• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.6
• /
• pp.1877-1891
• /
• 2022

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers. In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm. We verified its effectiveness through simulation experiments. In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0. First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018. Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability. The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris. DeepEnergyJSV2.0 achieves better energy efficiency than DeepEnergyJS by about 7.814%.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.10
• /
• pp.1721-1730
• /
• 2007

SThis paper proposes a new dispatch scheduling algorithm in interconnected regional system operations. The dispatch scheduling formulated as mixed integer non-linear programming (MINLP) problem can efficiently be computed by generalized Benders decomposition (GBD) algorithm. GBD guarantees adequate computation speed and solution convergency since it decomposes a primal problem into a master problem and subproblems for simplicity. In addition, the inter-temporal optimal power flow (OPF) subproblem of the dispatch scheduling problem is comprised of various variables and constraints considering time-continuity and it makes the inter-temporal OPF complex due to increased dimensions of the optimization problem. In this paper, regional decomposition technique based on auxiliary problem principle (APP) algorithm is introduced to obtain efficient inter-temporal OPF solution through the parallel implementation. In addition, it can find the most economic dispatch schedule incorporating power transaction without private information open. Therefore, it can be expanded as an efficient dispatch scheduling model for interconnected system operation.

• Journal of Korean Institute of Industrial Engineers
• /
• v.18 no.1
• /
• pp.91-104
• /
• 1992

Flowshop scheduling problem is known to be NP-complete. Since the optimization apporach like branch-and-bound is limited by exponentially growing computation time, many heuristic methods have been developed. Total tardiness is one of the criteria that the researchers have recently considered in flowshop scheduling. There, however, are few literatures which studied the general (m machine)-flowshop scheduling under the total tardiness criterion. In this paper, a heuristic scheduling method to minimize total tardiness at the (m machine, n job)-flowshop is presented. A heuristic value function is proposed to be used as a dispatching criterion in initial schedule generation. And the schedule improving procedure, by pairwise interchange of tardy job with the job right ahead of it, is introduced. Illustrative examles and simulated results are presented.

• Journal of Information Processing Systems
• /
• v.19 no.4
• /
• pp.450-464
• /
• 2023

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

• International conference on construction engineering and project management
• /
• 2007.03a
• /
• pp.777-786
• /
• 2007

This paper presents a modified simulated annealing algorithm to optimize linear scheduling projects with multiple resource constraints and its effectiveness is verified with a proposed problem. A two-stage solution-finding procedure is used to model the problem. Then the simulated annealing and the modified simulated annealing are compared in the same condition. The comparison results and the reasons of improvement by the modified simulated annealing are presented at the end.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.8
• /
• pp.3516-3541
• /
• 2018

Task scheduling is one of the most challenging aspects of cloud computing nowadays, and it plays an important role in improving overall performance in, and services from, the cloud, such as response time, cost, makespan, and throughput. A recent cloud task-scheduling algorithm based on the symbiotic organisms search (SOS) algorithm not only has fewer specific parameters, but also incurs time complexity. SOS is a newly developed metaheuristic optimization technique for solving numerical optimization problems. In this paper, the basic SOS algorithm is reduced, and chaotic local search (CLS) is integrated into the reduced SOS to improve the convergence rate. Simulated annealing (SA) is also added to help the SOS algorithm avoid being trapped in a local minimum. The performance of the proposed SA-CLS-SOS algorithm is evaluated by extensive simulation using the Matlab framework, and is compared with SOS, SA-SOS, and CLS-SOS algorithms. Simulation results show that the improved hybrid SOS performs better than SOS, SA-SOS, and CLS-SOS in terms of convergence speed and makespan.

• Proceedings of the KSME Conference
• /
• 2001.06d
• /
• pp.286-291
• /
• 2001

This paper describes an optimal scheduling for ice slurry systems for energy cost saving. The optimization technique applied in the study is the dynamic programming method, for which the state variable is the storage in the ice storage tank and the control variable is the state of chiller's on-off switching. Though the costs during charge period is included in optimization by taking the average cost of ice per hour for slurry making, the time horizon for the simulation is limited building cooling period because accurate charge rate from the ice maker into the ice storage tank cannot be estimated during the charge period. In the operating simulation after optimizing procedure, energy consumption and operating cost for the optimal control are calculated and compared with them for a conventional control with one case of cooling load profile.

• Industrial Engineering and Management Systems
• /
• v.12 no.1
• /
• pp.30-35
• /
• 2013

Motivated by the emergency scheduling in a transportation network, this paper considers a transportation problem, in which, the truck times and transportation costs are assumed as uncertain variables. To meet the demand in the practical applications, two optimization objectives are considered, one is the total costs and another is the completion times. And then, a multi-objective optimization model is developed according to the situation in applications. Because there are commensurability and conflicting between the two objectives commonly, a solution does not necessarily exist that is best with respective to the two objectives. Therefore, the problem is reduced to a single objective model, which is an uncertain programming with a chance-constrain. After some analysis, its equivalent deterministic form is obtained, which is a nonlinear programming. Based on a stepwise optimization strategy, a solution method is developed to solve the problem. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.61
• /
• pp.105-114
• /
• 2000

Job Shop Problem which consists of the m different machines and n jobs is a NP-hard problem of the combinatorial optimization. Each job consists of a chain of operations, each of which needs to be processed during an uninterrupted time period of a given length on a given machine. Each machine can process at most one operation at a time. The purpose of this paper is to develop the heuristic method to solve large scale scheduling problem using Constraint Satisfaction Problem method and Simulated Annealing. The proposed heuristic method consists of the search algorithm and optimization algorithm. The search algorithm is to find the solution in the solution space using CSP concept such as backtracking and domain reduction. The optimization algorithm is to search the optimal solution using SA. This method is applied to MT06, MT10 and MT20 Job Shop Problem, and compared with other heuristic method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.6
• /
• pp.708-714
• /
• 2018

Islanded operation of a microgrid can ensure the reliable operation of the system when a large accident occurs in the main grid. However, because the generation capability of a microgrid is typically limited, a microgrid operator should take islanded operation risk into account in scheduling its generation resources. To address this problem, in this paper we have proposed two unit commitment formulations based on the islanding scenario that reflect the expected and worst-case values of the islanded operation risk. An optimal resource scheduling strategy is obtained for the microgrid operator by solving these optimization problem, and the effectiveness of the proposed method is investigated by numerical simulations.