
applicable to the general problem of reshuffling in blocks world scenarios.
Additionally, we have developed a scheduling model, using constructive metaheuristic
techniques on a complex problem that combines sequences of scenarios with different
types of resources (Berth Allocation, Quay Crane Assignment, and Container Stacking
problems). These problems are usually solved separately and their integration allows
more optimized solutions.
Moreover, in order to address the impact and changes that arise in dynamic real-world
environments, a robustness model has been developed for scheduling tasks. This model
has been applied to metaheuristic schemes, which are based on genetic algorithms. The
extension of such schemes, incorporating the robustness model developed, allows us to
evaluate and obtain more robust solutions. This approach, combined with the classical
optimality criterion in scheduling problems, allows us to obtain, in an efficient in way,
optimized solution able to withstand a greater degree of incidents that occur in dynamic
scenarios. Thus, a proactive approach is applied to the problem that arises with the presence
of incidences and changes that occur in typical scheduling problems of a dynamic real world.Rodríguez Molins, M. (2015). Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48545TESISCompendi
Efficient pac-learning for episodic tasks with acyclic state spaces and the optimal node visitation problem in acyclic stochastic digaphs.
The first part of this research program concerns the development of customized and easily implementable Probably Approximately Correct (PAC)-learning algorithms for episodic tasks over acyclic state spaces. The defining characteristic of our algorithms is that they take explicitly into consideration the acyclic structure of the underlying state space and the episodic nature of the considered learning task. The first of the above two attributes enables a very straightforward and efficient resolution of the ``exploration vs exploitation' dilemma, while the second provides a natural regenerating mechanism that is instrumental in the dynamics of our algorithms. Some additional characteristics that distinguish our algorithms from those developed in the past literature are (i) their direct nature, that eliminates the need of a complete specification of the underlying MDP model and reduces their execution to a very simple computation, and (ii) the unique emphasis that they place in the efficient implementation of the sampling process that is defined by their PAC property.
More specifically, the aforementioned PAC-learning algorithms complete their learning task by implementing a systematic episodic sampling schedule on the underlying acyclic state space.
This sampling schedule combined with the stochastic nature of
the transitions taking place, define
the need for efficient routing policies that will help the
algorithms complete their exploration program while minimizing, in expectation, the
number of executed episodes.
The design of an optimal policy that
will satisfy a specified pattern of arc visitation requirements in
an acyclic stochastic graph, while minimizing the expected number
of required episodes, is a challenging problem, even under the
assumption that all the branching probabilities involved are known
a priori. Hence, the sampling process that takes place in
the proposed PAC-learning algorithms gives rise to a novel, very
interesting stochastic control/scheduling problem, that is
characterized as the problem of the Optimal Node Visitation
(ONV) in acyclic stochastic digraphs. The second part of the work presented herein seeks the systematic modelling and analysis of the ONV problem.
The last part of this research program explores the computational merits
obtained by heuristical implementations that result from the integration of
the ONV problem developments into the PAC-algorithms developed in the first part of this work. We study, through numerical experimentation, the relative performance of these resulting heuristical implementations in comparison to (i) the initial version of the PAC-learning algorithms, presented in the first part of the research program, and (ii) standard Q-learning algorithm variations provided in the RL literature. The work presented in this last part reinforces and confirms the driving assumption of this research, i.e., that one can design customized RL algorithms of enhanced performance if the underlying problem structure is taken into account.Ph.D.Committee Chair: Reveliotis, Spyros; Committee Member: Ayhan, Hayriye; Committee Member: Goldsman, Dave; Committee Member: Shamma, Jeff; Committee Member: Zwart, Ber
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