Explore the vast range of titles available.

This paper introduces a novel two-phase framework for designing a proactive–reactive scheduling model in the multi-mode resource-constrained project scheduling problem under disruptions. The proactive phase involves constructing a resilient baseline scheduling model using a mixed-integer linear programming model. This phase contributes to a multi-objective model that minimizes the project completion time and total project cost while maximizing resilience criteria. In this context, resilience refers to allocating float time to project activities to protect their start and finish times against future disruptions as much as possible. The reactive phase involves a bi-objective mathematical model that mitigates the impact of disruptions through preempt-repeat, preempt-resume, and activity-crashing strategies. Real-world projects involve many uncertain parameters that can negatively affect the optimization of rescheduling problems if overlooked. Therefore, for the first time, a scenario-based robust optimization approach is proposed to cope with the uncertainty of the reactive phase. Additionally, a novel hybrid multi-objective method based on goal programming is introduced to solve the proposed multi-objective model. Finally, to demonstrate the capability of the proposed approach, an oil and gas project in Iran is regarded as a real case study. The results indicate that the negative impact of disruptions on the makespan and total cost of the project can be largely mitigated by considering resilience criteria in the proactive phase and preempt-repeat, preempt-resume, and activity-crashing strategies in the reactive phase.

Low frequency vibration isolator is a common structure vibration control device, widely used in transportation and machinery manufacturing fields. This paper is centered around enhancing the optimization design of rubber isolators operating at low frequencies. In the initial section, the paper introduces the structural framework and operational principles of rubber isolators. Furthermore, it delves into the analysis of their significance and potential applications within engineering contexts. Subsequently, the paper narrows its focus to address the design challenges concerning rubber isolators with low-frequency capabilities. A comprehensive three-dimensional model is constructed using ANSYS software, employing finite element analysis techniques. The paper employs a multi-objective optimization algorithm through a direct optimization module to facilitate the optimization design process. Within this study, the NSGA-II algorithm was employed to drive the optimization of design, with the objective of achieving a 10 Hz frequency for a series of dual rubber isolators, while simultaneously constraining static deformation to within 8 mm. The outcomes of this investigation emphasize the successful attainment of optimization goals by meticulously fine-tuning the structural dimensions of the isolators.

Precipitation interpolation is widely used to generate continuous rainfall surfaces for hydrological simulations. However, increasing the precision of values at the unknown points generated by diefrent spatial interpolation methods is challenging. This study used the Chaohe River Basin, which is an important source of Beijing's drinking water, as a research area to comprehensively evaluate several precipitation interpolation methods (Thiessen polygon, inverse distance weighting, ordinary kriging and ANUSPLIN) for inputs in hydrological simulations. This research showed that the precipitation time-series surface generated using the ANUSPLIN interpolation method had higher accuracy and reliability. Using this precipitation input to drive the hydrological models, we explored the parameter uncertainties of four typical hydrological models (GR4J, IHACRES, Sacramento and MIKE SHE) based on the multi-objective generalized likelihood uncertainty estimation (GLUE) method. The GLUE method was used to study the parameter sensitivity and uncertainty of the model. Results showed that the ANUSPLIN precipitation interpolation surface combined with the Sacramento model performed best. The multi-objective GLUE method had obvious advantages in parameter uncertainty analysis in hydrological models. Simultaneously exploring the convex line and point density distributions of the behavioural parameters with multi-objective functions determined their distribution and sensitivity more efectively.

Summary In recent years, Distributed Generation (DG) has been utilized in electric power networks increasingly. DG units can affect the system operational conditions in different ways such as voltage profile improvement, amending voltage stability, reliability enhancement, securing power market, etc. if they are managed properly. Otherwise, they may have undesirable impacts on technical issues of power grids. A lot of studies have been done on various aspects of control and operation of DG units to find the optimal placement, sizing and also the proper technology of them. This paper proposes a novel comprehensive economic method for planning DG units which considers both the Distribution Company's (DisCo) and the DG Owner's (DGO) profits simultaneously. Multi‐objective particle swarm optimization technique is used to simulate many case studies on the IEEE 33‐bus distribution test system and finally find the best solution for the placement, size and contract price of the generated power of DGs. The proposed methodology not only considers operational aspects such as power loss reduction, voltage profile and stability improvement and reliability enhancement, but also leads to an accurate analysis which satisfies both the DisCo's and the DGO's economic viewpoints. Finally, an encouragement to invest more on DG technologies is proposed based on the gained results. Copyright © 2013 John Wiley & Sons, Ltd.

