1,685 research outputs found

    Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

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    Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

    The multi-visit drone-assisted pickup and delivery problem with time windows

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    We consider a new combined truck-drone routing problem with time windows in the context of last-mile logistics. A fleet of trucks, each equipped with an identical drone, is scheduled to provide both pickup and delivery services to a set of customers with minimum cost. Some customers are paired, in that the goods picked up from one must be delivered to the other on the same route. Drones are launched from and retrieved by trucks at a pool of designated stations, which can be used multiple times. Each drone can serve multiple customers in one flight. We formulate this problem as a large-scale mixed-integer bilinear program, with the bilinear terms used to calculate the load-time-dependent energy consumption of drones. To accelerate the solution process, multiple valid inequalities are proposed. For large-size problems, we develop a customised adaptive large neighbourhood search (ALNS) algorithm, which includes several preprocessing procedures to quickly identify infeasible solutions and accelerate the search process. Moreover, two feasibility test methods are developed for trucks and drones, along with an efficient algorithm to determine vehicles’ optimal waiting time at launch stations, which is important to consider due to the time windows. Extensive numerical experiments demonstrate the effectiveness of the valid inequalities and the strong performance of the proposed ALNS algorithm over two benchmarks in the literature, and highlight the cost-savings of the combined mode over the truck-only mode and the benefits of allowing multiple drone visits

    Solving the dynamic traveling salesman problem using a genetic algorithm with trajectory prediction: an application to fish aggregating devices

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    The paper addresses the synergies from combining a heuristic method with a predictive technique to solve the Dynamic Traveling Salesman Problem (DTSP). Particularly, we build a genetic algorithm that feeds on Newton's motion equation to show how route optimization can be improved when targets are constantly moving. Our empirical evidence stems from the recovery of fish aggregating devices (FADs) by tuna vessels. Based on historical real data provided by GPS buoys attached to the FADs, we first estimate their trajectories to feed a genetic algorithm that searches for the best route considering their future locations. Our solution, which we name Genetic Algorithm based on Trajectory Prediction (GATP), shows that the distance traveled is significantly shorter than implementing other commonly used methods.European Regional Development Fund | Ref. 10SEC300036PRMinisterio de Economía y Competitividad | Ref. ECO2013-45706

    Integrating forecasting in metaheuristic methods to solve dynamic routing problems: evidence from the logistic processes of tuna vessels

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    The multiple Traveling Salesman Problem (mTSP) is a widespread phenomenon in real-life scenarios, and in fact it has been addressed from multiple perspectives in recent decades. However, mTSP in dynamic circumstances entails a greater complexity that recent approaches are still trying to grasp. Beyond time windows, capacity and other parameters that characterize the dynamics of each scenario, moving targets is one of the underdeveloped issues in the field of mTSP. The approach of this paper harnesses a simple prediction method to prove that integrating forecasting within a metaheuristic evolutionary-based method, such as genetic algorithms, can yield better results in a dynamic scenario than their simple non-predictive version. Real data is used from the retrieval of Fish Aggregating Devices (FADs) by tuna vessels in the Indian Ocean. Based on historical data registered by the GPS system of the buoys attached to the devices, their trajectory is firstly forecast to feed subsequently the functioning of a genetic algorithm that searches for the optimal route of tuna vessels in terms of total distance traveled. Thus, although valid for static cases and for the Vehicle Routing Problem (VRP), the main contribution of this method over existing literature lies in its application as a global search method to solve the multiple TSP with moving targets in many dynamic real-life optimization problems.Ministerio de Economía y Competitividad | Ref. ECO2016-76625-RXunta de Galicia | Ref. GRC2014/02

    A new matheheuristic approach based on Chu-Beasley genetic approach for the multi-depot electric vehicle routing problem

