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RCAM Case Wind Power Systems
Published in Lina Bertling Tjernberg, Infrastructure Asset Management with Power System Applications, 2018
The investment cost is placed in year zero. Connection cost is included in the investment cost. PM and CM for 2 years are included in the investment cost. There is also a cost for basic maintenance materials in both years. These costs are included in CPM and CCM for years 1 and 2. CPM and CCM from year 3 are estimated for each year. A cost for production loss has been calculated as income times unavailability from year 1 and is called CPL. Commercial availability, that is, 97.5%, has been used in this calculation. Unavailability is calculated as 1-availability. A cost for demolition and recycling CRem has been estimated for the last year. Costs for major component replacements have been calculated. The gearbox is changed twice during a lifetime. The generator is changed once during a lifetime. The transformer and blades are changed 1/10th time during a lifetime. These costs are combined under cost of replacements CR, according to CR=CR,GB+CR,G+CR,T+CR,B where CR,GB, cost of replacing gearboxes for a year; CR,G, cost of replacing generators for a year; CR,T, cost of replacing transformers for a year; CR,B, cost of replacing blades for a year.
Minimising makespan in job-shops with deterministic machine availability constraints
Published in International Journal of Production Research, 2021
The unavailability constraints resulting from preventive maintenance can be divided into two main cases: deterministic and flexible. In deterministic cases, the maintenance periods are fixed in advance; in flexible cases, the maintenance periods are decision variables scheduled in given time windows. Azem et al. (2007) used disjunctive and time-indexed formulations to present two mixed-integer linear programming (MILP) models for the problem. Their experimental results showed that the disjunctive formulation outperformed the time-indexed one. Subsequently, Zribi et al. (2008) proposed a two-phase algorithm to minimise the makespan for the JSSP with limited machine availability. In the first phase, a priority rule-based heuristic was used to solve the assignment problem. Then, in the second phase, a genetic algorithm (GA) was applied to solve the sequencing problem; computational results showed that the two-phase algorithm gave interesting results compared to existing algorithms, as well as the theoretical lower bound. Aggoune et al. (2009) extended the geometric algorithm to present a polynomial solvable approach for solving the two-job JSSP with an arbitrary number of unavailability periods on each machine; test results showed that this algorithm outperformed existing ones in terms of complexity as well as its representation of real instances.
Analysis and Evaluation of Cyber-attack Impact on Critical Power System Infrastructure
Published in Smart Science, 2021
Neeraj Kumar Singh, Vasundhara Mahajan
Use a by of SUCIF score deals with the loss in terms of unavailability, which decides the impact intensity. For calculation of score following parameters are required: Duration of cyber-attack: define attack period.Unavailability of system: define as the total time the system is unavailable during and after a cyber-attack.
Dynamic cloud manufacturing service composition with re-entrant services: an online policy perspective
Published in International Journal of Production Research, 2023
Currently, most existing studies investigate the CMfg SC problem under the static cloud environment (Tao et al. 2013), where all the information (e.g. tasks, services, consumers and transactions) are assumed available, accessible and deterministic a priori. These ideal assumptions are far from engineering practice as the dynamic nature of CMfg SC is ignored. Specifically, dynamics in CMfg SC have several origins Task side. Similar to real-world practice in transportation (Zhang et al. 2021), ride-sharing (Dickerson et al. 2018; Sumita et al. 2022), spatial crowdsourcing (Tong et al. 2021) and more. The arrival of tasks often follows an online pattern (Dickerson et al. 2018; Sumita et al. 2022). In other words, rather than having complete prior knowledge of task arrival, it is more practical to have some knowledge about the distribution of task arrival (e.g. Poisson distribution Bumpensanti and Wang 2020) which can be observed from history. Moreover, the type of incoming task is random, not deterministic. Sometimes tasks are drawn from an underlying distribution (e.g. Multinomial distribution (Vera and Banerjee 2020) if assuming the task types are finite).Service side. Non-function-related parameters of services are dynamic (Hu et al. 2022), e.g. service price, service reputation and service duration vary with time. Also, sometimes a single service will become unavailable for a while, then return to the CMfg platform (i.e. re-entrance). Reasons for unavailability include being occupied by other tasks, machine breakdown, regular maintenance, service upgrade (software and hardware) and more. Moreover, other dynamics such as stochastic service cost, concave return assumptions (Devanur and Jain 2012)), and stochastic or even unknown (Rusmevichientong, Sumida, and Topaloglu 2020) service time of tasks make the revenue management of CMfg platform more complicated.