Developing efficient algorithms for solving multi-objective optimization problems is a challenging and essential task in many applications. This task involves two or more conflicting objectives that need to be simultaneously optimized. Many real-world problems fall into this category. We introduce an improved version of multi-objective differential evolution (DE) algorithm, namely MO n DE that uses a new mutation strategy and a normalization method to select non-dominated solutions. The new mutation strategy “DE/rand-to- n best” uses the best normalized individual in terms of all the objectives to guide the search towards the true pareto optimal solutions. As a result, the probability of producing superior solutions is increased and a faster convergence is achieved. Summation of normalized objective values method is used instead of non-domination sorting to overcome the high computational complexity and overhead problems of sorting non-dominated solutions. The performance of our approach is tested on a set of benchmark problems that consist of two to five objectives. Different combinations of multi-objective evolutionary programming and multi-objective differential evolution algorithms have been used for comparisons. The results affirm the efficiency and robustness of the proposed approach among other well-known algorithms from the literature.

Hippocampal dentation (HD) refers to a series of ridges (dentes) seen on the inferior aspect of the hippocampus. The degree of HD varies dramatically across healthy individuals, and hippocampal pathology may result in loss of HD. Existing studies show associations between HD and memory performance in healthy adults as well as temporal lobe epilepsy (TLE) patients. However, until now studies relied on visual assessment of HD as no objective methods to quantify HD have been described. In this work, we describe a method to objectively quantify HD by transforming the characteristic 3D surface morphology of HD into a simplified 2D plot for which area under the curve (AUC) was calculated. This was applied to T1w scans of 59 TLE subjects, each with one epileptic hippocampus and one normal appearing hippocampus. Results showed that AUC significantly correlated with the number of dentes based on visual inspection (p < .05) and correctly sorted a set of hippocampi from least to most prominently dentated. Intra‐ and inter‐rater reliability was nearly perfect (ICC ≥ 0.99). AUC values were significantly lower in epileptic hippocampi compared to contralateral hippocampi (p = .00019), consistent with previously published findings. In the left TLE group, the AUC values from the contralateral hippocampi showed a positive trend (p = .07) with verbal memory acquisition scores but was not statistically significant. The proposed approach is the first objective, quantitative measurement of dentation described in the literature. The AUC values numerically capture the complex surface contour information of HD and will enable future study of this interesting morphologic feature. This work demonstrates an objective method to quantify hippocampal dentation on epilepsy subjects. The proposed method converts the complex morphological feature of human hippocampus called dentation in to simple area under the curve measurements using super resolution segmentation approach.

In the Cloud computing, trust management has become a key requirement for its security as it has an important role where service-interactions take place in an anonymous environment. Trust assessment is an essential part of trust management technology for making any authorization decisions in the Cloud-based trust authorization system. The critical concern in trust assessment is the optimal assignment of weights to different factors that are involved in the trust assessment of the Cloud computing. The paper proposes a weighted averaging method for the Cloud computing paradigm wherein multiple factors are assigned weights dynamically by WMA-OWA functions. The proposed work overcomes the influence of the inflexibility of subjective weight assignment methods wherein weights are assigned manually or subjectively by experts based on their preferences such as random allocation, expert opinion, and, average weight. The experimental result shows that the proposed method can achieve greater flexibility, adaptability, and dynamic adjustment capability in the Cloud computing.

