• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.4
• /
• pp.547-554
• /
• 2016

Analyze the customer daily load patterns, be used to determine the optimal charging and discharging schedule which can minimize the electrical charges through the battery energy storage system(BESS) installed in consumers is an object of this paper. BESS, which analyzes the load characteristics of customer and reduce the peak load, is essential for optimal charging and discharging scheduling to save electricity charges. This thesis proposes optimal charging and discharging scheduling method, using particle swarm optimization (PSO) and penalty function method, of BESS for reducing energy charge. Since PSO is a global optimization algorithm, best charging and discharging scheduling can be found effectively. In addition, penalty function method was combined with PSO in order to handle many constraint conditions. After analysing the load patterns of target BESS, PSO based on penalty function method was applied to get optimal charging and discharging schedule.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.2
• /
• pp.338-346
• /
• 2016

In order for the optimal solution of generators’ annual maintenance scheduling to be applicable to the actual power system it is crucial to incorporate the constraints related to the equivalent operation hours (EOHs) in the optimization model. However, most of the existing researches on the optimal maintenance scheduling are based on the assumption that the maintenances are to be performed periodically regardless of the operation hours. It is mainly because the computation time to calculate EOHs increases exponentially as the number of generators becomes larger. In this paper an efficient algorithm based on demand grouping method is proposed to calculate the approximate EOHs in an acceptable computation time. The method to calculate the approximate EOHs is incorporated into the optimization model for the maintenance scheduling with consideration on the EOHs of generators. The proposed method is successfully applied to the actual Korean power system and shows significant improvement when compared to the result of the maintenance scheduling algorithm without consideration on EOHs.

• ETRI Journal
• /
• v.30 no.1
• /
• pp.129-140
• /
• 2008

In this paper, we propose a novel ant colony optimization (ACO)-based test scheduling method for testing network-on-chip (NoC)-based systems-on-chip (SoCs), on the assumption that the test platform, including specific methods and configurations such as test packet routing, generation, and absorption, is installed. The ACO metaheuristic model, inspired by the ant's foraging behavior, can autonomously find better results by exploring more solution space. The proposed method efficiently combines the rectangle packing method with ACO and improves the scheduling results by dynamically choosing the test-access-mechanism widths for cores and changing the testing orders. The power dissipation and variable test clock mode are also considered. Experimental results using ITC'02 benchmark circuits show that the proposed algorithm can efficiently reduce overall test time. Moreover, the computation time of the algorithm is less than a few seconds in most cases.

• Journal of the Korean Institute of Navigation
• /
• v.24 no.5
• /
• pp.385-395
• /
• 2000

In the transportation literature, many useful decision making models for ship routing and ship scheduling have been studied. But the majority of these studies are on industrial carriers, bulk carriers, or tankers. It is quite recent that a few optimization models have been developed for liner fleet routing and scheduling problems. However there have been few academic studies on decision making models for the routing or scheduling problems of passenger ships in spite of their economic importance in the entire shipping industry. The purpose of this study is to develop analytic decision making models for ship routing and scheduling for the passenger ship fleet. This study gives two optimization models, one is a linear programming model and the other a goal programming model. These two models are solved easy by commercial linear programming softwares and suggest optimal ship routing plans and many other useful implications for passenger ship fleet managers.

• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.4
• /
• pp.214-222
• /
• 2003

Studies on the optimization of machining process can be divided into two different approaches: off-line feedrate scheduling and adaptive control. Each approach possesses its respective strong and weak points compared to each other. That is, each system can be complementary to the other. In this regard, a combined system, which is a feedrate control system fur cutting force optimization, was proposed in this paper to make the best of each approach. Experimental results show that the proposed system could overcome the weak points of the off-line feedrate scheduling system and the adaptive control system. In addition, from the figure, it can be confirmed that the off-line feedrate scheduling technique can improve the machining quality and can fulfill its function in the machine tool which has a adaptive controller.

