Optimization-based decision-making models for disaster recovery and reconstruction planning of transportation networks

The purpose of this study is to analyze optimization-based decision-making models for the problem of Disaster Recovery Planning of Transportation Networks (DRPTN). In the past three decades, seminal optimization problems have been structured and solved for the critical and sensitive problem of DRPTN. The extent of our knowledge on the practicality of the methods and performance of results is however limited. To evaluate the applicability of those context-sensitive models in real-world situations, there is a need to examine the conceptual and technical structure behind the existing body of work. To this end, this paper performs a systematic search targeting DRPTN publications. Thereafter, we review the identified literature based on the four phases of the optimization-based decision-making modeling process as problem definition, problem formulation, problem-solving, and model validation. Then, through content analysis and descriptive statistics, we investigate the methodology of studies within each of these phases. Eventually, we detect and discuss four research improvement areas as [1] developing conceptual or systematic decision support in the selection of decision attributes and problem structuring, [2] integrating recovery problems with traffic management models, [3] avoiding uncertainty due to the type of solving algorithms, and [4] reducing subjectivity in the validation process of disaster recovery models. Finally, we provide suggestions as well as possible directions for future research.TU Berlin, Open-Access-Mittel - 202

The impact of the conflict on solving distributed constraint satisfaction problems

Distributed Constraint Satisfaction Problems (DCSPs) involve a vast number of AI andMulti-Agent problems. Many important efforts have been recen accomplished for solving these kinds of problems using both backtracking-based and mediation-based methods. One of the most successful mediation based algorithms in this field is Asynchronous Partial Overlay (APO) algorithm. By choosing some agents as mediators, APO tries to centralize portions of the distributed problem, and then each mediator tries to solve its centralized sub-problem. This work continues until the whole problem is solved. This paper presents a new strategy to select mediators. The main idea behind this strategy is that the number of mediators conflicts (violated constraints) impacts directly on its performance. Experimental results show that choosing the mediators with the most number of conflicts not only leads to considerable decrease in APO complexity, but also it can decrease the complexity of the other extensions of the APO such as IAPO algorithm. MaxCAPO and MaxCIAPO are two new expansions of APO which introduce this idea and are presented in this article. The results of using this mediator selection strategy show a rapid and desirable improvement over various parameters in comparison with APO and IAP

Analysis of Bifurcations in a Wind Turbine System Based on DFIG

This main aim of this study is investigation of the dynamic stability in a grid-connected wing turbine system based on Double Feed Induction Generator (DFIG) using the bifurcation theory. Regarding the overview of stability by Cardenas et. al. [1]. In our research, the proposed system model is simulated based on bifurcation theory in MATLAB software. In each step, one of the controlling or non-controlling parameters is selected. Eigenvalues of system are traced permanently during simulation. According to the change of the eigenvalues of system, due to the change of bifurcation parameter, stability of the equilibrium point and special bifurcations including saddle-node and Hopf bifurcations in the system are determined

In vitro evaluation of antimicrobial activities from aqueous and methanolic extracts of cyanobacteria

In this present study, antimicrobial activities of aqueous and methanolic extracts of cyanobacteria against some of fungi and pathogenic bacteria were investigated. Cyanobacteria strains Fischerella ambigua ISC67 and Schizothrix vaginata ISC108 were cultured in BG-11 medium. Extraction was performed by adding the solvent to cyanobacterial biomass and then filtering and drying of the mixture. The antimicrobial activity was evaluated by disc diffusion method and broth microdilution method was applied to determine the minimum inhibitory concentration. The results show that the aqueous and methanolic extracts of F. ambigua has a significant antimicrobial effect while, the tested extracts of S. vaginata was no significant antibacterial and antifungal activity. Highest antibacterial activity from aqueous extract of F. ambigua was against S. aureus (PTCC 1112) which the average zone diameter around it was 33.33 mm. The antibacterial effect of aqueous extracts against Gram-positive bacteria was more than Gram-negative bacteria significantly. Antifungal activity showed that methanolic extract of F. ambigua have significant antifungal activity. Minimum inhibitory concentration of active extract against most tested bacterial and fungal was 125 mg/ml. The present study has proved that the aqueous and methanolic extracts of F. ambigua possessed strong antibacterial and antifungal properties against the pathogenic microorganism. Therefore, cyanobacteria can be a rich source for natural products with antimicrobial activity. DOI: http://dx.doi.org/10.5281/zenodo.346363

Developing Programming Tools to Handle Traveling Salesman Problem by the Three Object-Oriented Languages

The traveling salesman problem (TSP) is one of the most famous problems. Many applications and programming tools have been developed to handle TSP. However, it seems to be essential to provide easy programming tools according to state-of-theart algorithms. Therefore, we have collected and programmed new easy tools by the three object-oriented languages. In this paper, we present ADT (abstract data type) of developed tools at first; then we analyze their performance by experiments. We also design a hybrid genetic algorithm (HGA) by developed tools. Experimental results show that the proposed HGA is comparable with the recent state-of-the-art applications

A prescriptive framework for recommending decision attributes of infrastructure disaster recovery problems

This paper proposes a framework to systematically evaluate and select attributes of decision models used in disaster risk management. In doing so, we formalized the attribute selection process as a sequential screening-utility problem by formulating a prescriptive decision model. The aim is to assist decision-makers in producing a ranked list of attributes and selecting a set among them. We developed an evaluation process consisting of ten criteria in three sequential stages. We used a combination of three decision rules for the evaluation process, alongside mathematically integrated compensatory and non-compensatory techniques as the aggregation methods. We implemented the framework in the context of disaster resilient transportation network to investigate its performance and outcomes. Results show that the framework acted as an inclusive systematic decision aiding mechanism and promoted creative and collaborative decision-making. Preliminary investigations suggest the successful application of the framework in evaluating and selecting a tenable set of attributes. Further analyses are required to discuss the performance of the produced attributes. The properties of the resulting attributes and feedback of the users suggest the quality of outcomes compared to the retrospective attributes that were selected in an unaided selection process. Research and practice can use the framework to conduct a systematic problem-structuring phase of decision analysis and select an equitable set of decision attributes.TU Berlin, Open-Access-Mittel – 202