Methods and algorithms to solve the vehicle routing problem with time windows and further conditions

Kovács, George L.; Yusupova, Nafissa; Сметанина, О. Н.; Rassadnikova, Ekaterina

doi:10.1556/606.2018.13.1.6

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…graph-based algorithms; 2. algorithms for avoiding obstacles; 3. algorithms using intelligent methods. Analysis of the literature has shown that recently, many different methods and algorithms have been proposed for route planning [4][5][6][7][8][9][10][11][12][13][14]. The article [6] presents a solution for planning the shortest route for moving a robot in a maze based on the Voronoi graph and has the following form in fig.…”

Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

Approach to Building a Global Mobile Agent Way Based on Q-Learning

Martovytskyi

Ivaniuk

2020

Innovative Technologies and Scientific Solutions for Industries

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Today, the problem of navigation of autonomous mobile systems in a space where disturbances are possible is urgent. The task of finding a route for a mobile robot is a complex and non-trivial task. At the moment, there are many algorithms that allow you to solve such problems in accordance with the specified criteria for building a route. Most of these algorithms are modifications of "basic" path planning methods that are optimized for specific conditions. The subject of research in the article is the process of building a global path for a mobile agent. The purpose of the work is to create an algorithm for planning the route of autonomous mobile systems in space using the Q-learning algorithm. The following tasks are solved in the article: development of an approach to training and support of a reinforcement learning algorithm for building a global path of a mobile agent; testing the agent's ability to find a path in environments that are not in the training set. The following methods are used: graph theory, queuing theory, Markov decisionmaking process theory and mathematical programming methods. The research is based on scientific articles and other materials from foreign conferences and archives in the field of machine learning, deep learning and deep reinforcement learning. The following results were obtained: an approach was formulated to construct the global path of a mobile agent based on the accumulated data in the process of interaction with the external environment. The environment rewards these actions and the agent continues to carry them out. This approach will allow this method to be applied to a wide range of situations and devices. Conclusions: This approach allows accumulating the knowledge of the outside world for further decision-making when planning a route where the robot can acquire the skill of self-learning, studying and training like a human, and finding the path from the initial state to the target state in an unknown environment. In the modern world, the use of robots and autonomous systems is spreading, designed to replace or facilitate human labor, make it safer and speed it up. Adaptive autonomous path finding algorithms are very important in many robotics applications. Thus, navigation tasks with limited information are relevant today, since this is the main task that the agent solves, and one of the tasks that are part of the robot during operation.

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Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

Approach to Building a Global Mobile Agent Way Based on Q-Learning

Martovytskyi

Ivaniuk

2020

Innovative Technologies and Scientific Solutions for Industries

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“…Each of the options for organizing regular transportation requires some costs, as well as it has both advantages and disadvantages. The following major principles are base in the evaluating the effectiveness of the project: consideration of the option (project) throughout the calculation period, positivity and maximum effect, time factor record, the inflation impact, uncertainty, risks [9], and suchlike. It is recommended that the beginning of the calculation period should be taken as the date of allocation of funds for the project work.…”

Section: Methods For Evaluating the Effectiveness Of Optionsmentioning

confidence: 99%

Providing the railway transit traffic Ukraine–European Union

Kurhan

2019

Pollack Periodica

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The authors developed a model for predicting and assessing the efficiency of railway transport from the border of one state to the border of the other one, taking into account all expenditures by the net present value indicator net present value of discounted cash flow. The results allows planning measures to accelerate the cargo delivery, both on the territory of Ukraine, and during the transfer of cargoes at border stations, to predict the revenue generation from the implementation of international transportation, which is very important for attracting investments.

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“…The control tasks of modular robots associated with the formation and reconfiguration of the kinematic structure are given in [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The listed works propose a methodological approach to the development of methods and algorithms for reconfiguration the structure of robots with a mechatronicmodular design.…”

Section: Introductionmentioning

confidence: 99%

“…The task of reconfiguring the kinematic structure of MMR can be considered as a variant of the general problem of planning and controlling the movement of the robot, which has been studied in robotics for many decades [9][10][11][12]. However, this problem differs from traditional approaches in that the connectivity of the modules (or the topology of the kinematic structure of the modular robot) also change if necessary.…”

Section: Introductionmentioning

confidence: 99%

Method for Reconfiguring the Kinematic Structure of a Mechatronic-Modular Robot in Non-Deterministic Conditions

Петренко¹,

Тебуева²,

Pavlov³

et al. 2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

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Modular robots, consisting of many identical modules, are one of the most difficult areas of robotics. Each newly added element changes the shape and capabilities of the end device, for example, adds functionality or allows the robot to move in new planes. The reconfiguration of the kinematic structure is a sequence of movements of each robot module from the initial position of the initial configuration to the final position of the desired configuration. The paper considers a method for reconfiguration the kinematic structure of a mechatronic-modular robot using reinforcement learning. The proposed method will be built on the basis of a learning algorithm, where the information for training will be the actions taken and the "reward" is a value characterizing the quality of the robot's completion of the target task. The purpose of the training is to build a control algorithm that maximizes the total reward for a certain period of time. The effectiveness of the learning algorithm was tested by computer simulation of a robot, consisting of 5, 10 and 15 modules, in the formation of the target configuration.

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Methods and algorithms to solve the vehicle routing problem with time windows and further conditions

Cited by 8 publications

References 13 publications

Approach to Building a Global Mobile Agent Way Based on Q-Learning

Approach to Building a Global Mobile Agent Way Based on Q-Learning

Providing the railway transit traffic Ukraine–European Union

Method for Reconfiguring the Kinematic Structure of a Mechatronic-Modular Robot in Non-Deterministic Conditions

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