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The purpose of this paper is to describe the introduction of simple assembly line balancing problems (SALBPs) under four joint combinations of two learning effects and two deterioration effects.

In this paper, effects of learning and deterioration are considered simultaneously. By the effects of learning and deterioration, it is meant that the task time of a task is defined by increasing function of its execution start time and position in the sequence.

It was shown that polynomial solutions can be obtained for SALBP under four joint combinations of two learning effects and two deterioration effects. Furthermore, this paper also discusses the adaptations of the proposed models by using known ALB problem.

In this paper, SALBPs were introduced under four joint combinations of two learning effects and two deterioration effects. Position dependent learning effect and time dependent learning effect were considered for learning effect when linear deterioration effect and non‐linear deterioration effect were taken up.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

This article aims to analyze strategically optimal maintenance actions for a multi‐component system whose deterioration is observed through a monitoring system set in place to support condition‐based maintenance.

Deterioration of a multi‐component system is modeled by a continuous‐time jump diffusion model which incorporates interaction between the components of the system. A simulation‐based optimization heuristic is developed to obtain strategically optimum maintenance actions. The methodology is applied to an illustrative example.

The article finds that the framework facilitates analyzing at a strategic level the role of degree of response to the deterioration of components for the overall functionality of a multi‐component system. The optimal solution for the illustrative example recommends a provider to perform a variety of opportunistic maintenance.

In this article, a framework is developed to determine strategically optimal maintenance actions for a multi‐component system whose deterioration is observed in real‐time through embedded monitoring units set in place to support condition‐based maintenance (CBM). The framework facilitates analyzing at a strategic level the role of degree of response to the deterioration of components for the overall functionality of a multi‐component system. A strategically optimal maintenance policy can then be enhanced to develop a detailed tactical maintenance strategy. This approach is expected to benefit the management of long‐term service agreements, where a service contract is sold bundled with a product, which makes a provider responsible for maintaining the product over a specified contract period.

Besides a tactical approach for performing maintenance, in order to stay profitable in the long‐run, a decision maker needs to assess the strategic performance of maintenance strategies adopted. This framework is a first attempt to facilitate this analysis at a strategic level for a monitoring‐enabled multi‐component system.

Pavement deterioration prediction models play a crucial role in determining maintenance strategies and future funding needs. While deterioration prediction models have been studied extensively in the past, applications of these models to local street networks have been limited. This study aims to address this gap by sharing the results of network level deterioration prediction models developed at a local level.

Network level pavement deterioration prediction models are developed using Markov chains for the local street network in Syracuse, New York, based on pavement condition rating data collected over a 15-year time period. Transition probability matrices are generated by calculating the percentage of street sections that transition from one state to another within one duty cycle. Bootstrap sampling with replacement is used to numerically generate 95% confidence intervals around the transition probability values.

The overall local street network is divided into three cohorts based on street type (i.e. avenues, streets and roads) and two cohorts based on pavement type. All cohorts demonstrated very similar deterioration trends, indicating the existence of a fast-paced deterioration mechanism for the local street network of Syracuse.

This study contributes to the body of knowledge in deterioration modeling of local street networks, especially in the absence of key predictor variables. Furthermore, this study introduces the use of bootstrap sampling with replacement method in generating confidence intervals for transition probability values.

The purpose of this paper is to develop a condition-based maintenance (CBM) model for those systems subject to the two-stage deterioration including a deterioration pitting initiation process and a deterioration pitting growth process.

Regarding environmental changes as random shocks, the effect of environmental changes on the deterioration process is considered. Then, non-homogeneous Poison process and non-stationary gamma process are introduced to model the deterioration pitting initiation process and the deterioration pitting growth process, respectively. Finally, based on the deterioration model, a CBM policy is put forward to obtain the optimal inspection interval by minimizing the expected maintenance cost rate. Numerical simulations are given to optimize the performance of the deteriorating system. Meanwhile, comparisons between a single-stage deterioration model and a two-stage deterioration model are conducted to demonstrate the application of the proposed approach.

The result of simulation verifies that the deterioration rate is not constant in the life cycle and is affected by the environment. Furthermore, the result shows that the two-stage deterioration model proposed makes up for the shortage of single-stage deterioration models and can effectively reduce system failures and unreasonable maintenance caused by optimistic prediction using single-stage deterioration models.

In practical situations, except for normal deterioration caused by internal factors, many systems are also greatly influenced by the random shocks during operation, which are probably caused by the environmental changes. What is more, most systems have self-protection ability in some extent that protects them to keep running as new ones for some time. Under such circumstances, the two-stage deterioration model proposed can effectively reduce system failures and unreasonable maintenance caused by optimistic prediction using single-stage deterioration models. In the combination with the bootstrap estimation, the paper obtains the life distributions with approximate 95 percent confidence intervals which can provide valuable information for practical system maintenance scheduling.

This paper presents a new CBM model for those systems subject to the two-stage deterioration including a deterioration pitting initiation process and a deterioration pitting growth process. Considering the effect of the environmental change on the system deterioration process, a two-stage deterioration model with environmental change factors is proposed to describe the system deterioration.

This paper models the deterioration process of a multi-component system. Each deterioration process is modelled by the Wiener process. The purposes of this paper are to address these issues and consider the cost process based on the multi-component system.

Condition-based Maintenance is a method for reducing the probability of system failures as well as the operating cost. Nowadays, a system is composed of multiple components. If the deteriorating process of each component can be monitored and then modelled by a stochastic process, the deteriorating process of the system is a stochastic process. The cost of repairing failures of the components in the system forms a stochastic process as well and is known as a cost process.

When a linear combination of the processes, which can be the deterioration processes and the cost processes, exceeds a pre-specified threshold, a replacement policy will be carried out to preventively maintain the system.

