A review of asset management literature on multi-asset systems

This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options. Moreover, discerning differences in characteristics between cross-component and cross-asset interactions, we select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, we delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. We expect that the output of this review paper will not only stress research gaps on multi-asset systems, but more importantly help systematise future studies on this aspect

Heuristic optimisation for multi-asset intervention planning in a petrochemical plant

Large infrastructure assets commonly require high intervention costs, but the absence of an effective asset management plan can bring about a massive production loss for a company. Hence, managing these assets is considered a daunting task and is even more complicated if these assets operate collectively to produce an output. This paper explores a pragmatic approach to a multi-asset intervention scheduling problem through a case study of a vessel fleet in a petrochemical plant. After the relationship between the asset configuration and the system output is defined, an optimisation model with an objective to jointly minimise cost and risk is developed. Since the calculation of risk profiles across the fleet requires complex non-linear functions, a genetic algorithm is employed to search for an optimal combination of intervention schedules. Compared to the current run-to-failure strategy, the optimal strategy results in a significant reduction in system failure risk and a substantial improvement in long-term fleet conditions while reducing the total cost

A value-based approach to optimizing long-term maintenance plans for a multi-asset k-out-of-N system

Devising a long-term maintenance plan for a system of large infrastructure assets is an exacting task. Any maintenance activity that induces system downtime can incur a massive production or service loss. This problem becomes increasingly challenging for a system of which the performance is based on the collective output of assets. Current approaches that optimise each asset in isolation or consider a binary performance relationship insufficiently address this issue because the negligence of performance interactions among assets results in an inaccurate cost estimation. To overcome these hurdles, we formulate a mathematical model that explicitly demonstrates dynamic risk of production loss according to the system aggregate output. Further, we propose an integrated solution method that couples a finite loop search with a Genetic Algorithm. Application of our model to a real-world case study has proved to simultaneously strike the balance between cost and risk. Validated by Monte Carlo simulation, the proposed model has shown to outperform existing approaches. By systematically scheduling maintenance actions over the planning horizon, the resultant strategy has demonstrated to offer considerable maintenance cost savings and significantly prolong the average asset life. Sensitivity analyses also evince the robustness of the proposed model under the volatility in key parameters.EPSRC (This does not appear on the submitted manuscript yet, but will be added in the final proof

A pragmatic approach to multi-objective optimisation for portfolio asset management

Among the reliability engineering and asset management problems addressed in the literature, those pertaining to multi-unit systems have particularly intrigued researchers because of their profound effects on the organisational performance. Despite impressive strides made in improving maintenance planning for assets of a single type, only a paucity of research has delved into a pragmatic approach to managing an asset portfolio — a system of heterogeneous assets. Specifically, existing portfolio management studies readily assume inter-asset performance independence due to the formulation complexity. Budget allocation across different asset types is one of the most prevalent problem classes in the portfolio management literature. Although assets in a portfolio are associated with different maintenance options and performance metrics, they are all under the control of a single entity and compete for central budget. This problem is inherently multi-objective as it entails multiple performance metrics are not directly comparable. Approaches adopted in existing portfolio management studies either consolidate multiple goals to form a single objective (a priori) or populate the entire Pareto optimal set (a posteriori). Nonetheless, neither of these methods adequately address the problem. While the a priori method relies heavily on subjective judgment of a decision maker (DM) to accommodate incommensurable objectives, the a posteriori method often delivers a Pareto optimal set with too many options, making it counter-productive. This thesis is dedicated to developing a pragmatic approach to a portfolio budget allocation problem with an objective to deliver the DM with a diverse yet compact solution set. To model intricate inter-asset performance dependence within an asset type, polynomial and Gaussian radial basis functions were employed to capture the relationship between intervention investment and collective performance of assets. The use of these approximation functions, coupled with newly proposed solution adjustment and validation operations, was proved to be applicable to any continuous multi-objective evolutionary algorithm (MOEA), thereby facilitating the efficient optimisation across different asset types. To overcome the limitations of the a posteriori method, the K-means and K-medoids methods were applied to pruning Pareto optimal solutions obtained from a selected MOEA. We also presented two novel indicators based on average Euclidean distance and cosine similarity metrics. The application of these indicators not only enabled the DM to objectively measure the relative diversity of the pruned solutions but also offered guidance on choosing the appropriate number of such solutions. The findings were corroborated by three numerical examples including a practical case in a rail network. Through these examples, the proposed approach was demonstrated to produce a pruned solution set that maintains high integrity of the Pareto front

A review of asset management literature on multi-asset systems

This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options. Moreover, discerning differences in characteristics between cross-component and cross-asset interactions, we select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, we delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. We expect that the output of this review paper will not only stress research gaps in multi-asset systems, but more importantly help systematise future studies on this aspect

Heuristic optimisation for multi-asset intervention planning in a petrochemical plant