The application of Analytic Hierarchy Process (AHP) method for the prioritization of pavement maintenance sections is widespread now-a-days. Although the evaluation of pavement maintenance section through AHP method is simple, where the relative importance (on Saaty's scale) assigned to each parameter in the hierarchy varies between the experts (transportation professionals) consulted, which leads to discrepancies in the final rankings of the sections', due to the subjectivity in the process. Further, experts base their decisions solely on their experience while consideration is not given to the actual quantitative physical condition of the roads. To overcome these difficulties an objective based AHP method is proposed in this study, where pairwise comparison values are assigned based on the collected field data from a road network in Mumbai city, consisting of 28 road sections. The final ranking list of candidate sections takes into consideration the priority weight of alternatives, which reflect the road conditions. The solution of priority ratings of AHP method is compared with the corresponding solution of road condition index method, a traditional pavement maintenance procedure. The findings of the present study suggest that objective based AHP method is more suitable for the prioritization of pavement maintenance of roads.

PurposeThis study aims to apply new modifications by changing the nonlinear logarithmic calculation steps in the method based on the removal effects of criteria (MEREC) method. Geometric and harmonic mean from multiplicative functions is used for the modifications made while extracting the effects of the criteria on the overall performance one by one. Instead of the nonlinear logarithmic measure used in the MEREC method, it is desired to obtain results that are closer to the mean and have a lower standard deviation.Design/methodology/approachThe MEREC method is based on the removal effects of the criteria on the overall performance. The method uses a logarithmic measure with a nonlinear function. MEREC-G using geometric mean and MEREC-H using harmonic mean are introduced in this study. The authors compared the MEREC method, its modifications and some other objective weight determination methods.FindingsMEREC-G and MEREC-H variants, which are modifications of the MEREC method, are shown to be effective in determining the objective weights of the criteria. Findings of the MEREC-G and MEREC-H variants are more convenient, simpler, more reasonable, closer to the mean and have fewer deviations. It was determined that the MEREC-G variant gave more compatible findings with the entropy method.Practical implicationsDecision-making can occur at any time in any area of life. There are various criteria and alternatives for decision-making. In multi-criteria decision-making (MCDM) models, it is a very important distinction to determine the criteria weights for the selection/ranking of the alternatives. The MEREC method can be used to find more reasonable or average results than other weight determination methods such as entropy. It can be expected that the MEREC method will be more used in daily life problems and various areas.Originality/valueObjective weight determination methods evaluate the weights of the criteria according to the scores of the determined alternatives. In this study, the MEREC method, which is an objective weight determination method, has been expanded. Although a nonlinear measurement model is used in the literature, the contribution was made in this study by using multiplicative functions. As an important originality, the authors demonstrated the effect of removing criteria in the MEREC method in a sensitivity analysis by actually removing the alternatives one by one from the model.

Cut-off Low (COLs) are often associated with heavy precipitation and strong wind events, but there are still uncertainties on how their identification affect the numbers and seasonality. This paper aims to determine the sensitivity of identifying Southern Hemisphere COLs in the ERA-Interim reanalysis to different types of identification criteria. Upper-level cyclones are initially tracked on the 300 hPa level using relative vorticity and geopotential in order to present different perspectives. This reveals significant differences between the numbers and length of the identified tracks for the two fields. To identify the COLs different post-tracking filters are applied which are divided into two steps. Firstly, three filters are considered to separate cut-off lows from open troughs by sampling winds at different offset radial distances from the upper-level cyclonic centres. Secondly, potential vorticity and temperature criteria are imposed to determine how these conditions affect the identified COLs in terms of numbers, seasonality and intensity. It was found that methods based on multiple criteria restrict the COL identification by imposing specific characteristics, while methods based on simpler schemes (e.g. using only winds) can detect larger samples of COLs observed visually in the geopotential maps. Although it is difficult to say which method is more accurate in identifying COLs, because of the subjective aspect of observer’s assessment, a scheme using only winds should be more representative of reality as this simply imposes on the detection system a cyclonic circulation appearance regardless of the physical and dynamical characteristics. Therefore, this type of method could be considered as a standard method for identifying COLs that can be used for either operational or research purposes.