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The purpose of this paper is to model an inspection maintenance strategy that is superior to existing strategies and which incorporates the inspection factor.

An appropriate inspection maintenance strategy can help prevent breakdown failure and reduce breakdown and repair costs. However, every inspection also incurs costs. The optimal model for an inspection maintenance strategy would be such that the inspection frequency closely follows the hazard rate of the equipment over its lifetime.

A model with a three‐layered structure needs to be developed. Over time, equipment can be expected to demonstrate a hazard rate that is decreasing, constant or increasing with time. In the first stage a model for inspection frequency is developed that is time‐dependent and can address this requirement. Second, the hazard rate has a dynamic relationship with the inspection frequency, i.e. as the inspection frequency increases, the hazard rate is known to decrease. In the second stage this is incorporated into the model, using the inspection factor. In the last stage a model for costing is superimposed for cost optimization.

The model developed in this paper provides maintenance managers with a powerful practical tool for determining the optimal inspection frequency for equipment over its lifetime.

The purpose of this paper is to review the evolution of inspection and maintenance (I&M) practices used for aging and newly built oil and gas (O&G) facilities. It also proposes a framework and an approach for mechanizing inspection planning to perform preventive maintenance (PM) activities, taking technical condition (TC) and relative degradation (RD) into consideration.

The paper systematically collects, categorizes, and analyzes the published literature of both researchers and practitioners. It also utilizes industrial experience that has been accrued and utilized from inspection planning practices for static mechanical equipment on aging O&G production plants.

The paper defines significant issues in I&M of O&G assets related to: different philosophies; stakeholders’ requirements trade-off; dependability and asset deterioration challenges; items interacting with inspection planning mechanization processes and I&M optimization approaches. A framework is identified to mechanize the inspection planning process in order to reduce the effect arising from human involvement, while improving the effective utilization of data from different sources. The suggested approach improves the quality of an inspection program, while minimizing the variability and cost to the engineering contractors as well as to the owners of O&G facilities.

The mechanization of inspection planning (MIP) is vital to have inspection programs with uniform quality. The currently employed inspection practices face challenges in maintaining uniform quality from one inspection program to another due to the variability present in the planning process, especially among the different inspection planning engineers. The suggested fuzzy logic-based MIP supports the minimization of the variability and increases the quality of inspection programs.

The paper provides a comprehensive review of research contributions and industrial development efforts. These will be useful to the life cycle stakeholders in both academia and industry in understanding the inspection planning problem and solution space within the O&G asset I&M context.

Reverse logistics (RL) is a waste revaluation process aimed at reintroducing once-used materials back into the production cycle. Public administration (PA) plays a strong role in influencing the implementation of RL in the construction industry through policy and laws. While much research identifies PA as an important driver in RL, no research yet describes how this occurs.

A systematic literature review was conducted on the theme PA in RL with 93 relevant publications retrieved.

Six strategies used by PA to promote RL were extracted: (1) legislation, (2) government subsidies, (3) landfill disposal fee, (4) government inspection, (5) use in public construction works and (6) penalties and fines. Moreover, a typology built on these six strategies was developed, mapping 15 interactional relationships between strategies, according to three levels of influence: (1) encouragement (2) requirement and (3) regulation.

It was found that legislation and government subsidies prove to be the more effective strategies, with the remaining strategies greatly neglected. Importantly, however, this study reveals that a combination of strategies are most effective when appropriately combined.

This study confirms the importance of PA in RL, in construction, while documenting the extent of current research. The resulting proposed typology, along with 15 identified future research priorities, is expected to be of value to academics and policy makers looking to advance understanding in this domain.

The purpose of this paper is to develop a decision support tool for risk-based maintenance scheduling for a large heavily equipped gas sweetening unit in a Liquefied Natural Gas (LNG) plant. Two conflicting objectives, i.e., total maintenance cost and the reliability, are considered in the tool. The tool is tested with the real plant data and suggests several Pareto-optimal schedules for a decision maker to choose from. The financial impacts are assessed.

A bi-objective scheduling optimization model is developed for maintenance scheduling using a risk-based framework. The model is developed integrating genetic algorithm and simulation-based optimization to find Pareto-optimal schedules. The model delivered true Pareto front optimal solutions for given plant-specific data. The two conflicting objectives: the minimization of total expenditures incurred on maintenance-related activities and improving the total reliability are considered.