Large infrastructure assets commonly require high intervention costs, but the absence of an effective asset management plan can bring about a massive production loss for a company. Hence, managing these assets is considered a daunting task and is even more complicated if these assets operate collectively to produce an output. This paper explores a pragmatic approach to a multi-asset intervention scheduling problem through a case study of a vessel fleet in a petrochemical plant. After the relationship between the asset configuration and the system output is defined, an optimisation model with an objective to jointly minimise cost and risk is developed. Since the calculation of risk profiles across the fleet requires complex non-linear functions, a genetic algorithm is employed to search for an optimal combination of intervention schedules. Compared to the current run-to-failure strategy, the optimal strategy results in a significant reduction in system failure risk and a substantial improvement in long-term fleet conditions while reducing the total cost

Pruning Pareto optimal solutions for multi-objective portfolio asset management

Budget allocation problems in portfolio management are inherently multi-objective as they entail different types of assets of which performance metrics are not directly comparable. Existing asset management methods that either consolidate multiple goals to form a single objective (a priori) or populate a Pareto optimal set (a posteriori) may not be sufficient because a decision maker (DM) may not possess comprehensive knowledge of the problem domain. Moreover, current techniques often present a Pareto optimal set with too many options, making it counter-productive. In order to provide the DM with a diverse yet compact solution set, this paper proposes a three-step approach. In the first step, we employ different approximation functions to capture investment-performance relationships at the asset-type level. These simplified relationships are then used as inputs for the multi-objective optimisation model in the second step. In the final step, Pareto optimal solutions generated by a selected evolutionary algorithm are pruned by a clustering method. To measure the spread of representative solutions over the Pareto front, we present two novel indicators based on average Euclidean distance and cosine similarity between original Pareto solutions and representative solutions. Through numerical examples, we demonstrate that this approach can provide a set of representative solutions that maintain high integrity of the original Pareto front. We also put forward suggestions on choosing appropriate approximation functions, pruning methods, and indicators.EPSR

Pruning Pareto optimal solutions for multi-objective portfolio asset management

Budget allocation problems in portfolio management are inherently multi-objective as they entail different types of assets of which performance metrics are not directly comparable. Existing asset management methods that either consolidate multiple goals to form a single objective (a priori) or populate a Pareto optimal set (a posteriori) may not be sufficient because a decision maker (DM) may not possess comprehensive knowledge of the problem domain. Moreover, current techniques often present a Pareto optimal set with too many options, making it counter-productive. In order to provide the DM with a diverse yet compact solution set, this paper proposes a three-step approach. In the first step, we employ different approximation functions to capture investment-performance relationships at the asset-type level. These simplified relationships are then used as inputs for the multi-objective optimisation model in the second step. In the final step, Pareto optimal solutions generated by a selected evolutionary algorithm are pruned by a clustering method. To measure the spread of representative solutions over the Pareto front, we present two novel indicators based on average Euclidean distance and cosine similarity between original Pareto solutions and representative solutions. Through numerical examples, we demonstrate that this approach can provide a set of representative solutions that maintain high integrity of the original Pareto front. We also put forward suggestions on choosing appropriate approximation functions, pruning methods, and indicators

A value-based approach to optimizing long-term maintenance plans for a multi-asset k-out-of-N system

Devising a long-term maintenance plan for a system of large infrastructure assets is an exacting task. Any maintenance activity that induces system downtime can incur a massive production or service loss. This problem becomes increasingly challenging for a system of which the performance is based on the collective output of assets. Current approaches that optimize each asset in isolation or consider a binary performance relationship insufficiently address this issue because the negligence of performance interactions among assets results in an inaccurate cost estimation. To overcome these hurdles, we formulate a mathematical model that explicitly demonstrates dynamic risk of production loss according to the system aggregate output. Further, we propose an integrated solution method that couples a finite loop search with a Genetic Algorithm. Application of our model to a real-world case study has proved to simultaneously strike the balance between cost and risk. Validated by Monte Carlo simulation, the proposed model has shown to outperform existing approaches. By systematically scheduling maintenance actions over the planning horizon, the resultant strategy has demonstrated to offer considerable maintenance cost savings and significantly prolong the average asset life. Sensitivity analyses also evince the robustness of the proposed model under the volatility in key parameters

A review of Pareto pruning methods for multi-objective optimization

Previous researchers have made impressive strides in developing algorithms and solution methodologies to address multi-objective optimization (MOO) problems in industrial engineering and associated fields. One traditional approach is to determine a Pareto optimal set that represents the trade-off between objectives. However, this approach could result in an extremely large set of solutions, making it difficult for the decision maker to identify the most promising solutions from the Pareto front. To deal with this issue, later contributors proposed alternative approaches that can autonomously draw up a shortlist of Pareto optimal solutions so that the results are more comprehensible to the decision maker. These alternative approaches are referred to as the pruning method in this review. The selection of the representative solutions in the pruning method is based on a predefined instruction, and its resolution process is mostly independent of the decision maker. To systematize studies on this aspect, we first provide the definitions of the pruning method and related terms; then, we establish a new classification of MOO methods to distinguish the pruning method from the a priori, a posteriori, and interactive methods. To facilitate readers in identifying a method that suits their interests, we further classify the pruning method by the instruction on how the representative solutions are selected, namely into the preference-based, diversity-based, efficiency-based, and problem specific methods. Ultimately, the comparative analysis of the pruning method and other MOO approaches allows us to provide insights into the current trends in the field and offer recommendations on potential research directions