412 research outputs found
New Variations of the Online <em>k</em>-Canadian Traveler Problem: Uncertain Costs at Known Locations
In this chapter, we study new variations of the online k-Canadian Traveler Problem (k-CTP) in which there is an input graph with a given source node O and a destination node D. For a specified set consisting of k edges, the edge costs are unknown (we call these uncertain edges). Costs of the remaining edges are known and given. The objective is to find an online strategy such that the traveling agent finds a route from O to D with minimum total travel cost. The agent learns the cost of an uncertain edge, when she arrives at one of its end-nodes and decides on her travel path based on the discovered cost. We call this problem the online k-Canadian Traveler Problem with uncertain edges. We analyze both the single-agent and the multi-agent versions of the problem. We propose a tight lower bound on the competitive ratio of deterministic online strategies together with an optimal online strategy for the single-agent version. We consider the multi-agent version with two different objectives. We suggest lower bounds on the competitive ratio of deterministic online strategies to these two problems
Efficient Routing for Disaster Scenarios in Uncertain Networks: A Computational Study of Adaptive Algorithms for the Stochastic Canadian Traveler Problem with Multiple Agents and Destinations
The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as MAD-CTP. We propose two approaches, namely MAD-OMT and MAD-HOP, to solve this variant. We evaluate the performance of these algorithms on Delaunay and Euclidean graphs of varying sizes, ranging from 20 nodes with 49 edges to 500 nodes with 1500 edges. Our results demonstrate that MAD-HOP outperforms MAD-OMT by a considerable margin, achieving a replan time of under 9 seconds for all instances. Furthermore, we extend the existing state-of-the-art algorithm, UCT, which was previously shown by Eyerich et al. (2010) to be effective for solving the single-source single-destination variant of the CTP, to address the MAD-CTP problem. We compare the performance of UCT and MAD-HOP on a range of instances, and our results indicate that MAD-HOP offers better performance than UCT on most instances. In addition, UCT exhibited a very high replan time of around 10 minutes. The inferior results of UCT may be attributed to the number of rollouts used in the experiments but increasing the number of rollouts did not conclusively demonstrate whether UCT could outperform MAD-HOP. This may be due to the benefits obtained from using multiple agents, as MAD-HOP appears to benefit to a greater extent than UCT when information is shared among agents
Efficient Routing for Disaster Scenarios in Uncertain Networks: A Computational Study of Adaptive Algorithms for the Stochastic Canadian Traveler Problem with Multiple Agents and Destinations
The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as MAD-CTP. We propose two approaches, namely MAD-OMT and MAD-HOP, to solve this variant. We evaluate the performance of these algorithms on Delaunay and Euclidean graphs of varying sizes, ranging from 20 nodes with 49 edges to 500 nodes with 1500 edges. Our results demonstrate that MAD-HOP outperforms MAD-OMT by a considerable margin, achieving a replan time of under 9 seconds for all instances. Furthermore, we extend the existing state-of-the-art algorithm, UCT, which was previously shown by Eyerich et al. (2010) to be effective for solving the single-source single-destination variant of the CTP, to address the MAD-CTP problem. We compare the performance of UCT and MAD-HOP on a range of instances, and our results indicate that MAD-HOP offers better performance than UCT on most instances. In addition, UCT exhibited a very high replan time of around 10 minutes. The inferior results of UCT may be attributed to the number of rollouts used in the experiments but increasing the number of rollouts did not conclusively demonstrate whether UCT could outperform MAD-HOP. This may be due to the benefits obtained from using multiple agents, as MAD-HOP appears to benefit to a greater extent than UCT when information is shared among agents
Online routing and scheduling of search-and-rescue teams
We study how to allocate and route search-and-rescue (SAR) teams to areas with trapped victims in a coordinated manner after a disaster. We propose two online strategies for these time-critical decisions considering the uncertainty about the operation times required to rescue the victims and the condition of the roads that may delay the operations. First, we follow the theoretical competitive analysis approach that takes a worst-case perspective and prove lower bounds on the competitive ratio of the two variants of the defined online problem with makespan and weighted latency objectives. Then, we test the proposed online strategies and observe their good performance against the offline optimal solutions on randomly generated instances
