• 한국멀티미디어학회논문지
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• 제11권12호
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• pp.1716-1721
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• 2008

Currently, Wireless Networks have some nice characteristics such as multi-hop, multi-channel, multi-radio, etc but these kinds of resources are not fully used. The most difficulty to solve this issue is to solve mixed integer optimization. This paper proposes a method to solve a class of mixed integer optimization for wireless networks by using AMPL modeling language with CPLEX solver. The result of method is scheduling and congestion control in multi-channel multi-radio wireless networks.

• Journal of Electrical Engineering and Technology
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• 제10권5호
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• pp.1940-1949
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• 2015

This paper presents an Enhanced Particle Swarm Optimization (EPSO) to solve short-term hydrothermal scheduling (STHS) problem with non-convex fuel cost function and a variety of operational constraints related to hydro and thermal units. The operators of the conventional PSO are dynamically controlled using exponential functions for better exploration and exploitation of the search space. The overall methodology efficiently regulates the velocity of particles during their flight and results in substantial improvement in the conventional PSO. The effectiveness of the proposed method has been tested for STHS of two standard test generating systems while considering several operational constraints like system power balance constraints, power generation limit constraints, reservoir storage volume limit constraints, water discharge rate limit constraints, water dynamic balance constraints, initial and end reservoir storage volume limit constraints, valve-point loading effect, etc. The application results show that the proposed EPSO method is capable to solve the hard combinatorial constraint optimization problems very efficiently.

• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.235-245
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• 1998

This paper presents the fuzzy optimization model based scheduling methodology for the efficient production control of a FFS(FIexible Flow Shop) under the uncertain production environment. To develop the methodology, a fuzzy optimization technique is introduced in which the uncertain production capacity caused by the random events like the machine breakdowns or the absence of workers is modeled by fuzzy number. Since the problem is NP hard, the goal of this study is to obtain the near optimal but practical schedule in an efficient way. Thus, Lagrangian relaxation method is used to decompose the problem into a set of subproblems which are easier to solve than the original one. Also, to construct the feasible schedule, a heuristic algorithm was proposed. To evaluate the performance of the proposed method, computational experiments, based on the real factory data, are performed. Then, the results are compared with those of the other methods, the deterministic one and the existing one used in the factory, in the various performance indices. The comparison results demonstrate that the proposed method is more effective than the other methods.

• 한국경영과학회지
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• 제26권2호
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• pp.59-68
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• 2001

The job shop scheduling problem is not only NP-hard, but is one of the well known hardest combinatorial optimization problems. The goal of this research is to develop an efficient scheduling method based on hybrid genetic algorithm to address job shop scheduling problem. In this scheduling method, generating method of initial population, new genetic operator, selection method are developed. The scheduling method based on genetic algorithm are tested on standard benchmark job shop scheduling problem. The results were compared with another genetic algorithm0-based scheduling method. Compared to traditional genetic, algorithm, the proposed approach yields significant improvement at a solution.

• 대한안전경영과학회지
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• 제25권2호
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• pp.1-8
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• 2023

This paper is proposing a novel machine scheduling model for the unrelated parallel machine scheduling problem without setup times to minimize the total completion time, also known as "makespan". This problem is a NP-complete problem, and to date, most approaches for real-life situations are based on the operator's experience or simple heuristics. The new model based on the Memetic Algorithm, which was proposed by P. Moscato in 1989, is a hybrid algorithm that includes genetic algorithm and local search optimization. The new model is tested on randomly generated datasets, and is compared to optimal solution, and four scheduling models; three rule-based heuristic algorithms, and a genetic algorithm based scheduling model from literature; the test results show that the new model performed better than scheduling models from literature.

• 산업경영시스템학회지
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• 제23권59호
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• pp.37-42
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• 2000

The parallel machines scheduling problems is one of the combinatorial optimization problems that often occurs in the real world. This problem is classified into two cases, one of which is the case which processing time are identical and the other, nonidentical. Not so much researches have been made on the case that nonidentical parallel machines scheduling problem. This study proposes Tabu Search methods for solving parallel machines scheduling problems related to due dates: minimizing mean tardiness, minimizing the number of tardy jobs, minimizing the maximum tardiness.

• 산업기술연구
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• 제26권A호
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• pp.173-180
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• 2006

Scheduling for shipbuilding processes has many alternative solutions since it has long time horizon and handles many jobs. In this paper, an shipbuilding scheduling system for outfitting and painting is presented based on constraint satisfaction techniques(CST). We provide a field based model and an efficient solution methodology by using ILOG Scheduler and Solver. ILOG Solver support extensive optimization goals easily. We do verify the improvement and efficiency of the schedule generated by the scheduling system presented in this paper.

• 산업융합연구
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• 제1권2호
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• pp.69-79
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• 2003

In order to solve scheduling, many techniques including optimization and heuristics methods have been proposed. However, these conventional approaches are usually inadequate to obtain a satisfactory solution because of a NP-Complete which requires extensive computation effort. The aim of this paper is to develop an expert system for job shop scheduling using an AI technique. Through this system, the user can choose one of several performances. If one performance is chosen, then the system solves a scheduling in order to satisfy that performance. Then, this system decides urgent job. This system can utilize production resources efficiently and minimize work-in-process inventories and tardy jobs.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.273-276
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• 2006

At an integrated steel mill, various raw material such as coal and iron ore are unloaded from a large ship. The unloaded raw material is then transported to storage yards through a complex belt conveyer network. We propose an optimization model for scheduling the unloading operations under the limitations of available berths, unloading equipments and transportation capacity of the belt conveyer network. We show that the problem is NP-Hard and propose a heuristic approach to the problem.

• 경영과학
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• 제32권4호
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• pp.69-80
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• 2015

The semiconductor industry is one of those in which the most intricate processes are involved and there are many critical factors that are controlled with precision in those processes. Naturally production scheduling in the semiconductor industry is also very complex and studied by the industry and academia for many years; however, still there are many issues left unclear in the problem. This paper proposes an multi-objective optimization-based scheduling method for semiconductor fabrication(fab). Two main objectives are throughput maximization and meeting target production quantities. The first objective aims to reduce production cost, especially the fixed cost incurred by a large investment constructing a new fab facility. The other is meeting customer orders on time and also helps a fab maintain stable throughput through controlled WIP balancing in the long run. The paper shows a trade-off structure between the two objectives through experimental studies, which provides industrial practitioners with useful references.