• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.79-86
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• 2020

To make a satisfactory decision regarding project scheduling, a trade-off between the resource-related cost and project duration must be considered. A beneficial method for decision makers is to provide a number of alternative schedules of diverse project duration with minimum resource cost. In view of optimization, the alternative schedules are Pareto sets under multi-objective of project duration and resource cost. Assuming that resource cost is closely related to resource leveling, a heuristic algorithm for resource capacity reduction (HRCR) is developed in this study in order to generate the Pareto sets efficiently. The heuristic is based on the fact that resource leveling can be improved by systematically reducing the resource capacity. Once the reduced resource capacity is given, a schedule with minimum project duration can be obtained by solving a resource-constrained project scheduling problem. In HRCR, VNS (Variable Neighborhood Search) is implemented to solve the resource-constrained project scheduling problem. Extensive experiments to evaluate the HRCR performance are accomplished with standard benchmarking data sets, PSPLIB. Considering 5 resource leveling objective functions, it is shown that HRCR outperforms well-known multi-objective optimization algorithm, SPEA2 (Strength Pareto Evolutionary Algorithm-2), in generating dominant Pareto sets. The number of approximate Pareto optimal also can be extended by modifying weight parameter to reduce resource capacity in HRCR.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.173-180
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• 2005

In this paper, we propose an efficient real-time scheduling method of multimedia tasks for decoding frames of MPEG-2 video streams. In our task model, each frame is decoded by a separate multimedia task. The decoding task for each frame is assigned to the priority according to the precedence and importance of frames in a video stream. We use a priority-based scheduling policy in order to effectively allocate the CPU bandwidth to multimedia tasks for MPEG-2 decoding. We show how to dynamically control the fraction of the CPU bandwidth allocated to each multimedia task according to the priority. The primary purpose of our scheduling method is to enhance the real-time performance of the multimedia system by minimizing the number of decoding tasks that have missed their deadlines while reducing the decoding times of these multimedia tasks. The performance of this scheduling method is compared with that of similar mechanisms through simulation experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1164-1183
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• 2019

The rapid development of cloud computing and high requirements of operators requires strong support from the underlying Data Center Networks. Therefore, the effectiveness of using resources in the data center networks becomes a point of concern for operators and material for research. In this paper, we discuss the online virtual-cluster provision problem for multiple tenants with an aim to decide when and where the virtual cluster should be placed in a data center network. Our objective is maximizing the total revenue for the data center networks under the constraints. In order to solve this problem, this paper divides it into two parts: online multi-tenancy scheduling and virtual cluster placement. The first part aims to determine the scheduling orders for the multiple tenants, and the second part aims to determine the locations of virtual machines. We first approach the problem by using the variational inequality model and discuss the existence of the optimal solution. After that, we prove that provisioning virtual clusters for a multi-tenant data center network that maximizes revenue is NP-hard. Due to the complexity of this problem, an efficient heuristic algorithm OMS (Online Multi-tenancy Scheduling) is proposed to solve the online multi-tenancy scheduling problem. We further explore the virtual cluster placement problem based on the OMS and propose a novel algorithm during the virtual machine placement. We evaluate our algorithms through a series of simulations, and the simulations results demonstrate that OMS can significantly increase the efficiency and total revenue for the data centers.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.563-570
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• 2004

Genetic algorithms have been successfully applied to various optimization problems belonging to NP-hard problems. The sequential ordering problems(SOP) and the job shop scheduling problems(JSP) are well-known NP-hard problems with strong influence on industrial applications. Both problems share some common properties in that they have some imposed precedence constraints. When genetic algorithms are applied to this kind of problems, it is desirable for genetic operators to be designed to produce chromosomes satisfying the imposed precedence constraints. Several genetic operators applicable to such problems have been proposed. We call such genetic operators precedence-preserving genetic operators. This paper presents three existing precedence-preserving genetic operators: Precedence -Preserving Crossover(PPX), Precedence-preserving Order-based Crossover (POX), and Maximum Partial Order! Arbitrary Insertion (MPO/AI). In addition, it proposes two new operators named Precedence-Preserving Edge Recombination (PPER) and Multiple Selection Precedence-preserving Order-based Crossover (MSPOX) applicable to such problems. It compares the performance of these genetic operators for SOP and JSP in the perspective of their solution quality and execution time.

• Journal of Korean Society for Quality Management
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• v.14 no.1
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• pp.33-38
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• 1986

This paper deals with probabilistic order level inventory system which the quantity ordered at the end of the scheduling period is dependent on lead times. To find an optimal solution, pearson system of distributions is used to approximate the probability density function of the on-order quantity. An example is solved and sensitivity analysis is performed to examine the relation between lead times and the ordering quantity.

• Journal of the Korean Operations Research and Management Science Society
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• v.5 no.1
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• pp.53-59
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• 1980

An order level inventory model is developed for deteriorating items. The demand during prescribed scheduling period is constant and deterministic in which the demand follows power pattern. Deterioration is assumed to be a constant fraction of the on hand inventory. The expression for the optimal order level is developed and an example is given to illustrate the model.

• Korean Management Science Review
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• v.18 no.2
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• pp.1-10
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• 2001

Due to environmental change in market, delivery satisfaction to customer end redaction of LeadTime are critical In the Make-to-Order manufacturing system. This paper focuses on Order Feasibility Decision System Based on SNS System. We suggest BOP (Bill of Process) in which aggregated information is used When the load p1anning problem is solved while more detailed information is used when the scheduling problem is solved.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.145-170
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• 2005

This paper presents scheduling models for dispatching vehicles to accomplish a sequence of container jobs at the container terminal, in which the starting times as well as the order of vehicles for carrying out these jobs need to be determined. To deal with this scheduling problem, three mixed 0-1 integer programming models, Model 1, Model 2 and Model 3 are provided. We present interesting techniques to reformulate the two mixed integer programming models, Model 1 and Model 2, as pure 0-1 integer programming problems with simple constraint sets and present a lower bound for the optimal value of Model 1. Model 3 is a complicated mixed integer programming model because it involves a set of non-smooth constraints, but it can be proved that its solutions may be obtained by the so-called greedy algorithm. We present numerical results showing that Model 3 is the best among these three models and the greedy algorithm is capable of solving large scale problems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.9C
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• pp.836-841
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• 2002

Packet scheduling is an essential function to guarantee a quality of service by differentiating services in the Internet. Scheduling algorithms that have been suggested so far can be devided into Round-Robin methods and Fair Queueing methods Round-Robin methods have the advantage of high-speed processing through simple implementations, while Fair Queueing methods offer accurate services. Fair queueing algorithms, however, have problems of computational overheads and implementation complexity as their schedulers manage the states of every flow. This paper suggests a new method in which each flow performs the calculation in a distributed way to decide the service order. Our algorithm significantly reduces the scheduler's computational overheads while providing the same level of accuracy with the previous Fair Queueing algorithms.

• The Journal of Society for e-Business Studies
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• v.20 no.1
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• pp.79-88
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• 2015

Many servers, such as web and database servers, receive a continual stream of requests. The servers should schedule these requests to provide the best services to users. In this paper, a hybrid genetic algorithm is proposed for scheduling divisible requests without interruption in which the objective is to minimize the total stretch. The stretch of a request is the ratio of the amount of time the request spent in the system to its response time. The hybrid genetic algorithm adopts the idea of seed selection and development in order to improve the exploitation and exploration power of genetic algorithms. Extensive computational experiments have been conducted to compare the performance of the hybrid genetic algorithm with that of genetic algorithms.