• Journal of Korea Multimedia Society
• /
• v.10 no.12
• /
• pp.1741-1751
• /
• 2007

The imprecise computation technique ensures that all time-critical tasks produce their results before their deadlines by trading off the quality of the results for the computation time requirements of the tasks. In the imprecise computation, most scheduling problems of satisfying both 0/1 constraints and timing constraints, while the total error is minimized, are NP-complete when the optional tasks have arbitrary processing times. In the previous studies, the reasonable strategies of scheduling tasks with the 0/1 constraints on uniprocessors and multiprocessors for minimizing the total error are proposed. But, these algorithms are all off-line algorithms. Then, in the on-line scheduling, NORA(No Off-line tasks and on-line tasks Ready upon Arrival) algorithm can find a schedule with the minimum total error. In NORA algorithm, EDF(Earliest Deadline First) strategy is adopted in the scheduling of optional tasks. On the other hand, for the task system with 0/1 constraints, NORA algorithm may not suitable any more for minimizing total error of the imprecise tasks. Therefore, in this paper, an on-line algorithm is proposed to minimize total error for the imprecise real-time task system with 0/1 constraints. This algorithm is suitable for the imprecise on-line system with 0/1 constraints. Next, to evaluate performance of this algorithm, a series of experiments are done. As a consequence of the performance comparison, it has been concluded that IOSMTE(Imprecise On-line Scheduling to Minimize Total Error) algorithm proposed in this paper outperforms LOF(Longest Optional First) strategy and SOF(Shortest Optional First) strategy for the most cases.

• Journal of Internet Computing and Services
• /
• v.19 no.2
• /
• pp.33-41
• /
• 2018

In recent years, technology is rapidly changing to support new service consumption and distribution models in multimedia service systems and hybrid delivery of media services is a key factor for enabling next generation multimedia services. This phenomenon can lead to rapidly increasing network traffic and ultimately has a direct and aggravating effect on the user's quality of service (QOS). To address the issue, we propose a novel system architecture to provide smart hybrid media services efficiently. The architecture is designed to apply the software-defined networking (SDN) method, detect changes in traffic, and combine the data, including user data, service features, and computation node status, to provide a service schedule that is suitable for the current state. To this end, the proposed architecture is based on 2-level scheduling, where Level-1 scheduling is responsible for the best network path and a computation node for processing the user request, whereas Level-2 scheduling deals with individual service requests that arrived at the computation node. This paper describes the overall concept of the architecture, as well as the functions of each component. In addition, this paper describes potential scenarios that demonstrate how this architecture could provide services more efficiently than current media-service architectures.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.6
• /
• pp.3851-3857
• /
• 2014

Shippers' requests of freight trains vary with time, but generating an additional schedule of freight trains is not easy due to many considerations, such as the line capacity, operation rules, and conflicts with existing trains. On the other hand, an additional freight train schedule has been continuously requested and manually processed by domestic train operation companies using empirical method, which is time consuming. This paper proposes a model to determine the additional freight train schedule that assesses the feasibility of the added freight trains, and generates as many additional schedules as possible, while minimizing the delay of the existing schedules. The problem is presented using time-space network, modeled as multi-commodity flow problem, and solved using the column generation method. Three levels of experiment were conducted to show validity of the proposed model in the computation time.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.8
• /
• pp.3201-3223
• /
• 2020

Wireless communication has become the promising technology in the recent times because of its applications in Internet of Things( IoT) devices. The IEEE 802.15.4e has become the key technology for IoT devices which utilizes the Time-Slotted Channel Hopping (TSCH) networks for the communication between the devices. In this paper, we develop a Two Level Scheduling Algorithm (TLSA) for scheduling multiple packets with different arrival rate at the source nodes in a TSCH networks based on the link activated by a centralized scheduler. TLSA is developed by considering three types of links in a network such as link i with packets arrival type 1, link j with packets arrival type 2, link k with packets arrival type 3. For the data packets arrival, two stages in a network is considered.At the first stage, the packets are considered to be of higher priority.At the second stage, the packets are considered to be of lower priority.We introduce level 1 schedule for the packets at stage 1 and level 2 schedule for the packets at stage 2 respectively. Finally, the TLSA is validated with the two different energy functions i.e., y = e^ax - 1 and y = 0.5x² using MATLAB 2017a software for the computation of average and worst ratios of the two levels.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.2
• /
• pp.111-120
• /
• 2017

