24 research outputs found

    Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

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    In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects

    Distributed multi-agent pathfinding in horizontal transportation

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    Horizontal transportation in maritime container terminals plays a crucial role in ensuring safe, efficient, and cost-effective operations. Heavy working machines, such as straddle carriers, trucks, and automated guided vehicles, transport containers between cranes, creating complex routing problems known as multi-agent pathfinding (MAPF) problems. Existing solutions may not adequately address the unique challenges presented by container terminals, necessitating the development of new algorithms. This thesis aims to develop and demonstrate a distributed MAPF algorithm for horizontal transportation in container terminals. The MAPF problem is first formulated as a binary linear programming (BLP) model by expressing the actions in the container terminal using a directed pseudograph. Optimal solutions are obtained using PYOMO, an open-source Python-based optimization software. The Augmented Lagrangian, a graph pathfinding algorithm, and a stochastic element are then employed to create a sub-optimal, distributed algorithm. The developed algorithm is evaluated against an optimal solution and a reference method that prioritizes calculating the path for one agent at a time while taking into account previously calculated paths. A simulator is set up to emulate horizontal transportation in a maritime container terminal, by modeling the terminal as a graph in MATLAB. In the simulator, MAPF algorithms are applied in combination with a high-level coordinator assigning destinations. The experimental part of this thesis investigates the trade-off between solution time (iterations) and solution quality by tuning algorithm parameters and evaluating the performance of the distributed algorithm in comparison to the priority-based method under two different map layouts, particularly addressing the presence of a bottleneck. The results demonstrate the need to adapt the algorithm's parameters and strategies according to specific environments and map layouts, to ensure good performance across various scenarios. The main contribution of this thesis lies in the development of a adaptable, distributed MAPF solution that can ultimately address diverse scenarios and environments. Merikonttiterminaaleissa konttien vaakatasoinen kuljettaminen on keskeinen tekijä turvallisen, tehokkaan ja kustannustehokkaan toiminnan varmistamisessa. Raskaat työkoneet, kuten konttilukit, kuorma-autot ja automaattisti ohjatut ajoneuvot (AGV:t), kuljettavat kontteja nosturien välillä, luoden monimutkaisia reititysongelmia, joita kutsutaan monitoimija-reitinhaku (MAPF) -ongelmiksi. Olemassa olevat ratkaisut eivät riittävästi käsittele konttiterminaaleille asetettuja erityisiä haasteita, mikä edellyttää uusien hajautettujen MAPF-algoritmien kehittämistä. Tämän diplomityön tavoitteena on kehittää ja esitellä hajautettu MAPF-algoritmi vaakasuuntaiseen kuljetukseen konttiterminaaleissa. MAPF-ongelma mallinnetaan ensin binääriseksi lineaariseksi ohjelmointimalliksi (BLP), jossa konttiterminaalissa liikkuminen mallinnetaan suunnattuna pseudograafina. Optimaalisia ratkaisuja tutkitaan käyttämällä PYOMO-ohjelmistoa, joka on avoimen lähdekoodin Python-pohjainen optimointiohjelmisto. Tämän jälkeen laajennettua Lagrangen kertoimien menetelmää, graafin polunetsintä algoritmia ja stokastistista elementtiä käytetään luomaan lähes optimaalinen, hajautettavissa oleva algoritmi. Kehitettyä algoritmia arvioidaan vertaamalla sitä optimaaliseen ratkaisuun ja viite-menetelmään, joka priorisoi polkujen laskemista yhden agentin kerrallaan ottaen huomioon aiemmin lasketut polut. Simulaattori rakennetaan jäljittelemään vaakasuuntaista kuljetusta merikonttiterminaaleissa, mallintamalla terminaali graafina MATLABissa. Simulaattorissa MAPF-algoritmeja sovelletaan yhdessä korkean tason koordinaattorin kanssa, joka määrittelee määränpäät. Diplomityön kokeellisessa osuudessa tarkastellaan ratkaisuajan (iteraatioiden) ja ratkaisun laadun välistä tasapainoa kokeilemalla erilaisia parametreja ja arvioimalla hajautetun algoritmin suorituskykyä verrattuna prioriteettipohjaiseen menetelmään kahden erilaisen karttapohjan päällä, keskittyen erityisesti pullonkaulan lisäämisen tuomiin vaikutuksiin. Tulokset osoittavat, että algoritmin parametrien ja strategioiden mukauttaminen erityisiin ympäristöihin ja karttapohjiin on tarpeen, jos halutaan varmistua tulosten olevan mahdollisimman lähellä optimaalisuutta erilaisissa skenaarioissa. Tämän diplomityön pääasiallinen kontribuutio on joustavan, hajautetun MAPF-ratkaisun kehittäminen, jolla voidaan tulevaisuudessa käsitellä erilaisia tilanteita ja ympäristöjä

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    LIPIcs, Volume 258, SoCG 2023, Complete Volume

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    LIPIcs, Volume 258, SoCG 2023, Complete Volum

    The Parameterized Complexity of Coordinated Motion Planning

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    Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality

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    Conflict-Based Search is one of the most popular methods for multi-agent path finding. Though it is complete and optimal, it does not scale well. Recent works have been proposed to accelerate it by introducing various heuristics. However, whether these heuristics can apply to non-grid-based problem settings while maintaining their effectiveness remains an open question. In this work, we find that the answer is prone to be no. To this end, we propose a learning-based component, i.e., the Graph Transformer, as a heuristic function to accelerate the planning. The proposed method is provably complete and bounded-suboptimal with any desired factor. We conduct extensive experiments on two environments with dense graphs. Results show that the proposed Graph Transformer can be trained in problem instances with relatively few agents and generalizes well to a larger number of agents, while achieving better performance than state-of-the-art methods.Comment: Accepted by ICRA 202

    LIPIcs, Volume 248, ISAAC 2022, Complete Volume

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    LIPIcs, Volume 248, ISAAC 2022, Complete Volum
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