• Korean Management Science Review
• /
• v.8 no.1
• /
• pp.91-108
• /
• 1991

This paper presents an efficient optimization algorithm for the crude oil tanker scheduling problem. The algorithm consists of two stages. In stage one, all the potentially optimal schedules (called 'candidate schedules') are generated from feasible schedules for each ship. In the second stage, a multiple ship scheduling problem is formulated as 0-1 integer programming problem considering only the those candidate schedules. The efficiency of the suggested algorithm was improved by exploiting the special structure of the formulation. The algorithm was illustrated by a numerical example and tested on practical ship scheduling problems.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.38 no.1
• /
• pp.48-57
• /
• 2010

A comparison of two kinds of scheduling optimization algorithms is presented in this paper. As satellite control and operation techniques have been developed, satellite missions became more complicated and overall quantity of missions also increased. These changes require more specific consideration and a huge amount of computation for the satellite mission scheduling. Therefore, it is a good strategy to make a scheduling optimization algorithm for the efficient satellite mission scheduling operation. In this paper, two kinds of scheduling optimization algorithms are designed with tabu-search algorithm and genetic algorithm respectively. These algorithms are applied for the same mission scenario and the results of each algorithm are compared and analyzed.

• KIEE International Transactions on Power Engineering
• /
• v.5A no.4
• /
• pp.331-338
• /
• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.8
• /
• pp.1382-1389
• /
• 2007

This paper presents a new approach for solving the problem of maintenance scheduling of generating units using a binary particle swarm optimization (BPSO). In this paper, we find the optimal solution of the maintenance scheduling of generating units within a specific time horizon using a binary particle swarm optimization algorithm, which is the discrete version of a conventional particle swarm optimization. It is shown that the BPSO method proposed in this paper is effective in obtaining feasible solutions in the maintenance scheduling of generating unit. IEEE reliability test systems(1996) including 32-generators are selected as a sample system for the application of the proposed algorithm. From the result, we can conclude that the BPSO can find the optimal solution of the maintenance scheduling of the generating unit with the desirable degree of accuracy and computation time, compared to other heuristic search algorithm such as genetic algorithms. It is also envisaged that BPSO can be easily implemented for similar optimizations and scheduling problems in power system problems to obtain better solutions and improve convergence performance.

• Journal of Korean Society for Quality Management
• /
• v.36 no.2
• /
• pp.28-35
• /
• 2008

This study deals with an Excel-based scheduling system for a small and medium sized manufacturing factory without sufficient capability for managing full-scale information systems. The factory has the bottleneck with identical machines and unique batching characteristics. The scheduling problem is formulated as a variation of the parallel-machine scheduling system. It can be solved by a two-phase method: the first phase with an ant colony optimization (ACO) heuristic for order grouping and the second phase with a mixed integer programming (MIP) algorithm for scheduling groups on machines.

• Journal of Communications and Networks
• /
• v.12 no.4
• /
• pp.346-357
• /
• 2010

Opportunistic scheduling provides the capability of resource management in wireless networks by taking advantage of multiuser diversity and by allowing delay variation in delivering data packets. It generally aims to maximize system throughput or guarantee fairness and quality of service (QoS) requirements. In this paper, we develop an extended proportional fair (PF) scheduling policy that can statistically guarantee three kinds of QoS. The scheduling policy is derived by solving the optimization problems in an ideal system according to QoS constraints. We prove that the practical version of the scheduling policy is optimal in opportunistic scheduling systems. As each scheduling policy has some parameters, we also consider practical parameter adaptation algorithms that require low implementation complexity and show their convergences mathematically. Through simulations, we confirm that our proposed schedulers show good fairness performance in addition to guaranteeing each user's QoS requirements.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.10a
• /
• pp.527-530
• /
• 2004

This paper presents an optimization formulation for resource scheduling in Critical Resource Diagramming (CRD) of production scheduling networks. A CRD network schedules units of resources against points of needs in a production network rather than the conventional approach of scheduling tasks against resource availability. This resource scheduling approach provides more effective tracking of utilization of production resources as they are assigned or 'moved' from one point of need to another. Using CRD, criticality indices can be developed for resource types in a way similar to the criticality of activities in Critical Path Method (CPM). In our supply chain model, upstreams may choose either normal operation or expedited operation in resource scheduling. Their decisions affect downstream's resource scheduling. The suggested optimization formulation models resources as CRD elements in a production two-stage supply to minimize the total operation cost

• Journal of Computational Design and Engineering
• /
• v.3 no.4
• /
• pp.295-311
• /
• 2016

This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.4
• /
• pp.1348-1356
• /
• 2017

Integration of wind generators with the conventional power plants will raise operational challenges to the electric power utilities due to the uncertainty of wind availability. Thus, the Generation Scheduling (GS) among the online generating units has become crucial. This process can be formulated mathematically as an optimization problem. The GS problem of wind integrated power system is inherently complex because the formulation involves non-linear operational characteristics of generating units, system and operational constraints. As the robust tool is viable to address the chosen problem, the modern bio-inspired algorithm namely, Grey Wolf Optimization (GWO) algorithm is chosen as the main optimization tool. The intended algorithm is implemented on the standard test systems and the attained numerical results are compared with the earlier reports. The comparison clearly indicates the intended tool is robust and a promising alternative for solving GS problems.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.10a
• /
• pp.37-42
• /
• 1997

Off-line feed rate scheduling is an advanced methodology to automatically determine optimum feed rates for the optimization of NC code. However, the present feed rate scheduling systems have lim~tations to generate the optimized NC codes because they use the material removal rate or non-generalized cutting force model. In this paper, a feed rate scheduling system based on an improved cutting force model that can predrct cutting forces exactly in general machining was presented. Original blocks of NC code were divided to small ones with the modified feed rates to adjust the peak value of cutting forces to a constant vale. The characteristic of acceleration and deceleration for a given machrne tool was considered when off-line feed rate scheduhng was performed. Software for the NC code optimization was developed and applied to pocket machining simulation.