For large and complex processing facilities such as LNG plant, a shutdown of facility generates a significant financial impact, resulting in millions of dollars in production loss. The developed risk-based equipment selection strategy helps to minimize such an event of production loss by generating a thorough maintenance strategy for inspection, repair, overhaul or replacement schedule of the unit without initiating the shutdown. The proposed model has been successfully applied to obtain an optimize maintenance schedule for a gas sweetening unit.

A future work may consider the state-dependent models for various failure modes that will result in obtaining a better representation of the model. The proposed scheduling can further be extended to multi-criteria scheduling including availability, resource limitation and inflationary condition. A comparative analysis with other meta-heuristic techniques such as harmony search algorithm, tabu search, and simulated annealing will further help in confirming the schedule obtained from this application.

Maintenance scheduling using a conventional approach for special equipment generally does not consider the conflicting objectives. This research addresses this aspect using a bi-objective model. The usefulness of risk-based method is to assist in minimizing the financial and safety risk exposure to the operating companies, but some variation in results is expected due to varying risk matrix for different organizations.

Managing two objectives, i.e., minimizing the cost of maintenance-related activities, while at the same time maximizing the overall reliability dramatically, helps in mitigating adverse safety and financial risk due to fires, explosions, fatality and excessive maintenance cost.

Research develops a decision support tool for managing conflicting objectives for an LNG process. This research highlights the impact of utilizing the simulation-based approach coupled with risk-based equipment selection for complex processing unit or plant maintenance scheduling optimization.

Purpose – The purpose of this paper is to propose a risk‐based approach for spare parts demand forecast and spare parts inventory management for effective allocation of limited resources. Design/methodology/approach – To meet the availability target and to reduce downtime, process facilities usually maintain inventory of spare parts. The maintaining of non‐optimized spare parts inventory claims more idle investment. Even if it is optimized, lack of attention towards the critical equipment spares could threaten the availability of the plant. This paper deals with the various facets of spare parts inventory management, mainly risk‐based spare parts criticality ranking, forecasting, and effective risk reduction through strategic procurement policy to ensure spare parts availability. A risk‐based approach is presented that helps managing spare parts requirement effectively considering the criticality of the components. It also helps ensuring the adequacy of spare parts inventory on the basis of equipment criticality and dormant failure without compromising the overall availability of the plant. Findings – The paper proposes a risk‐based approach that used conjugate distribution technique with the capability to incorporate historical failure rate as well as expert judgment to estimate the future spare demand through posterior demand distribution. The approach continuously updates the prior distribution with most recent observation to give posterior demand distribution. Hence the approach is unique in its kind. Practical implications – Appropriate spare parts unavailability could have great impact on process operation and result in costly downtime of the plant. Following proposed approach the availability target can be achieved in process industry having limited maintenance resources, by forecasting spare parts demand precisely and maintaining inventory in good condition. Originality/value – Adopting the approach proposed in the paper, risk level can be minimized and plant availability can be maximized within the financial constraint. The resources are allocated to the most critical components and thereby increased availability, and reduce risk.

In most automated assembly systems, each product is inspected after every assembly operation and, if it is found to be defective, it will be repaired or scrapped immediately. This ensures the quality of the outputs of the systems, but it also causes a short stop of the assembly line. This paper shows that the number of short stops can be reduced by installing inspection stations and repair stations strategically. Then, an optimizing problem of placing these stations under a given assembly sequence is formulated and an algorithm is given to this problem. Further, the problem of not only placing these stations but also determining the assembly sequence is formulated and an algorithm to solve the problem is developed. Finally, numerical examples are given to illustrate the efficiencies of the algorithms.

Drawing on the international business and game theory literature, this study assesses foreign firm treatment in the early stages of regulatory enforcement.

Treating regulation intensity as an exposure variable, negative binomial regression models were applied to firm-level data from 32 emerging markets (n = 15,331) to identify the determinants of inspection interactions. Robustness checks also were performed via variable substitutions for several predictors and an alternative form of statistical testing (i.e. Tobit regression, since it arguably better addresses dependent variables with corner solution responses).

Controlling for multiple organizational, regulatory and national characteristics, the findings are consistent with a foreign privilege, manifesting in reduced vulnerability to multiple encounters with labor inspection officials. Moreover, inward FDI stock was negatively related to the general probability of repeat interactions regardless of locus of ownership, an effect that was not moderated by stage of development or the regulatory influence of host interest groups. This collectively suggests that foreign firms not only are favored in compliance monitoring but also work post-entry to influence agencies to generally benefit business.

