Non-parametric estimates of the first hitting time of Li-ion battery

Hasilová, Kamila; Vališ, David

doi:10.1016/j.measurement.2017.08.030

Cited by 8 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been implemented into several models of military tactics, such as optimal cooperative reconnaissance by aerial and/or ground unmanned vehicles, and optimal logistics distribution on the battlefield. More details about the information support of commanders can be found in [33][34][35][36][37][38][39].…”

Section: Practical Applicationmentioning

confidence: 99%

Using Metaheuristics on the Multi-Depot Vehicle Routing Problem with Modified Optimization Criterion

Stodola

2018

Algorithms

View full text Add to dashboard Cite

This article deals with the modified Multi-Depot Vehicle Routing Problem (MDVRP). The modification consists of altering the optimization criterion. The optimization criterion of the standard MDVRP is to minimize the total sum of routes of all vehicles, whereas the criterion of modified MDVRP (M-MDVRP) is to minimize the longest route of all vehicles, i.e., the time to conduct the routing operation is as short as possible. For this problem, a metaheuristic algorithm-based on the Ant Colony Optimization (ACO) theory and developed by the author for solving the classic MDVRP instances-has been modified and adapted for M-MDVRP. In this article, an additional deterministic optimization process which further enhances the original ACO algorithm has been proposed. For evaluation of results, Cordeau's benchmark instances are used.Algorithms 2018, 11, 74 2 of 14 was introduced by Montoya-Torres et al. [6], who consider papers published between 1988 and 2014. Several variants of MDVRP are studied in the article: time windows, split delivery, heterogeneous fleet, periodic deliveries, and pickup and delivery. Another survey of publications on VRP and its variants can be found in [7], where 277 articles published between 2009 and 2015 are classified.Popular metaheuristic algorithms for MDVRP are based on the tabu search approach. One of the first tabu search-based algorithms was published by Gillet and Johnson in 1976 [8]. The same approach is used by Chao, Golden and Vasil [9], or Renaud, Laporte and Boctor [10], and indeed, examples of new algorithms using the tabu search approach continue to be published [11,12].Other popular metaheuristic approaches for MDVRP are simulated annealing [13,14] and genetic algorithms [15]. A hybrid algorithm, based on both simulated annealing and the genetic approach, was proposed by Seidgar et al. [16]. An excellent survey of papers using genetic algorithms for MDVRP can be found in [17].More recently, bio-inspired algorithms have also been broadly used for MDVRP. Ant colony optimization algorithms are inspired by the behavior of ants when searching for food. There are many publications using this for MDVRP. Yu, Yang and Xie [18] proposed an algorithm which transfers the problem to VRP by inserting a virtual central depot (V-MDVRP). Another solution was proposed by Yao et al. [19], who applied the ACO algorithm to a seafood product delivery routing problem. The most recent works include algorithms by Yalian [20], Gao [21], and Ma et al. [22].In the last few years, hybrid metaheuristic methods have emerged. They combine features from various methodologies to take advantage of their different strengths. Ting and Chen [23] introduced a combination of the ant colony algorithm, and simulated annealing for MDVRP with time windows. Vidal et al. [24] proposed a method which combines the exploration breadth of population-based evolutionary searches, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and advanced population-diversity management schemes. Oliveira et al. [25] de...

show abstract

Section: Practical Applicationmentioning

confidence: 99%

Using Metaheuristics on the Multi-Depot Vehicle Routing Problem with Modified Optimization Criterion

Stodola

2018

Algorithms

View full text Add to dashboard Cite

show abstract

“…Another issue, widely discussed when comparing successive generations of aircraft, is the reduction of unit costs and environmental impact MWL (2021); Grabowski et al (2021); Hasilova and Valis (2018).…”

Section: Impact Of the Introduction Of New Designs On Flight Safetymentioning

confidence: 99%

Comparison of Flight Safety Between Different Aircraft Modifications

Woch,

Tomaszewska,

Zieja

et al. 2023

Proceeding of the 33rd European Safety and Reliability Conference

View full text Add to dashboard Cite

This paper discusses the relationship between the next aircraft modifications on the number and consequences of accidents or incidents. The problem is discussed on the example of selected models of the Boeing brand. The first part of the paper discusses the impact of introducing new designs on flight safety. The differences between modifications of selected Boeing aircraft are briefly presented. Definitions of an accident and an aircraft incident are quoted and the sources of data used for the analysis are presented. In the next part of the study, the number of aircraft of a given type in service at a given time was estimated based on delivery reports. The numbers of aircraft in service were compared with aircraft accident and incident reports. The number of adverse occurrences per aircraft of a given type in the sequential years following the introduction of a new model was presented in tables and graphs. In the following part of the study, the probability of occurrence of different loss categories in the first two years of operation after the launch of the new aircraft models was calculated. Finally, the results obtained are summarised and discussed as well as interpreted.

show abstract

“…Such degradation in critical engineering systems (e.g., wind turbines, drilling equipments, power/smart grids, and mechanical devices) takes the form of aging, corrosion, erosion, fatigue crack, deterioration or wear that may lead to the loss of structural integrity and catastrophic failure when it hits a boundary. With advanced measurement tools such as sensors, degradation data can be measured and collected effectively and economically, e.g., the Li-ion battery capacity data [8,23,44], the integrated circuit propagation delay data [9], the metal fatigue-crack-growth data [26], and the transistor gain data [42]. The degradation data series over the life cycle reflect the evolution of the system's health state that contain more information than the sparse failure time data for reliable systems.…”

Section: Introductionmentioning

confidence: 99%