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    Operations with Electric Vehicles (EVs) on logistic companies and power utilities are increasingly related due to the charging stations representing the point of standard coupling between transportation and power networks. From this perspective, the Multi-depot Electric Vehicle Routing Problem (MDEVRP) is addressed in this research, considering a novel hybrid matheheuristic approach combining exact approaches and a Chu-Beasley Genetic Algorithm. An existing conflict is shown in three objectives handled through the experimentations: routing cost, cost of charging stations, and increased cost due to energy losses. EVs driving range is chosen as the parameter to perform the sensitivity analysis of the proposed MDEVRP. A 25-customer transportation network conforms to a newly designed test instance for methodology validation, spatially combined with a 33 nodes power distribution system

    Perbandingan Implementasi Evolutionary Algorithm (EPO, FHO, dan CFA) pada Kasus Travelling Salesman Problem untuk Tempat Pariwisata di Surabaya

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    Traveling merupakan bisnis yang tumbuh pesat di seluruh dunia, dan Indonesia tidak terkecuali. Di Indonesia, khususnya Surabaya, industri pariwisata telah mengalami peningkatan dalam beberapa tahun terakhir, dan diharapkan akan terus tumbuh dalam beberapa tahun ke depan. Dengan peningkatan tersebut, pencarian rute untuk pariwisata harus efisien dan cepat, salah satu solusi yang populer saat ini adalah Evolutionary Algorithms (EA). Algoritma evolusi adalah jenis teknik optimisasi yang meniru proses evolusi alami untuk menemukan solusi terhadap masalah yang kompleks. Salah satu permasalahan yang dapat diselesaikan dengan efektif menggunakan algoritma evolusi adalah Traveling Salesman Problem (TSP). Permasalahan tersebut melibatkan pengunjungan pada beberapa kota dan menemukan rute terpendek untuk kembali ke titik awal. Beberapa algoritma evolusi telah dicadangkan untuk menyelesaikan TSP, seperti algoritma Cuttlefish (CFA), Emperor Penguin Optimizer (EPO) dan Fire Hawk Optimizer (FHO). Algoritma sotong didasarkan pada perilaku sotong liar, EPO terinspirasi oleh perilaku berkerumun dari penguin kaisar, sedangkan FHO menggunakan prinsip propagasi api. Semua algoritma yang telah disebutkan tadi memiliki potensi untuk menyelesaikan TSP dengan keunikannya masing-masing. Kesimpulan kami untuk semua algoritma yang digunakan dalam penelitian ini adalah bahwa EPO berhasil menemukan solusi terbaik diikuti dengan solusi dari CFA dan FHO. Berdasarkan hasil percobaan kami, didapatkan EPO menghasilkan solusi 39.97% lebih baik dari CFA serta 14.75% lebih baik dari FHO secara rata-rata. Serta EPO juga memiliki waktu komputasi rata-rata lebih cepat (69.59% lebih cepat dari CFA dan 178.34% lebih cepat dari FHO)

    Exploration autonome et efficiente de chantiers miniers souterrains inconnus avec un drone filaire

