• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1151-1161
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• 1998

In this paper, we are considering a heterogeneous processor system in which each processor may have different performance and reliability characteristics. In other to fully utilize this diversity of processing power it is advantageous to assign the program modules of a distributed program to the processors in such a way that the execution time of the entire program is minimized. This assignment of tasks to processors to maximize performance is commonly called load balancing, since the overloaded processors can perform their own processing with the performance degradation. For the task assignment problem, we propose a new objective function which formulates this imbalancing cost. Thus the task assignment problem is to be carried out so that each module is assigned to a processor whose capabilities are most appropriate for the module, and the total cost is minimized that sum of inter-processor communication cost and execution cost and imbalance cost of the assignment. To find optimal assignment is known to be NP-hard, and thus we proposed an efficient heuristic algorithm with time complexity $O(n^2m)$ in case of m task modules and n processors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3566-3582
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• 2019

In intelligent warehouse picking system, the allocation of tasks has an important influence on the efficiency of the whole system because of the large number of robots and orders. The paper proposes a method to solve the task allocation problem that multi-robot task allocation problem is transformed into transportation problem to find a collision-free task allocation scheme and then improve the capability of task processing. The task time window and the power consumption of multi-robot (driving distance) are regarded as the utility function and the maximized utility function is the objective function. Then an integer programming formulation is constructed considering the number of task assignment on an agent according to their battery consumption restriction. The problem of task allocation is solved by table working method. Finally, simulation modeling of the methods based on table working method is carried out. Results show that the method has good performance and can improve the efficiency of the task execution.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3413-3430
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• 2017

With the fast increasing popularity of mobile services, ubiquitous mobile devices with enhanced sensing capabilities collect and share local information towards a common goal. The recent Mobile Crowd Sensing (MCS) paradigm enables a broad range of mobile applications and undoubtedly revolutionizes many sectors of our life. A critical challenge for the MCS paradigm is to induce mobile devices to be workers providing sensing services. In this study, we examine the problem of sensing task assignment to maximize the overall performance in MCS system while ensuring reciprocal advantages among mobile devices. Based on the overlapping coalition game model, we propose a novel workload determination scheme for each individual device. The proposed scheme can effectively decompose the complex optimization problem and obtains an effective solution using the interactive learning process. Finally, we have conducted extensive simulations, and the results demonstrate that the proposed scheme achieves a fair tradeoff solution between the MCS performance and the profit of individual devices.

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.141-149
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• 2011

We consider the problem of assigning tasks to homogeneous nodes in the distributed system, so as to minimize the amount of communication, while balancing the processors' loads. This issue can be posed as the graph partitioning problem. Given an undirected graph G=(nodes, edges), where nodes represent task modules and edges represent communication, the goal is to divide n, the number of processors, as to balance the processors' loads, while minimizing the capacity of edges cut. Since these two optimization criteria conflict each other, one has to make a compromise between them according to the given task type. We propose a new cost function to evaluate static task assignments and a heuristic algorithm to solve the transformed problem, explicitly describing the tradeoff between the two goals. Simulation results show that our approach outperforms an existing representative approach for a variety of task and processing systems.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.11
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• pp.988-996
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• 2012

This paper presents a distributed task assignment algorithm for the suppression of enemy air defense (SEAD) mission of heterogeneous UAVs, based on the consensus-based bundle algorithm (CBBA). SEAD mission can be modeled as a task assignment problem of multiple UAVs performing multiple air defense targets, and UAVs performing SEAD mission consist of the weasel for destruction of enemy's air defense system and the striker for the battle damage assessment (BDA) or other tasks. In this paper, a distributed task assignment algorithm considering path-planning in presence of terrain obstacle is developed for heterogeneous UAVs, and then it is applied to SEAD mission. Through numerical simulations the performance and the applicability of the proposed method are tested.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.47-53
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• 1995

The Generalized Assignment (GAP) determines the minimum assignment of n tasks to m workstations such that each task is assigned to exactly one workstation, subject to the capacity of a workstation. In this paper, we presented a new heuristic search algorithm for GAPs. Then we tested it on 4 different benchmark sample sets of random problems generated according to uniform distribution on a microcomputer.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.102-108
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• 2020

One of the key features of a mobile crowd-sensing (MCS) system is task allocation, which aims to recruit workers efficiently to carry out the tasks. Due to various constraints of the tasks (such as specific sensor requirement and a probabilistic guarantee of task completion) and workers heterogeneity, the task allocation become challenging. This assignment problem becomes more intractable because of the deadline of the tasks and a lot of possible task completion order or moving path of workers since a worker may perform multiple tasks and need to physically visit the tasks venues to complete the tasks. Therefore, in this paper, a hybrid search algorithm for task allocation called HST is proposed to address the problem, which employ a traveling salesman problem heuristic to find the task completion order. HST is developed based on the tabu search algorithm and exploits the premature convergence avoiding concepts from the genetic algorithm and simulated annealing. The experimental results verify that our proposed scheme outperforms the existing methods while satisfying given constraints.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.1-22
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• 1993

This paper deals with the problem of mixed model assembly line balancing. In mixed model assembly lines, tsks should be assigned to stations in such a manner that all stations have approximately the same amount of work on a production cycle basis. Further in balancing assembly lines, the related tasks, the performing task side and the team tasks should be considered to improve work methods, to give more job satisfaction to workers, and to allow greater flexibility in the design of assembly lines. In this paper, the heuristic dispatch assignment rule is developed to assign evenly tasks of each model to all stations. The heuristic method based on the assignment rule developed is presented for mixed model assembly line balancing with the considerations of the related tasks, the performing task side, and the team tasks. The proposed method is analyzed, and compared with other methods for line balancing.

• International Journal of Automotive Technology
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• v.8 no.1
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• pp.39-48
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• 2007

This paper addresses the problem of period and priority assignment in networked control systems (NCSs) using a fixed priority scheduler. The problem of assigning periods and priorities to tasks and messages is formulated as an optimization problem to allow for a systematic approach. The temporal characteristics of an NCS should be considered by defining an appropriate performance index (PI) which represents the temporal behavior of the NCS. In this study, the sum of the end-to-end response times required to process all I/Os with precedence relationships is defined as a PI. Constraints are derived from the task and message deadline requirements to guarantee schedulability. Genetic algorithms are used to solve this constrained optimization problem because the optimization formulation is discrete and nonlinear. By considering the effects of communication, an optimum set of periods and priorities can be holistically derived.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.11a
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• pp.51-55
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• 2005

Assuring integrity of permission assignment (PA) constraints is a difficult task in role-based access control (RBAC) because of the large number of constraints, users, roles and permissions in a large enterprise environment. We provide solutions for this problem using the conflict concept. This paper introduces the conflict model in order to understand the conflicts easily and to detect conflicts effectively. The conflict model is classified as a permission-permission model and a role-permission model. This paper defines two type conflicts using the conflict model. The first type is an inter-PA-constraints (IPAC) conflict that takes place between PA constraints. The other type is a PA-PAC conflict that takes place between a PA and a PA constraint (PAC) Also, the conditions of conflict occurrence are formally specified and proved. We can assure integrity on permission assignment by checking conflicts before PA and PA constraints are applied.