This paper proposes an improved standard genetic algorithm (GA) of making a near optimal schedule for integrated process planning and scheduling problem (IPPS) considering tool flexibility and tool related constraints. Process planning involves the selection of operations and the allocation of resources. Scheduling, meanwhile, determines the sequence order in which operations are executed on each machine. Due to the high degree of complexity, traditionally, a sequential approach has been preferred, which determines process planning firstly and then performs scheduling independently based on the results. The two sub-problems, however, are complicatedly interrelated to each other, so the IPPS tend to solve the two problems simultaneously. Although many studies for IPPS have been conducted in the past, tool flexibility and capacity constraints are rarely considered. Various meta-heuristics, especially GA, have been applied for IPPS, but the performance is yet satisfactory. To improve solution quality against computation time in GA, we adopted three methods. First, we used a random circular queue during generation of an initial population. It can provide sufficient diversity of individuals at the beginning of GA. Second, we adopted an inferior selection to choose the parents for the crossover and mutation operations. It helps to maintain exploitation capability throughout the evolution process. Third, we employed a modification of the hybrid scheduling algorithm to decode the chromosome of the individual into a schedule, which can generate an active and non-delay schedule. The experimental results show that our proposed algorithm is superior to the current best evolutionary algorithms at most benchmark problems.

• IE interfaces
• /
• v.10 no.1
• /
• pp.209-222
• /
• 1997

The paper provides a methodology to obtain the automaticity indicator of a factory and the sequence of enabling technologies of factory automation. The automaticity indicator is the measure of the current automation status of a factory and can be used as a crucial criteria for the future automation schedule and investment. Although most industries have their own computation methods which usually consider the number of workers in the shop floor, this research covers five evaluation items of automation, such as, production facility, material transfer system, inspection and test system, information system, and flexibility. The detailed evaluation models are developed for each item. Automation sequencing prioritizes the enabling technologies of factory automation on the basis of several criteria which consist of two phases. The first phase includes the automation indicator and the second phase includes six sub-criteria such as production rate, quality, number of workers, capital investment, development duration, development difficulty. For this evaluation, AHP(Analytical Hierarchy Process) is introduced to prevent the decision maker's subject intention. As results of the automaticity indicator and automation sequence, the manager can save time and cost in building constructive and transparent automation plans.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.5
• /
• pp.949-966
• /
• 2013

Recently, Wireless Body Area Network (WBAN) has promise to revolutionize human daily life. The need for multiple sensors and constant monitoring lead these systems to be energy hungry and expensive with short operating lifetimes. In this paper, we offer a review of existing work of WBAN and focus on energy-aware management in it. We emphasize that nodes computation, wireless communication, topology deployment and energy scavenging are main domains for making a long-lived WBAN. We study the popular power management technique Dynamic Voltage and Frequency Scaling (DVFS) and identify the impact of slack time in Dynamic Power Management (DPM), and finally propose an enhanced dynamic power management method to schedule scaled jobs at slack time with the goal of saving energy and keeping system reliability. Theoretical and experimental evaluations exhibit the effectiveness and efficiency of the proposed method.

• Journal of Korean Institute of Industrial Engineers
• /
• v.21 no.3
• /
• pp.409-428
• /
• 1995

Job shop scheduling with m different machines and n different jobs is a NP-hard problem of combinatorial optimization. The purpose of the paper is to develop the heuristic method using tabu search for job shop scheduling to minimize makespan or mean flowtime. To apply tabu search to job shop scheduling problem, in this paper we propose the several move methods that employ insert moves in order to generate the neighbor solutions, and present the efficient rescheduling procedure that yields active schedule for a changed operation sequence by a move of operations. We also discuss the tabu search techniques of diversifying the search of solution space as well as the simple tabu search. By experiments, we find the appropriate tabu list size and tabu attributes, and analyze the proposed tabu search techniques with respect to the quality of solutions and the efforts of computation. The experimental results show that the proposed tabu search techniques using long-term memory function have the ability to search a good solution, and are more efficient in the mean flowtime minimization problem than in the makespan minimization.

• Proceedings of the KSME Conference
• /
• 2004.11a
• /
• pp.522-527
• /
• 2004

Resealed Simulated Annealing (RSA) has been devised for improving the disadvantage of Simulated Annealing (SA) which require tremendous amount of computation time. RSA and SA have been for optimization of satellite structures and for comparison of results from two algorithms. As a practical application, a satellite structure is optimized by the two algorithms. Weights of satellite upper platform and propulsion module are minimized. MSC/NASTRAN is used for the static and dynamic analysis. The optimization results of the RSA are compared with results of the classical SA. The numbers of optimization iterations could be effectively reduced by the RSA.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.23 no.2
• /
• pp.15-27
• /
• 1998

In the parallel processing systems, a compiler partitions a loaded program into tasks, allocates the tasks on multiple processors and schedules the tasks on each allocated processor. In this paper we suggest a Genetic Algorithm(GA) based scheduling method to find an optimal allocation and sequence of tasks on each Processor. The suggested method uses a chromosome which consists of task sequence and binary string that represent the number and order of tasks on each processor respectively. Two correction algorithms are used to maintain precedency constraints of the tasks in the chromosome. This scheduling method determines the optimal number of processors within limited numbers, and then finds the optimal schedule for each processor. A result from computational experiment of the suggested method is given.