Online Failure Diagnosis in Interdependent Networks
Motivation. In interdependent networks, nodes are connected to each other with respect to their failure dependency relations. As a result of this dependency, a failure in one of the nodes of one of the networks within a system of several interdependent networks can cause the failure of the entire system. Diagnosing the initial source of the failure in a collapsed system of interdependent networks is an important problem to be addressed. We study an online failure diagnosis problem defined on a collapsed system of interdependent networks where the source of the failure is at an unknown node (v). In this problem, each node of the system has a positive inspection cost and the source of the failure is diagnosed when v is inspected. The objective is to provide an online algorithm which considers dependency relations between nodes and diagnoses v with minimum total inspection cost. Methodology. We address this problem from worst-case competitive analysis perspective for the first time. In this approach, solutions which are provided under incomplete information are compared with the best solution that is provided in presence of complete information using the competitive ratio (CR) notion. Results. We give a lower bound of the CR for deterministic online algorithms and prove its tightness by providing an optimal deterministic online algorithm. Furthermore, we provide a lower bound on the expected CR of randomized online algorithms and prove its tightness by presenting an optimal randomized online algorithm. We prove that randomized algorithms are able to obtain better CR compared to deterministic algorithms in the expected sense for this online problem
ℓ-CTP: Utilizing Multiple Agents to Find Efficient Routes in Disrupted Networks
Recent hurricane seasons have demonstrated the need for more effective methods of coping with flooding of roadways. A key complaint of logistics managers is the lack of knowledge when developing routes for vehicles attempting to navigate through areas which may be flooded. In particular, it can be difficult to re-route large vehicles upon encountering a flooded roadway. We utilize the Canadian Traveller’s Problem (CTP) to construct an online framework for utilizing multiple vehicles to discover low-cost paths through networks with failed edges unknown to one or more agents a priori. This thesis demonstrates the following results: first, we develop the ℓ-CTP framework to extend a theoretically validated set of path planning policies for a single agent in combination with the iterative penalty method, which incentivizes a group of ℓ \u3e 1 agents to explore dissimilar paths on a graph between a common origin and destination. Second, we carry out simulations on random graphs to determine the impact of the addition of agents on the path cost found. Through statistical analysis of graphs of multiple sizes, we validate our technique against prior work and demonstrate that path cost can be modeled as an exponential decay function on the number of agents. Finally, we demonstrate that our approach can scale to large graphs, and the results found on random graphs hold for a simulation of the Houston metro area during hurricane Harvey
Solving Canadian Traveller Problem
Tato práce se zabývá problémem kanadského cestujícího (CTP), který se dá definovat jako problém hledání nejkratší cesty ve stochastickém prostředí. V rešeršní části práce je zpracován přehled typů tohoto problému a k nim existujících metod řešení. V dalších částech se práce zaměřuje na stochastickou variantu CTP (SCTP), pro kterou jsou vybrané metody řešení (strategie) probrány více do hloubky. Zároveň jsou prezentovány i originální strategie pojmenované UCTO2 a UCTP. Dále se práce zabývá popisem okenní aplikace implementované v jazyku Java. Ta byla vyvinuta pro ověření a otestování správné funkce vybraných strategií. Nakonec jsou vyhodnoceny provedené experimenty, a z nich plynoucí srovnání vybraných strategií.This thesis deals with Canadian traveller problem (CTP), which can be defined as the shortest path problem in a stochastic environment. The overview of different CTP variants is presented in theoretical part of this thesis, as well as known solutions to these variants. In the next parts, the thesis focuses on the stochastic variation of CTP (SCTP). For this variant chosen solutions (strategies) are discussed more in depth. At the same time, the original strategies named UCTO and UCTP are presented. Further, the thesis deals with the description of a window application implemented in Java, which has been developed to validate and test the functionality of selected strategies. The final part contains experiments and comparison of selected strategies.
