Functional requirements and system architecture for decision support of energy efficient building design in retrofit and maintenance stage

This paper describes development of a methodology to support better retrofit and maintenance with optimised energy consumption using evolving technologies in material, components and systems both at building and neighbourhood levels. It is based on a retrofit and maintenance scenario focused on specification of the functional requirements, databases requirement and system architecture for the construction and operation of the decision support tool. Decision support (DS) tools have already been developed for architects and building designers to choose best building design options with retrofit and maintenance in mind. However, there is a lack of understanding of the required data structures, databases, definition of the functional requirements and the variety of the possible system architectures for this application. The proposed DS tool will support Facility Management (FM) to design their option on Building Information Model (BIM) file by making best retrofit and maintenance decisions for improved energy efficiency (EE) without needing full knowledge of the latest technologies in any required subject and without being expert in building energy performance analysis and simulation. A detailed retrofit and maintenance scenario and its corresponding process map are developed and explained in details. Database requirements are extracted and discussed, leading to specification of the necessary structure and content with a level of details. The functional requirements for retrofit and maintenance design scenario are discussed and an exhaustive list is generated. The decision support tool was structured using four building blocks: (i) energy performance and simulation block; (ii) retrofit and maintenance options generator; (iii) optimisation block and; (iv) a decision making block based on Multiple Criteria Decision Making (MCDM) method

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Integrating IVHM and Asset Design

Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collection of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process

Maintenance strategy optimisation for infrastructure assets through cost modelling

In infrastructure asset management, maintenance strategies in terms of cost modelling is normally adopted to achieve two broad strategic objectives: to ensure that sufficient funding is available to maintain the portfolio of assets; and to ensure that a minimum cost is achieved while maintaining safety. The data and information required for carrying out cost modelling are often not sufficient in quantity and quality. Even if the data is available, the uncertainty associated with the data and the assessment of the assets’ condition remain a challenge to be dealt with. We report in this paper that cost modelling can be carried out at the initial stage instead of delaying it due to data insufficiency. Subjective experts’ knowledge is elicited and utilised together with some information which is gathered only for a small sample of assets. Linear Bayes methods is adopted to combine the sample data with the subjective experts’ knowledge to estimate unknown model parameters of the cost model. We use a case study from the rail industry to demonstrate the methods proposed in this paper. The assets are metal girders on bridges from a rail company. The optimal maintenance strategy is obtained via simulation based on estimated model parameters

Integrated Production-Distribution Planning with Considering Preventive Maintenance

The preventive maintenance activity is important thing in production system especially for a continuous production process, for example in fertilizer industry. Therefore, it has to be considered in production-distribution planning. This paper considers the interval of production facility’s preventive maintenance in production-distribution planning of multi echelon supply chain system which consists of a manufacturer with a continuous production process, a distribution center, a number of distributors and a number of retailers. The problem address in this paper is how to determine coordinated productiondistribution policies that considers the interval of production facility’s preventive maintenance, and customer demand only occurred at retailers and it fluctuates by time. Based on model of Santoso, et al. (2007), using the periodic review inventory model and a coordinated production and replenishment policies that are decided by central planning office and it must be obeyed by all entities of multi-echelon supply chain, the integrated production-distribution planning model is developed to determine the production and replenishment policies of all echelon in the supply chain system in order to minimize total system cost during planning horizon. Total system cost consists of set-up/ordering cost, maintenance cost, holding cost, outsourcing cost and transportation cost at all of entities. With considering preventive maintenance and there is one production run over the planning horizon, the replenishment cycle at distribution center, distributors and retailers that are found out are greater than the basic model. Also, the multiplication of replenishment cycle at distribution center in production cycle that is found out is greater than the basic model but the multiplication of replenishment cycle at retailers in its distributor are smaller than the basic model

Construction safety and digital design: a review

As digital technologies become widely used in designing buildings and infrastructure, questions arise about their impacts on construction safety. This review explores relationships between construction safety and digital design practices with the aim of fostering and directing further research. It surveys state-of-the-art research on databases, virtual reality, geographic information systems, 4D CAD, building information modeling and sensing technologies, finding various digital tools for addressing safety issues in the construction phase, but few tools to support design for construction safety. It also considers a literature on safety critical, digital and design practices that raises a general concern about ‘mindlessness’ in the use of technologies, and has implications for the emerging research agenda around construction safety and digital design. Bringing these strands of literature together suggests new kinds of interventions, such as the development of tools and processes for using digital models to promote mindfulness through multi-party collaboration on safet