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    Abstract: Underground mining stopes are often mapped using a sensor located at the end of a pole that the operator introduces into the stope from a secure area. The sensor emits laser beams that provide the distance to a detected wall, thus creating a 3D map. This produces shadow zones and a low point density on the distant walls. To address these challenges, a research team from the Université de Sherbrooke is designing a tethered drone equipped with a rotating LiDAR for this mission, thus benefiting from several points of view. The wired transmission allows for unlimited flight time, shared computing, and real-time communication. For compatibility with the movement of the drone after tether entanglements, the excess length is integrated into an onboard spool, contributing to the drone payload. During manual piloting, the human factor causes problems in the perception and comprehension of a virtual 3D environment, as well as the execution of an optimal mission. This thesis focuses on autonomous navigation in two aspects: path planning and exploration. The system must compute a trajectory that maps the entire environment, minimizing the mission time and respecting the maximum onboard tether length. Path planning using a Rapidly-exploring Random Tree (RRT) quickly finds a feasible path, but the optimization is computationally expensive and the performance is variable and unpredictable. Exploration by the frontier method is representative of the space to be explored and the path can be optimized by solving a Traveling Salesman Problem (TSP) but existing techniques for a tethered drone only consider the 2D case and do not optimize the global path. To meet these challenges, this thesis presents two new algorithms. The first one, RRT-Rope, produces an equal or shorter path than existing algorithms in a significantly shorter computation time, up to 70% faster than the next best algorithm in a representative environment. A modified version of RRT-connect computes a feasible path, shortened with a deterministic technique that takes advantage of previously added intermediate nodes. The second algorithm, TAPE, is the first 3D cavity exploration method that focuses on minimizing mission time and unwound tether length. On average, the overall path is 4% longer than the method that solves the TSP, but the tether remains under the allowed length in 100% of the simulated cases, compared to 53% with the initial method. The approach uses a 2-level hierarchical architecture: global planning solves a TSP after frontier extraction, and local planning minimizes the path cost and tether length via a decision function. The integration of these two tools in the NetherDrone produces an intelligent system for autonomous exploration, with semi-autonomous features for operator interaction. This work opens the door to new navigation approaches in the field of inspection, mapping, and Search and Rescue missions.La cartographie des chantiers miniers souterrains est souvent réalisée à l’aide d’un capteur situé au bout d’une perche que l’opérateur introduit dans le chantier, depuis une zone sécurisée. Le capteur émet des faisceaux laser qui fournissent la distance à un mur détecté, créant ainsi une carte en 3D. Ceci produit des zones d’ombres et une faible densité de points sur les parois éloignées. Pour relever ces défis, une équipe de recherche de l’Université de Sherbrooke conçoit un drone filaire équipé d’un LiDAR rotatif pour cette mission, bénéficiant ainsi de plusieurs points de vue. La transmission filaire permet un temps de vol illimité, un partage de calcul et une communication en temps réel. Pour une compatibilité avec le mouvement du drone lors des coincements du fil, la longueur excédante est intégrée dans une bobine embarquée, qui contribue à la charge utile du drone. Lors d’un pilotage manuel, le facteur humain entraîne des problèmes de perception et compréhension d’un environnement 3D virtuel, et d’exécution d’une mission optimale. Cette thèse se concentre sur la navigation autonome sous deux aspects : la planification de trajectoire et l’exploration. Le système doit calculer une trajectoire qui cartographie l’environnement complet, en minimisant le temps de mission et en respectant la longueur maximale de fil embarquée. La planification de trajectoire à l’aide d’un Rapidly-exploring Random Tree (RRT) trouve rapidement un chemin réalisable, mais l’optimisation est coûteuse en calcul et la performance est variable et imprévisible. L’exploration par la méthode des frontières est représentative de l’espace à explorer et le chemin peut être optimisé en résolvant un Traveling Salesman Problem (TSP), mais les techniques existantes pour un drone filaire ne considèrent que le cas 2D et n’optimisent pas le chemin global. Pour relever ces défis, cette thèse présente deux nouveaux algorithmes. Le premier, RRT-Rope, produit un chemin égal ou plus court que les algorithmes existants en un temps de calcul jusqu’à 70% plus court que le deuxième meilleur algorithme dans un environnement représentatif. Une version modifiée de RRT-connect calcule un chemin réalisable, raccourci avec une technique déterministe qui tire profit des noeuds intermédiaires préalablement ajoutés. Le deuxième algorithme, TAPE, est la première méthode d’exploration de cavités en 3D qui minimise le temps de mission et la longueur du fil déroulé. En moyenne, le trajet global est 4% plus long que la méthode qui résout le TSP, mais le fil reste sous la longueur autorisée dans 100% des cas simulés, contre 53% avec la méthode initiale. L’approche utilise une architecture hiérarchique à 2 niveaux : la planification globale résout un TSP après extraction des frontières, et la planification locale minimise le coût du chemin et la longueur de fil via une fonction de décision. L’intégration de ces deux outils dans le NetherDrone produit un système intelligent pour l’exploration autonome, doté de fonctionnalités semi-autonomes pour une interaction avec l’opérateur. Les travaux réalisés ouvrent la porte à de nouvelles approches de navigation dans le domaine des missions d’inspection, de cartographie et de recherche et sauvetage