Passengers, Crowding and Complexity : Models for passenger oriented public transport
Passengers, Crowding and Complexity was written as part of the Complexity in Public Transport (ComPuTr) project funded by the Netherlands Organisation for Scientific Research (NWO). This thesis studies in three parts how microscopic data can be used in models that have the potential to improve utilization, while preventing excess crowding.
_In the first part_, the emergence of crowding caused by interactions between the behavior of passengers and the public transport operators who plan the vehicle capacities is modeled. Using simulations the impact of the information disclosed to the passengers by public transport operators on the utilization and passenger satisfaction is analyzed. A quasi-experiment with a large group of students in a similar setting finds that four types of behavior can be observed.
_In the second part_, algorithms that can extract temporal and spatial patterns from smart card data are developed and a first step to use such patterns in an agent based simulation is made. Furthermore, a way to generate synthetic smart card data is proposed. This is useful for the empirical validation of algorithms that analyze such data.
_In the third and final part_ it is considered how individual decision strategies can be developed in situations where there exists uncertainty ab
Measuring Social Influence in Online Social Networks - Focus on Human Behavior Analytics
With the advent of online social networks (OSN) and their ever-expanding reach, researchers seek to determine a social media user’s social influence (SI) proficiency. Despite its exploding application across multiple domains, the research confronts unprecedented practical challenges due to a lack of systematic examination of human behavior characteristics that impart social influence. This work aims to give a methodical overview by conducting a targeted literature analysis to appraise the accuracy and usefulness of past publications. The finding suggests that first, it is necessary to incorporate behavior analytics into statistical measurement models. Second, there is a severe imbalance between the abundance of theoretical research and the scarcity of empirical work to underpin the collective psychological theories to macro-level predictions. Thirdly, it is crucial to incorporate human sentiments and emotions into any measure of SI, particularly as OSN has endowed everyone with the intrinsic ability to influence others. The paper also suggests the merits of three primary research horizons for future considerations
School Design to Promote Physical Activity
Increasing children’s physical activity (PA) at school is a national focus to address childhood obesity. Research has demonstrated associations between school built environments and students’ PA, but has lacked a comprehensive synthesis of evidence. Chapter 1 presents new evidence-, theory-, and practice-informed school design guidelines, including evidence substantiality ratings, to promote PA in school communities. These guidelines delineate strategies for school designers, planners, and educators to create K-12 school environments conducive to PA. They also engage public health scientists in needed transdisciplinary perspectives.
There have been few longitudinal studies to verify causal relationships between the school built environment and PA. Chapter 2 presents results from a natural experiment with objective PA-related measures before and after a move to a new K-5 school designed based on the Chapter 1 guidelines. The study hypothesized that the school would have desirable impacts on students’ sedentary behaviors and PA. The intervention school group was compared longitudinally with a demographically-similar group at 2 control schools. School-time analyses showed that the intervention school design had positive impact on accumulation of sedentary time, and time in light PA, likely due to movement-promoting classroom design.
Studies of built environment impacts on human behaviors and health have presented challenges in control of confounding effects. Chapter 3 presents results from experiments using an agent based model (ABM) to simulate population samples of children and to quantify the impact of a single design intervention, dynamic furniture in school, on obesity and overweight prevalence over time. Results of computational experiments showed that there could be some desirable population impact among girls with low PA profiles.
Chapter 4 places the work presented in Chapters 1-3 in a larger context. Via exploration of theories of space as a social phenomenon, of design as a discipline with human purpose, and of limitations of current public health built environment studies, the investigator proposes key strategies toward achieving substantial unrealized potential to design our built environments to achieve health
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