More comprehensive assessments were precluded given the lack of information on reasons for contact, citations and fines, and inspectorate reactions to company responses. Second, enforcement-risk management was measured indirectly since investors' internal dealings and actions toward officials are unavailable in secondary sources.

These findings have important implications for social responsibility, suggesting CSR stakeholders need to track enforcement more closely and exert pressure where needed so rights are not sacrificed for economic development.

This study provides the most rigorous assessment to date of the role that firm, government and economic factors play in national inspection targeting. It also examined whether foreign owners pool and leverage their political influence to impact general inspection activity, a previously untested prospect.

In certain environments, the system may not fail completely, but undergoes degradation, and the system productivity might decrease. Meanwhile, at the same time, the system may be vulnerable to shocks. A single-unit system prone to degradation and shocks is proposed in this study, and emphasis is placed upon determining its availability and cost rate.

The considered single-unit system is expected to have three states, namely, normal, degraded and failed. As the system enters the degraded state, it is said to be partially failed. The degraded state incurs higher degradation than the normal state and is more prone to shocks. Inspections are used to determine the state and failure type of the system. Inspections are predetermined to be carried out sequentially at time I, I+aI, I+aI+a2I,… where 0 < a ≤ 1, until the detection of degradation/failure. Perfect repairs are conducted instantly on spotting the partial/complete failure. Two cases have been considered of repair taking constant times and random times.

Explicit results on the reliability, availability (both point and limiting availability) and long-run average cost rate (LRACR) of a sequentially inspected single-unit system prone to degradation and shocks under constant and random repair times are given. Numerical example of an oil pipeline system is taken to clarify the acquired results.

A sequentially inspected single-unit system prone to degradation and shock is studied unlike done previously.

Visual inspection and human judgement form the cornerstone of daily operations and maintenance (O&M) services activities carried out by facility managers nowadays. Recent advances in technologies such as building information modelling (BIM), distributed sensor networks, augmented reality (AR) technologies and digital twins present an immense opportunity to radically improve the way daily O&M is conducted. This paper aims to describe the development of an AR-supported automated environmental anomaly detection and fault isolation method to assist facility managers in addressing problems that affect building occupants’ thermal comfort.

The developed system focusses on the detection of environmental anomalies related to the thermal comfort of occupants within a building. The performance of three anomaly detection algorithms in terms of their ability to detect indoor temperature anomalies is compared. Based on the fault tree analysis (FTA), a decision-making tree is developed to assist facility management (FM) professionals in identifying corresponding failed assets according to the detected anomalous symptoms. The AR system facilitates easy maintenance by highlighting the failed assets hidden behind walls/ceilings on site to the maintenance personnel. The system can thus provide enhanced support to facility managers in their daily O&M activities such as inspection, recording, communication and verification.

Taking the indoor temperature inspection as an example, the case study demonstrates that the O&M management process can be improved using the proposed AR-enhanced inspection system. Comparative analysis of different anomaly detection algorithms reveals that the binary segmentation-based change point detection is effective and efficient in identifying temperature anomalies. The decision-making tree supported by FTA helps formalise the linkage between temperature issues and the corresponding failed assets. Finally, the AR-based model enhanced the maintenance process by visualising and highlighting the hidden failed assets to the maintenance personnel on site.

The originality lies in bringing together the advances in augmented reality, digital twins and data-driven decision-making to support the daily O&M management activities. In particular, the paper presents a novel binary segmentation-based change point detection for identifying temperature anomalous symptoms, a decision-making tree for matching the symptoms to the failed assets, and an AR system for visualising those assets with related information.

The purpose of this article is to examine the regime to remediate contaminated land in the UK set out in Part 2A of the Environmental Protection Act 1990 and to analyse the UK Government's intent and objectives in introducing the regime. The legislative provisions and the statutory guidance that accompanies that legislation are then analysed to determine whether those objectives could have been met.

A research approach was taken to trace the legislative history of Part 2A and to analyse the statutory provisions and the statutory guidance. The approach included researching Parliamentary debates on the statute, consultations on the statutory guidance, other information published by the UK Government, commentaries on the regime, and contaminated land regimes in other jurisdictions.

The paper found that the introduction of a contaminated land regime that delegates primary implementation and enforcement authority to local authorities, and that severely limits their discretion in doing so, has resulted in a regime that has proven to be unworkable in practice and that has failed to meet its objectives.

The article is the first paper to examine the legislative intent and objectives behind Part 2A and to analyse their effect on the provisions in the statute and the statutory guidance and their implementation and enforcement.