    The Agricultural Spraying Vehicle Routing Problem With Splittable Edge Demands

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    In horticulture, spraying applications occur multiple times throughout any crop year. This paper presents a splittable agricultural chemical sprayed vehicle routing problem and formulates it as a mixed integer linear program. The main difference from the classical capacitated arc routing problem (CARP) is that our problem allows us to split the demand on a single demand edge amongst robotics sprayers. We are using theoretical insights about the optimal solution structure to improve the formulation and provide two different formulations of the splittable capacitated arc routing problem (SCARP), a basic spray formulation and a large edge demands formulation for large edge demands problems. This study presents solution methods consisting of lazy constraints, symmetry elimination constraints, and a heuristic repair method. Computational experiments on a set of valuable data based on the properties of real-world agricultural orchard fields reveal that the proposed methods can solve the SCARP with different properties. We also report computational results on classical benchmark sets from previous CARP literature. The tested results indicated that the SCARP model can provide cheaper solutions in some instances when compared with the classical CARP literature. Besides, the heuristic repair method significantly improves the quality of the solution by decreasing the upper bound when solving large-scale problems.Comment: 25 pages, 8 figure

    ОСОБЛИВОСТІ ЗАСТОСУВАННЯ АЛГОРИТМУ АСО ДО ДЕЯКИХ ЗАДАЧ КРИПТОАНАЛІЗУ

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    Requirements for information security dictate the necessity of developing new methods of cryptanalysis. Modern cryptanalysis depend on mathematics, in particular on theory and optimization methods. Taking into account the generally recognized requirements for attack resistance of ciphers, the decryption problem should be considered as a combinatorial optimization problem The paper proves the necessary of  the development of new methods of cryptanalysis using metaheuristics, contains a retrospective review of publications in the last period in this area. The number of publications indicates the relevance of the research direction. Specialities of the application of the Ant Colony Optimization algorithm to cryptanalysis problems, in particular, factorization problem, are considered. The structure and general principles of the ACO algorithm are described, as well as the adaptation of this algorithm to the solution of a specific problem of combinatorial optimization. Various variants of the fitness function, features of their application, methods of narrowing the search space, rules for choosing the direction of movement on the graph, modification of local search are discussed. The addition of genetic operators of crossover, mutation, and selection is considered as one of the modification options. The conditions for stopping the operation of the algorithm are described. The various facts of using metaheuristics for solving combinatorial optimization problems arising in numerous subject areas, in particular, in cryptanalysis, are described.  It is emphasized that since theoretical studies of combinatorial optimization algorithms rarely allow obtaining results that can be applied in practice. The main tool for analyzing their effectiveness is a computational experiment.Вимоги до інформаційної безпеки диктують неохідність розвитку нових методів криптоаналізу. Сучасний криптоаналіз спирається на математику, зокрема на теорію та методи оптимізації. Враховучи загальновизнані вимоги до зламостійкості шифрів, задача розшифрування мусить розглядатися, як задача комбінаторної оптимізації. В роботі обґрунтовується необхідність розвитку нових методів криптоаналізу із застосуванням метаевристик, міститься ретрспективний огляд публікацій за останній період в даній області. Кількість публікацій свідчить про актуальність напрямку досліджень. Розглядаються особливості застосування алгоритму АСО (Ant Colony Optimization) до задач криптоаналізу, зокрема, задачі факторизації. Описується структура і загальні принципи роботи алгоритму АСО, адаптація даного алгоритму до розв’язання конкретної задачі комбінаторної оптимізації. Розглянуто різні варіанти фітнес-функції, особливості їх застосування, способи звуження простору пошуку, правила вибору напрямку руху на графі, модифікація локального пошуку. Як один із варіантів модифікації розглядається додавання генетичних операторів кросоверу, мутації, селекції. Описано умови припинення роботи алгоритму. Обґрунтовано доцільність застосування метаевристик для розв’зання задач комбінаторної оптимізації що виникають у різних предметних областях, зокрема, у криптоаналізі. Підкреслюється, що так як теоретичні дослідження алгоритмів комбінаторної оптимізації рідко дозволяють отримувати результати, які можуть бути застосовані на практиці, то основним інструментом аналізу їх ефективності є обчислювальний експеримент
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