111 research outputs found

    Approximation algorithms for mobile multi-agent sensing problem

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2020. 8. ๋ฌธ์ผ๊ฒฝ.Multi-agent systems are generally applicable in a wide diversity of domains, such as robot engineering, computer science, the military, and smart cities. In particular, the mobile multi-agent sensing problem can be defined as a problem of detecting events occurring in a large number of nodes using moving agents. In this thesis, we introduce a mobile multi-agent sensing problem and present a mathematical formulation. The model can be represented as a submodular maximization problem under a partition matroid constraint, which is NP-hard in general. The optimal solution of the model can be considered computationally intractable. Therefore, we propose two approximation algorithms based on the greedy approach, which are global greedy and sequential greedy algorithms, respectively. We present new approximation ratios of the sequential greedy algorithm and prove tightness of the ratios. Moreover, we show that the sequential greedy algorithm is competitive with the global greedy algorithm and has advantages of computation times. Finally, we demonstrate the performances of our results through numerical experiments.๋‹ค์ค‘ ์—์ด์ „ํŠธ ์‹œ์Šคํ…œ์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๋กœ๋ด‡ ๊ณตํ•™, ์ปดํ“จํ„ฐ ๊ณผํ•™, ๊ตฐ์‚ฌ ๋ฐ ์Šค๋งˆํŠธ ๋„์‹œ์™€ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ, ๋ชจ๋ฐ”์ผ ๋‹ค์ค‘ ์—์ด์ „ํŠธ ๊ฐ์ง€ ๋ฌธ์ œ๋Š” ์›€์ง์ด๋Š” ์—์ด์ „ํŠธ๋ฅผ ์ด์šฉํ•ด ๋งŽ์€ ์ˆ˜์˜ ๋…ธ๋“œ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ด๋ฒคํŠธ๋ฅผ ๊ฐ์ง€ํ•˜๋Š” ๋ฌธ์ œ๋กœ ์ •์˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ชจ๋ฐ”์ผ ๋‹ค์ค‘ ์—์ด์ „ํŠธ ๊ฐ์ง€ ๋ฌธ์ œ์˜ ์ˆ˜ํ•™์  ๊ณต์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๋ฌธ์ œ๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ NP-๋‚œํ•ด ๋ฌธ์ œ์ธ ๋ถ„ํ•  ๋งคํŠธ๋กœ์ด๋“œ ์ œ์•ฝ ํ•˜์—์„œ ํ•˜์œ„ ๋ชจ๋“ˆ ํ•จ์ˆ˜์˜ ์ตœ๋Œ€ํ™” ๋ฌธ์ œ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฌธ์ œ์˜ ์ตœ์ ํ•ด๋Š” ์ž…๋ ฅ ๋ฐ์ดํ„ฐ์˜ ํฌ๊ธฐ๊ฐ€ ์ปค์งˆ์ˆ˜๋ก ๋ณดํ†ต ํ•ฉ๋ฆฌ์ ์ธ ์‹œ๊ฐ„ ์ด๋‚ด์— ๊ณ„์‚ฐํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํƒ์š•์  ์ ‘๊ทผ ๋ฐฉ์‹์— ๊ธฐ์ดˆํ•œ ๋‘ ๊ฐ€์ง€ ๊ทผ์‚ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜ (์ „์—ญ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜, ์ˆœ์ฐจ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜)์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, ์ˆœ์ฐจ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์ƒˆ๋กœ์šด ๊ทผ์‚ฌ ๋น„์œจ์„ ์ฆ๋ช…ํ•˜๊ณ  ๊ทผ์‚ฌ ๋น„์œจ์— ์ •ํ™•ํ•˜๊ฒŒ ์ผ์น˜ํ•˜๋Š” ์ธ์Šคํ„ด์Šค๋ฅผ ์ œ์‹œํ•œ๋‹ค. ๋˜ํ•œ, ์ˆ˜์น˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋กœ ์ˆœ์ฐจ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ํšจ๊ณผ์ ์ธ ํ•ด๋ฅผ ์ฐพ์•„์ค„ ๋ฟ ์•„๋‹ˆ๋ผ, ์ „์—ญ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๋น„๊ตํ•ด ๊ณ„์‚ฐ ์‹œ๊ฐ„์˜ ์ด์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•œ๋‹ค.Chapter 1 Introduction 1 Chapter 2 Literature Review 4 Chapter 3 Problem statement 7 Chapter 4 Algorithms and approximation ratios 11 Chapter 5 Computational Experiments 22 Chapter 6 Conclusions 30 Bibliography 31 ๊ตญ๋ฌธ์ดˆ๋ก 40Maste

    A two-level facility location and sizing problem for maximal coverage

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    This paper presents a two-stage hierarchical location problem for systems where the lower level facilities act as the first points contact for the customers while the upper level facilities act as suppliers of the lower level facilities that either serve them or provide advanced services to customers. Furthermore, more recent and realistic coverage constructs such as gradual and cooperative covering are included in our setting. Although our problem can be applicable in various settings, the most fitting application is in wireless telecommunication networks to determine the location of base stations and mobile switching centers. We have developed two competing formulations for the problem, each of which involve nonlinear components that are difficult to deal with. We then develop their respective linearizations and tested their performances. These formulations are solved by commercial optimizers for a set of reasonably large problem instances and it is found that majority of the problems can be solved within a maximum of 10% optimality gap within a short time

    Hybrid Modelling and Simulation (M&S): Driving Innovation in the Theory and Practice of M&S

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    This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordHybrid Simulation (HS) is the application of two or more simulation techniques (e.g., ABS, DES, SD) in a single M&S study. Distinct from HS, Hybrid Modelling (HM) is defined as the combined application of simulation approaches (including HS) with methods and techniques from the broader OR/MS literature and also across disciplines. In this paper, we expand on the unified conceptual representation and classification of hybrid M&S, which includes both HS (Model Types A-C), hybrid OR/MS models (D, D.1) and crossdisciplinary hybrid models (Type E), and assess their innovation potential. We argue that model types associated with HM (D, D.1, E), with its focus on OR/MS and cross-disciplinary research, are particularly well-placed in driving innovation in the theory and practice of M&S. Application of these innovative HM methodologies will lead to innovation in the application space as new approaches in stakeholder engagement, conceptual modelling, system representation, V&V, experimentation, etc. are identified

    Photovoltaics and Electrification in Agriculture

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    Integration of photovoltaics and electrification in agriculture. Works on the integration of photovoltaics in agriculture, as well as electrification and microgrids in agriculture. In addition, some works on sustainability in agriculture are added

    Dynamics in Logistics

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    This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions
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