• Korean Chemical Engineering Research
• /
• v.43 no.5
• /
• pp.571-578
• /
• 2005

We propose a new optimization framework for the reactive scheduling. The proposed rescheduling scheme is specially focused on how to generate rescheduling results when equipment failure occurs. The approach is based on a continuous-time problem representation that takes into account the schedule in progress, the updated information on the batches still to be processed, the present plant state, the deviations in plant parameters and the time data. To update the predictive scheduling, we used right shift rescheduling and total regeneration when equipment failure occurs. And, a practical solution to the rescheduling problem requires satisfaction of two often confliction measures: the efficiency measure that evaluates the satisfaction of a desired objective function value and the stability measure that evaluates the amount of change between the schedules before and after the disruption. In this paper, the efficiency is measured by the makespan of all jobs in the system. And, the stability is measured by the percentage change in makespan and the modified sequence deviation in the predictive scheduling and rescheduling.

• Industrial Engineering and Management Systems
• /
• v.10 no.3
• /
• pp.203-208
• /
• 2011

Job shop scheduling is well-known as one of the hardest combinatorial optimization problems and has been demonstrated to be NP-hard problem. In the past decades, several researchers have devoted their effort to develop evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for job shop scheduling problem. Differential Evolution (DE) algorithm is a more recent evolutionary algorithm which has been widely applied and shown its strength in many application areas. However, the applications of DE on scheduling problems are still limited. This paper proposes a one-stage differential evolution algorithm (1ST-DE) for job shop scheduling problem. The proposed algorithm employs random key representation and permutation of m-job repetition to generate active schedules. The performance of proposed method is evaluated on a set of benchmark problems and compared with results from an existing PSO algorithm. The numerical results demonstrated that the proposed algorithm is able to provide good solutions especially for the large size problems with relatively fast computing time.

• Journal of the Korean Institute of Navigation
• /
• v.18 no.2
• /
• pp.57-67
• /
• 1994

This paper treats the optimization analysis of tactical ship scheduling problems in the world seaborne bulk trade. The authors use the term 'tactial' to describe the ship scheduling problem where the owners should employ skillful tactics as an expedient toward gaining the higher profits per period in short term. Relevent research and related problems on ship scheduling problems are reviewed briefly and a model for the tactical ship scheduling problem formulated as Set Problem is introduced by modifying the previous work of Fisher(1989). The reality and practicability of the model is validated by some ship-ping statistics. Proper solution approaches are outlined in the context of computational tractability in tackling the Mixed Integer Propramming. Some underlying consideration for the computational experiment is also mentioned. The authors conclude the paper with the remarks on the need of user-friendly Decision Support System for ship scheduling under varying decision environment.

• International conference on construction engineering and project management
• /
• 2005.10a
• /
• pp.802-807
• /
• 2005

For linear projects, it has long been known that resource utilization is important in improving work efficiency. However, most existing scheduling techniques cannot satisfy the need for solving such issues. This paper presents an optimization model for solving linear scheduling problems involving resource assignment tasks. The proposed model adopts constraint programming (CP) as the searching algorithm for model formulation, and the proposed model is designed to optimize project total cost. Additionally, the concept of outsourcing resources is introduced here to improve project performance.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.304-310
• /
• 2021

The paper presents problems of optimization of the synthesis of embedded systems, in particular Pareto optimization. The model of such a system for its design for high-level of abstract is based on the classic approach known from the theory of task scheduling, but it is significantly extended, among others, by the characteristics of tasks and resources as well as additional criteria of optimal system in scope structure and operation. The metaheuristic algorithm operating according to this model introduces a new approach to system synthesis, in which parallelism of task scheduling and resources partition is applied. An algorithm based on a genetic approach with simulated annealing and Boltzmann tournaments, avoids local minima and generates optimized solutions. Such a synthesis is based on the implementation of task scheduling, resources identification and partition, allocation of tasks and resources and ultimately on the optimization of the designed system in accordance with the optimization criteria regarding cost of implementation, execution speed of processes and energy consumption by the system during operation. This paper presents examples and results for multi-criteria optimization, based on calculations for specifying non-dominated solutions and indicating a subset of Pareto solutions in the space of all solutions.