Under this setting, this paper investigates maintenance policies based on the deterioration process and the cost process. Numerical examples are given to illustrate the optimisation process.

The purpose of this paper is to determine associations between initially recorded deviations in individual bedside vital parameters that contribute to total Modified Early Warning Score (MEWS) levels 2 or 3 and further clinical deterioration (MEWS level=4).

This was a prospective study in which 27,504 vital parameter values, corresponding to a total MEWS level⩾2, belonging to 1,315 adult medical and surgical inpatient patients admitted to a 90-bed study setting at a university hospital, were subjected to binary logistic and COX regression analyses to determine associations between vital parameter values initially corresponding to total MEWS levels 2 or 3 and later deterioration to total MEWS level ⩾4, and to evaluate corresponding time intervals.

Respiratory rate, heart rate and patient age were significantly (p=0.012, p<0.001 and p=0.028, respectively) associated with further deterioration from a total MEWS level 2, and the heart rate also (p=0.009) from a total MEWS level 3. Within 24 h from the initially recorded total MEWS levels 2 or 3, 8 and 17 percent of patients, respectively, deteriorated to a total MEWS level=4. Patients initially scoring MEWS 2 had a 27 percent 30-day mortality rate if they later scored MEWS level=4, and 8.7 percent if they did not.

It is important to observe all patients closely, but especially elderly patients, if total MEWS levels 2 or 3 are tachypnoea and/or tachycardia related.

Findings might contribute to patient safety by facilitating appropriate clinical and organizational decisions on adequate time spans for early warning scoring in general ward patients.

The purpose of this paper is to review the current state of research on the various biological agents that could cause deterioration of paper materials and possible intervention strategies against these biotic agents in the tropics.

Recent literature in the tropics is reviewed to gain insight into the problems that confront libraries in the tropics as regards the biological deterioration of library materials.

The paper identifies moulds as the most important biodeteriorating agents of library materials. In addition to destroying, disfiguring and staining books, the moulds have been linked to numerous adverse human health effects that fall into three categories: allergic, toxic and infectious. The other biological agents include bacteria, insects and rodents. The important insects in tropical environment are cockroaches and termites. The warm humid tropical conditions and dirty environment trigger/promote biodeterioration processes and make book deterioration to be more pronounced in the tropics than in the temperate regions.

Physical visits to the libraries were not carried out and the conclusions reached were based on evidence from scientific literature on the subject in the tropics.

A very useful source of information on how to curb the menace of biological agents against the destruction of library materials in the tropics. These include training programmes for library staff on conservation and preservation of library collections, the creation of awareness on the issue among library users, the adoption of good house keeping practices and modification of storage environment to make them unsuitable for the biodeteriogens. It is recommended that libraries and archives in tropical countries should have preservation guidelines and integrated pest management teams and should also cooperate with scientists in relevant disciplines to be able to find lasting solutions to the problem of biodeterioration of library materials.

This paper looks into the neglected area of biological deterioration of papers which is the main information carrier in libraries. It provides very useful and practical suggestions that libraries in the tropics could adopt to tackle the menace of biological agents.

Deterioration of timber bridges can often be related to a number of deficiencies in the bridge elements, connectors and/or as a result of been in aggressive environments which they are exposed to. The maintenance cost of timber bridges is affected significantly by a number of deterioration mechanisms which require a systematic approach for diagnosis and treatment. Evaluating the risk of failure of these bridges is of importance in bridge performance assessment and decision making to optimize rehabilitation options. The paper aims to discuss these issues.

This paper identifies common causes for timber bridge deterioration and demonstrates an integrated approach based on fault tree analysis to obtain qualitative or quantitative estimation of the risk of failure of timber bridge sub-systems. Level 2 inspection report for a timber bridge in Queensland, Australia has been utilized as a case study in this research to identify the failure modes of the bridge.

A diagnostic tool for timber bridge deterioration will benefit asset inspectors, managers, and engineers to identify the type, size and the distress mechanisms in order to recognize the proper corrective measures either to prevent or to reduce further deterioration. Timber bridge maintenance is a major issue in Queensland, Australia. If a decision support tool can be developed, it will benefit road authorities and local councils.

Timber bridge maintenance is a major issue in Queensland, Australia. If a decision support tool can be developed as initiated in this research paper it will benefit road authorities and local councils.

Successful bridge management system (BMS) development requires a reliable bridge deterioration model, which is the most crucial component in a BMS. Historical condition ratings obtained from biennial bridge inspections are a major source for predicting future bridge deterioration in BMSs. However, historical condition ratings are very limited in most bridge agencies, thus posing a major barrier for predicting reliable future bridge performance. The purpose of this paper is to present a preliminary study as part of a long‐term research on the development of a reliable bridge deterioration model using advanced Artificial Intelligence (AI) techniques.

This proposed study aims to develop a reliable deterioration model. The development work consists of two major Stages: stage 1 – generating unavailable bridge element condition rating records using the Backward Prediction Model (BPM). This helps to provide sufficient historical deterioration patterns for each element; and stage 2 – predicting long‐term condition ratings based on the outcome of Stage 1 using time delay neural networks (TDNNs).

Long‐term prediction using proposed method can also be expressed in the same form of inspection records – element quantities of each bridge element can be predicted. The proposed AI‐based deterioration model does not ignore critical failure risks in small number of bridge elements in low condition states (CSs). This implies that the risk in long‐term predictions can be reduced.

The proposed methodology aims to utilise limited bridge inspection records over a short period to predict large datasets spanning over a much longer time period for a reliable, accurate and efficient long‐term bridge deterioration model. Typical uncertainty, due to the limitation of overall condition rating (OCR) method, can be minimised in long‐term predictions using limited inspection records.