Y. Shang
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1
Global maritime transportation facilitates more than 80% of world trade; therefore, the reliability of ships is inextricably linked to global supply chains. Inefficient inspection and maintenance practices can trigger failures, which in turn increase operating costs. These failures can account for up to 35% of total expenses and cause downtime that cascades through logistic networks. Consequently, unplanned equipment and machinery failures that occur on a vessel can disrupt the ship’s overall operation and lead to delays, higher transport costs, and wider societal impacts, most notably supply shortages and elevated environmental risks. To avoid these domino effects, ship operators must understand the most common failure mechanisms and their underlying causes. Thus, systematic risk identification is a starting point for any strategy that aims to mitigate operational downtime. Existing literature on risk identification in ship inspection and maintenance operations largely focuses on technical and engineering solutions. In order to address this gap, this research adopts the socio-technical system (STS) approach to identify improvement opportunities for inspection and maintenance activities, ultimately mitigating operational downtime. A two-stage Failure Mode Effects and Criticality Analysis (FMECA) is conducted to determine the fundamental failure mechanism in oil tankers and the most critical system, based on industry inspection reports. The Functional Resonance Analysis Method (FRAM) is used to deep dive into that failure. Semi-structured expert interviews and operational data serve as a means of identifying performance variability scenarios across human, organizational, and environmental contexts, within the ship operational process. Finally, the integration of the FMECA and FRAM assists in evaluating suggested control measures based on their effect on ship availability. Corrosion in the steam supply subsystem arises as the leading operational downtime driver. Personnel competence, equipment availability, inspection areas accessibility, and time constraints are the key factors that create performance variability in the operational process. Hinging upon the FRAM models, which qualitatively visualize the propagation of these variability scenarios, control measures are developed based on a Hierarchy of Controls (HoC) As Low As Reasonably Practicable (ALARP) framework. Next steps should begin with a pilot on a single ship subsystem that is highly critical, and a high-resolution failure and downtime dataset. Virtual tests are advised to be conducted before the actual deployment of the identified control measures.
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Global maritime transportation facilitates more than 80% of world trade; therefore, the reliability of ships is inextricably linked to global supply chains. Inefficient inspection and maintenance practices can trigger failures, which in turn increase operating costs. These failures can account for up to 35% of total expenses and cause downtime that cascades through logistic networks. Consequently, unplanned equipment and machinery failures that occur on a vessel can disrupt the ship’s overall operation and lead to delays, higher transport costs, and wider societal impacts, most notably supply shortages and elevated environmental risks. To avoid these domino effects, ship operators must understand the most common failure mechanisms and their underlying causes. Thus, systematic risk identification is a starting point for any strategy that aims to mitigate operational downtime. Existing literature on risk identification in ship inspection and maintenance operations largely focuses on technical and engineering solutions. In order to address this gap, this research adopts the socio-technical system (STS) approach to identify improvement opportunities for inspection and maintenance activities, ultimately mitigating operational downtime. A two-stage Failure Mode Effects and Criticality Analysis (FMECA) is conducted to determine the fundamental failure mechanism in oil tankers and the most critical system, based on industry inspection reports. The Functional Resonance Analysis Method (FRAM) is used to deep dive into that failure. Semi-structured expert interviews and operational data serve as a means of identifying performance variability scenarios across human, organizational, and environmental contexts, within the ship operational process. Finally, the integration of the FMECA and FRAM assists in evaluating suggested control measures based on their effect on ship availability. Corrosion in the steam supply subsystem arises as the leading operational downtime driver. Personnel competence, equipment availability, inspection areas accessibility, and time constraints are the key factors that create performance variability in the operational process. Hinging upon the FRAM models, which qualitatively visualize the propagation of these variability scenarios, control measures are developed based on a Hierarchy of Controls (HoC) As Low As Reasonably Practicable (ALARP) framework. Next steps should begin with a pilot on a single ship subsystem that is highly critical, and a high-resolution failure and downtime dataset. Virtual tests are advised to be conducted before the actual deployment of the identified control measures.
Construction schedule optimisation
Optimisation of BIM-based, component-level construction schedule for building structural and MEP systems considering parallel working zones
Construction schedule optimisation problems have been explored extensively, including activity sequencing rules and work packaging. Yet knowledge is still lacking in the sequencing of mechanical, electrical and plumbing (MEP) components with geometric complexity, and how to handle conflicting precedence between MEP work packages arising from the geometric complexity. Another concept of interest is working zones, which are spaces a building may be divided into to enable parallel working: they have the potential to reduce idle working space and project duration, but its integration with the scheduling of MEP systems and effect on schedule optimisation are under-investigated. This work studies the optimisation of construction schedules for building structural and MEP systems considering working zones. First, a conceptual framework is developed, on: (1) activity sequencing rules, featuring preferences on spatial proximity and component size for MEP components; (2) clustering and cluster-splitting method, to resolve the conflicts among MEP packages; and (3) mathematical formulation of schedule optimisation problems as mixed-integer linear programming (MILP) problems. Next, a software tool consisting of an Excel add-in, a MATLAB executable programme and an Excel macro is developed to implement the framework. Two case studies are carried out. The results of case studies and further analysis demonstrate the large potential of zones in reducing project duration, the effect of the amount of resource available, and strategies for future scheduling practices. Applicability to general construction projects, limitations and future directions are also discussed.
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Construction schedule optimisation problems have been explored extensively, including activity sequencing rules and work packaging. Yet knowledge is still lacking in the sequencing of mechanical, electrical and plumbing (MEP) components with geometric complexity, and how to handle conflicting precedence between MEP work packages arising from the geometric complexity. Another concept of interest is working zones, which are spaces a building may be divided into to enable parallel working: they have the potential to reduce idle working space and project duration, but its integration with the scheduling of MEP systems and effect on schedule optimisation are under-investigated. This work studies the optimisation of construction schedules for building structural and MEP systems considering working zones. First, a conceptual framework is developed, on: (1) activity sequencing rules, featuring preferences on spatial proximity and component size for MEP components; (2) clustering and cluster-splitting method, to resolve the conflicts among MEP packages; and (3) mathematical formulation of schedule optimisation problems as mixed-integer linear programming (MILP) problems. Next, a software tool consisting of an Excel add-in, a MATLAB executable programme and an Excel macro is developed to implement the framework. Two case studies are carried out. The results of case studies and further analysis demonstrate the large potential of zones in reducing project duration, the effect of the amount of resource available, and strategies for future scheduling practices. Applicability to general construction projects, limitations and future directions are also discussed.