• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.169-180
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• 2015

Recently, scheduling problems with position-dependent processing times have received considerable attention in the literature, where the processing times of jobs are dependent on the processing sequences. However, they did not consider cases in which each processed job has different learning or aging ratios. This means that the actual processing time for a job can be determined not only by the processing sequence, but also by the learning/aging ratio, which can reflect the degree of processing difficulties in subsequent jobs. Motivated by these remarks, in this paper, we consider a two-agent single-machine scheduling problem with linear job-dependent position-based learning effects, where two agents compete to use a common single machine and each job has a different learning ratio. Specifically, we take into account two different objective functions for two agents: one agent minimizes the total weighted completion time, and the other restricts the makespan to less than an upper bound. After formally defining the problem by developing a mixed integer non-linear programming formulation, we devise a branch-and-bound (B&B) algorithm to give optimal solutions by developing four dominance properties based on a pairwise interchange comparison and four properties regarding the feasibility of a considered sequence. We suggest a lower bound to speed up the search procedure in the B&B algorithm by fathoming any non-prominent nodes. As this problem is at least NP-hard, we suggest efficient genetic algorithms using different methods to generate the initial population and two crossover operations. Computational results show that the proposed algorithms are efficient to obtain near-optimal solutions.

• International Journal of Automotive Technology
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• v.7 no.3
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• pp.369-375
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• 2006

This paper describes a simple proportional and integral control algorithm for a swirl motor controller and its application. The control algorithm may be complicated in order to have desired performance, such as low steady state errors, fast response time, and relatively low overshoot. At the same time, it should be compact so that it can be easily implemented on a low cost microcontroller, which has no floating-point calculation capability and low computing speed. These conflicting requirements are fulfilled by the proposed control algorithm which consists of a gain scheduling proportional controller and an anti-windup integral controller. The mechanical friction, which is caused by gears and a return spring, varies very nonlinearly according to the angular position of the system. This nonlinear static friction is overcome by the proportional controller, which has a two-dimensional look up gain table. It has error axis and angular position axis. The integral controller is designed not only to minimize the steady state error but also to avoid the windup effect, which may be caused by the saturation of a motor driver. The proposed control algorithm is verified by use of a commercial product to prove the feasibility of the algorithm.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.77-88
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• 2015

In this paper, we consider a two-agent single-machine scheduling problem with exponential job-dependent position-based learning effects. The objective is to minimize the total weighted completion time of one agent with the restriction that the makespan of the other agent cannot exceed an upper bound. First, we propose a branch-and-bound algorithm by developing some dominance /feasibility properties and a lower bound to find an optimal solution. Second, we design an efficient simulated annealing (SA) algorithm to search a near optimal solution by considering six different SAs to generate initial solutions. We show the performance superiority of the suggested SA using a numerical experiment. Specifically, we verify that there is no significant difference in the performance of %errors between different considered SAs using the paired t-test. Furthermore, we testify that random generation method is better than the others for agent A, whereas the initial solution method for agent B did not affect the performance of %errors.

• Journal of Platform Technology
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• v.9 no.4
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• pp.51-59
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• 2021

In this paper, we propose a location-based service that the user's location is known by connection history to WI-FI AP installed in a facility known its location previously. The location-based service proposed in this paper manages job processing states of technicians who inspect and maintain the facility like elevator by using worker's location and arrival time information. The job scheduling management method proposed in this paper is based on previously scheduled arrival time and actual arrival time to a target facility. And we experimentally implemented the inspection and maintenance system of an elevator to show the feasibility of job schedule management based on location and arrival time information acquired from Wi-Fi AP connection history. Our experimental implementation shows that our proposed method is usefulness in the field of the inspection and maintenance of a facility.

