More scalable solution for multi-robot–multi-target assignment problem

Marangoz, Salih; Amasyalı, Mehmet Fatih; Uslu, Erkan; Çakmak, Furkan; Altuntas, Nihal; Yavuz, Sırma

doi:10.1016/j.robot.2019.01.005

Cited by 6 publications

(4 citation statements)

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“…The results of this study support previous research (Hussein & Shiker, 2020;Divya & Balaji, 2020;Marangoz et al, 2019;Li et al, 2018;Supian et al, 2018) which found that the use of the Hungarian method in assignments can save costs and time.…”

Section: A Forming An Assignment Matrixsupporting

confidence: 90%

“…Optimization of Assignment Problems Using the Hungarian Method, research on assignment problems includes allocating sources of work that 1 (one) person can carry out at a certain time . The relationship between the number of workers and financing is described in a one-way assignment that is maximized for profit (Marangoz et al, 2019). The profits earned by each person are directed to completing one job in a specified time according to the financing (paid) and the column (time for completion of work) (Li et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Division of Tasks Using the Hungarian Method

Zain

Akbar

2022

JBMR

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Optimization is the process of optimizing existing resources in order to obtain effective and efficient results. The maximum division of tasks is when each employee does one job at a time. The division of work needs to be seriously handled so that every employee gets the maximum wage according to the working hours determined by the company. This research was conducted in the Rumah Jahit Nia, Sewing House industry in Pekanbaru City, Riau Province, Indonesia. The purpose of this study is that the company can divide each worker’s tasks to minimize the budget expenditure for paying overtime salaries so that the sources of financing and time used to complete the work are minimized. The research design uses quantitative data with applied research. The analysis method uses an assignment model with the Hungarian method. The completion of the Hungarian method is that the number of employees employed must be equal to the number of jobs that must be completed. In this study, the data source is seven employees with seven jobs to produce the minimum total financing. The study results obtained minimal financing with one employee doing one job. The minimization optimization results are Operator A, assignment IV financing Rp.13,333,-; Operator B, assignment VII with the financing of IDR 20,000; Operator C, assignment VI, financing Rp. 30,000, -; Operator D, assignment III with the financing of Rp. 25,333,-; Operator E, assignment I financing Rp. 25,000, - ; Operator F, assignment V financing Rp. 42,000, - ; Operator G, assignment II financing Rp.33,750,- Total minimum financing Rp.189,416,-.

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Section: A Forming An Assignment Matrixsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Division of Tasks Using the Hungarian Method

Zain

Akbar

2022

JBMR

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“…The assignment problem is also commonly known as the classic scheduling problem, where there are workers assigned to complete jobs [4,5]. Many problems in the real world applied the assignment problem, ranging from personnel scheduling [6][7][8], train scheduling [5], workforce planning [4], smart parking system [9], robotic assignment system [1,10,11], cloud computing [12], weapon target assignment [13,14] and many others. The main objective of the assignment problem is to find an optimal and efficient pair of workers and jobs [15].…”

Section: Introductionmentioning

confidence: 99%

A Fast Dynamic Assignment Algorithm for Solving Resource Allocation Problems

Amalia

Saikhu

Soelaiman

2021

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The assignment problem is one of the fundamental problems in the field of combinatorial optimization. The Hungarian algorithm can be developed to solve various assignment problems according to each criterion. The assignment problem that is solved in this paper is a dynamic assignment to find the maximum weight on the resource allocation problems. The dynamic characteristic lies in the weight change that can occur after the optimal solution is obtained. The Hungarian algorithm can be used directly, but the initialization process must be done from the beginning every time a change occurs. The solution becomes ineffective because it takes up a lot of time and memory. This paper proposed a fast dynamic assignment algorithm based on the Hungarian algorithm. The proposed algorithm is able to obtain an optimal solution without performing the initialization process from the beginning. Based on the test results, the proposed algorithm has an average time of 0.146 s and an average memory of 4.62 M. While the Hungarian algorithm has an average time of 2.806 s and an average memory of 4.65 M. The fast dynamic assignment algorithm is influenced linearly by the number of change operations and quadratically by the number of vertices.

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“…In that case, for each type of subtask, a robot team of appropriate type must be employed. This case poses an unexplored MRTA problem whose coupling and cooperation between those complementary teams is the motivation of this work.In the context of MRTA, notable attention has been devoted for revealing various aspects of dynamic problems [14][15][16][17][18]. For instance, [19] present a mathematical model of a general dynamic task allocation mechanism where robots use only local sensing and no direct communication is needed between them for task allocation.…”

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confidence: 99%

Multiagent Task Allocation in Complementary Teams: A Hunter-and-Gatherer Approach

et al. 2020

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Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment. This paper considers each task comprised of two sequential subtasks: detection and completion, where each subtask can only be carried out by a certain type of agent. We address this problem using a novel nature-inspired approach called "hunter and gatherer". The proposed method employs two complementary teams of agents: one agile in detecting (hunters) and another dexterous in completing (gatherers) the tasks. To minimize the collective cost of task accomplishments in a distributed manner, a game-theoretic solution is introduced to couple agents from complementary teams. We utilize market-based negotiation models to develop incentive-based decision-making algorithms relying on innovative notions of "certainty and uncertainty profit margins". The simulation results demonstrate that employing two complementary teams of hunters and gatherers can effectually improve the number of tasks completed by agents compared to conventional methods, while the collective cost of accomplishments is minimized. In addition, the stability and efficacy of the proposed solutions are studied using Nash equilibrium analysis and statistical analysis respectively. It is also numerically shown that the proposed solutions function fairly, i.e. for each type of agent, the overall workload is distributed equally.Index Terms-Distributed multiagent system, dynamic task allocation, game theory, negotiation. I. INTRODUCTIONMultirobot systems are expected to undertake imperative roles in a wide variety of fields such as urban search and rescue (USAR) [1,2], agricultural field operations [3], security patrols [4,5], environmental monitoring [6], and industrial procedures [7]. Studies have shown that multi-robot systems have advantage over single-robot systems by offering more reliability, redundancy, and time efficiency when the nature of the tasks is inherently distributed [8]. Nonetheless, the problem of multi-robot task-allocation (MRTA) poses many critical challenges that has called for investigation in the past two decades [9][10][11]. In this regards, the complexity of MRTA problems increases significantly in a dynamic environment, where the number and location of tasks are unknown for agents [12,13]. Thus, robots need to explore the environment to find tasks before accomplishing them. In real world problems, any robot designated to perform one of the tasks in [1-7] needs to be sufficiently dexterous which makes it relatively heavy and incapable of agile exploration. Having said that, the dynamic problem can be turned into a problem where each task is comprised of sequential subtasks, each possible to be done only by a certain type of agent. In that case, for each type of subtask, a robot team of appropriate type must be employed. This case poses an unexplored MRTA problem whose coupling and cooperation between those complementary teams is the motivation of this work.In the context of MRTA, notable attention has been devoted for revealing ...

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More scalable solution for multi-robot–multi-target assignment problem

Cited by 6 publications

References 20 publications

Division of Tasks Using the Hungarian Method

Division of Tasks Using the Hungarian Method

A Fast Dynamic Assignment Algorithm for Solving Resource Allocation Problems

Multiagent Task Allocation in Complementary Teams: A Hunter-and-Gatherer Approach

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