• Journal of Platform Technology
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• v.10 no.1
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• pp.11-19
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• 2022

In this paper, we propose a location-based service that the user's location is known by connection history to WI-FI AP installed in a facility known its location previously. The location-based service proposed in this paper manages job processing states of technicians who inspect and maintain the facility like elevator by using worker's location and arrival time information. The job scheduling management method proposed in this paper is based on previously scheduled arrival time and actual arrival time to a target facility. And we experimentally implemented the inspection and maintenance system of an elevator to show the feasibility of job schedule management based on location and arrival time information acquired from Wi-Fi AP connection history. Our experimental implementation shows that our proposed method is usefulness in the field of the inspection and maintenance of a facility.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.7
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• pp.429-439
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• 2006

A desktop grid, which is a computing grid composed of idle computing resources in a large network of desktop computers, is a promising platform for compute-intensive distributed computing applications. However, due to reliability and unpredictability of computing resources, effective scheduling of parallel computing applications on such a platform is a difficult problem. This paper proposes a new scheduling method aimed at reducing the total execution time of a parallel application on a desktop grid. The proposed method is based on utilizing the histories of execution behavior of individual computing nodes in the scheduling algorithm. In order to test out the feasibility of this idea, execution trace data were collected from a set of 40 desktop workstations over a period of seven weeks. Then, based on this data, the execution of several representative parallel applications were simulated using trace-driven simulation. The simulation results showed that the proposed method improves the execution time of the target applications significantly when compared to previous desktop grid scheduling methods. In addition, there were fewer instances of application suspension and failure.

• International Journal of Automotive Technology
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• v.6 no.1
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• pp.71-77
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• 2005

This paper presents a systematic approach which determines the optimal period to minimize performance measure subject to the schedulability constraints of a real-time control system by formulating the scheduling problem as an optimal problem. The performance measure is derived from the summation of end-to-end response times of processed I/Os scheduled by the static cyclic method. The schedulability constraint is specified in terms of allowable resource utilization. At first, a uniprocessor case is considered and then it is extended to a distributed system connected through a communication link, local-inter network, UN. This approach is applied to the design of an automotive body control system in order to validate the feasibility through a real example. By using the approach, a set of optimal periods can easily be obtained without complex and advanced methods such as branch and bound (B&B) or simulated annealing.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.1
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• pp.166-171
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• 2003

This paper presents the algorithms and Matlab Toolbox for co-simulation of distributed real-time control system based on OSEK-OS and CAN protocol. This toolbox enables the developers to analyze the timing uncertainty, which is caused by resource sharing including shared memories and networks, and to take the timing uncertainty into consideration in the early design phase. Furthermore, this toolbox helps the developers to model the behaviors of a control system by providing graphical user interface for objects of OSEK-OS and CAN. To prove the feasibility of this toolbox, a vehicle body network system is modeled with this toolbox, and the timing uncertainties are analyzed.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.115-131
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• 1997

The problem addressed in this paper is to minimize the deviations of workloads assigned to worker and to maximize the total utilizations(degree of skill) between workers and machines simultaneously. Each worker can handle the set of machines with the different degree of skill and each machine has the workloads needed to be processed. Also, each worker has to be assigned to at least one machine with the minimum workload deviation. This problem can be formulated as a preemptive goal programming with generalized assignment constraints. The proposed algorithm consists of two phases. First, a capacitated circulation network is constructed to assign the workers to machines with the maximum total utilizations while considering workloads balance. Then, a refinement process is applied to the split machines to satisfy the feasibility condition. The real industrial application in a plastic extrusion manufacturer is included along with several computational experiments.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.123-133
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• 1994

This paper presents an output arbitrator for input buffering ATM (Asynchronous Transfer Mode) switches using neural networks. To avoid blocking in ATM switches with blocking characteristics, it is required to buffer ATM cells in input buffer and to schedule them. The N$\times$N request matrix is divided into N/16 submatrices in order to get rid of internal blocking systematically in scheduling phase. The submatrices are grouped into N/4 groups, and the cells in each group are switched alternatively. As the window size of input buffer is increases, the number of input cells switched in a time slot approaches to N. The selection of nonblocking cells to be switched is done by neural network modules. N/4 neural network modules are operated simultaneously. Fast selection can be achieved by massive parallelism of neural networks. The neural networks have 4N neurons and 14N connection. The proposed method is implemented in C language, and the simulation result confirms the feasibility of this method.