• 산업경영시스템학회지
• /
• 제13권21호
• /
• pp.91-100
• /
• 1990

This paper is concerned with the development of optimal algorithms for multi-stage flowshop scheduling problems with sequence dependent setup times. In the previous researches the setup time of a job is considered to be able to begin at the earliest opportunity given a particular sequence at the start of operations. In this paper the setup time of a job is considered to be able to begin only at the completion of that job on the previous machine to reflect the effects of the setup time to the performance measure of sequence dependent setup time flowshop scheduling. The results of the study consist of two areas; first, a general integer programming(IP) model is formulated and a nixed integer linear programming(MILP) model is also formulated by introducing a new binary variable. Second a depth-first branch and bound algorithm is developed. To reduce the computational burdens we use the best heuristic schedule developed by Choi(1989) as the first trial. The experiments for developed algorithm are designed for a 4$\times$3$\times$3 factorial design with 360 observations. The experimental factors are PS(ratio of processing time to setup time), M(number of machines), N(number of jobs).

• 한국컴퓨터정보학회논문지
• /
• 제18권8호
• /
• pp.9-15
• /
• 2013

클라우드 컴퓨팅을 구성하는 자원은 이질적인 자원으로 구성되므로 다양한 요구사항의 작업을 이질적인 자원에 할당하는 것은 중요한 문제이다. 이는 다수의 판단 기준하에 다수의 선택 문제로 정의할 수 있는데 본 연구에서는 AHP (Analytic hierarchy process) 기법을 통해 이 문제를 해결하고자 한다. 본 연구에서는 작업의 특성을 고려한 우선순위기반 스케줄링 기법을 제안하는데 1단계에서는 작업의 중요도에 의해 우선순위를 부여하고 2단계에서는 응답시간, 시스템 이용율, 그리고 비용을 판단기준으로 하여 AHP알고리즘에 의해 최적의 자원에 작업을 할당한다. 제안하는 알고리즘의 분석과 실험을 통해 제안 기법의 효율성을 제시하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권8호
• /
• pp.2993-3010
• /
• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
• /
• pp.183-187
• /
• 2000

In this study, we design a scheduling system for painting shop, called HYPOS(Hyundai heavy industry Painting shop Operating System) and develop a system implementing the design. The painting shop operations are currently scheduled manually by experts. Manual scheduling is a serious time consuming job and generally can not guarantee a full optimality. Building a traditional heuristic scheduling system lot this problem, however, is not promising because there are various kinds of constraints to be satisfied including space allocation of shipbuilding blocks in a painting cell. We, therefore, adopt a spatial scheduling approach and develop scheduling algorithms based on field-oriented scheduling heuristics and computational geometry. And we show that the algorithms can successfully be applied to the painting shop scheduling problem.

• 대한기계학회논문집A
• /
• 제29권11호
• /
• pp.1463-1471
• /
• 2005

This paper proposes an agent-based scheduling method fur semiconductor wafer fabrication facilities with hard inter-operation temporal constraints. The scheduling problem is to find the feasible schedules that guarantee both logical and temporal correctness. A proposed multi-agent based architecture is composed of scheduling agents, workcell agents, and machine agents. A scheduling agent computes optimal schedules through bidding mechanisms with a subset or entire set of the workcell agents. A dynamic planning-based approach is adopted for the scheduling mechanism so that the dynamic behaviors such as aperiodic job arrivals and reconfiguration can be taken into consideration.

• 한국IT서비스학회지
• /
• 제16권3호
• /
• pp.103-111
• /
• 2017

The real time scheduling is a key research area in high performance computing and has been a source of challenging problems. A periodic task is an infinite sequence of task instance where each job of a task comes in a regular period. The RMS (Rate Monotonic Scheduling) algorithm has the advantage of a strong theoretical foundation and holds out the promise of reducing the need for exhaustive testing of the scheduling. Many real-time systems built in the past based their scheduling on the Cyclic Executive Model because it produces predictable schedules which facilitate exhaustive testing. In this work we propose hybrid scheduling method which combines features of both of these scheduling algorithms. The original rate monotonic scheduling algorithm didn't consider the uniform sampling tasks in the real time systems. We have enumerated some issues when the RMS is applied to our hybrid scheduling method. We found the scheduling bound for the hard real-time systems which include the uniform sampling tasks. The suggested hybrid scheduling algorithm turns out to have some advantages from the point of view of the real time system designer, and is particularly useful in the context of large critical systems. Our algorithm can be useful for real time system designer who must guarantee the hard real time tasks.

• Industrial Engineering and Management Systems
• /
• 제11권4호
• /
• pp.310-330
• /
• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• Journal of Platform Technology
• /
• 제9권4호
• /
• pp.51-59
• /
• 2021

본 논문은 사전에 위치가 알려져 있는 시설물에 설치된 Wi-Fi AP에 접속한 기록을 이용하여 결정한 사용자의 위치 정보를 이용하여 서비스를 제공하는 방법을 제안한다. 본 논문에서 제안하는 위치 기반 서비스는 작업자의 위치 및 시간 정보를 이용하여 승강기 등 주기적 점검 및 유지보수가 필요한 시설물을 대상으로 현장 작업자의 작업 진행 상황을 관리하는 것이다. 본 논문에서 제안하는 작업 스케줄 관리 방법은 작업을 위해 작업자가 시설물에 도착해야 하는 사전 스케줄된 시간과 실제 그 시설물에 도착한 시간을 기반으로 한다. 또, Wi-Fi AP 접속 정보를 기반으로 한 위치 결정 및 시간 정보를 기반으로 한 작업스케줄 관리의 가능성을 보이기 위해 본 논문에서는 주기적 점검 및 유지보수가 필요한 승강기 유지보수 분야에서 적용할 수 있는 실험적 구현을 일부 하였다. 실험적으로 구현한 결과는 본 논문에서 제안한 방법이 시설물의 점검 및 유지보수 관련 분야에 활용될 수 있음을 보인다.

• Journal of Platform Technology
• /
• 제10권1호
• /
• pp.11-19
• /
• 2022

본 논문은 사전에 위치가 알려져 있는 시설물에 설치된 Wi-Fi AP에 접속한 기록을 이용하여 결정한 사용자의 위치 정보를 이용하여 서비스를 제공하는 방법을 제안한다. 본 논문에서 제안하는 위치 기반 서비스는 작업자의 위치 및 시간 정보를 이용하여 승강기 등 주기적 점검 및 유지보수가 필요한 시설물을 대상으로 현장 작업자의 작업 진행 상황을 관리하는 것이다. 본 논문에서 제안하는 작업 스케줄 관리 방법은 작업을 위해 작업자가 시설물에 도착해야 하는 사전 스케줄 된 시간과 실제 그 시설물에 도착한 시간을 기반으로 한다. 또, Wi-Fi AP 접속 정보를 기반으로 한 위치 결정 및 시간 정보를 기반으로 한 작업 스케줄 관리의 가능성을 보이기 위해 본 논문에서는 주기적 점검 및 유지보수가 필요한 승강기 유지보수 분야에서 적용할 수 있는 실험적 구현을 일부 하였다. 실험적으로 구현한 결과는 본 논문에서 제안한 방법이 시설물의 점검 및 유지보수 관련 분야에 활용될 수 있음을 보인다.

• 한국경영과학회지
• /
• 제24권3호
• /
• pp.73-82
• /
• 1999

In this paper, we address a single machine non-preemptive n-job scheduling problem to minimize the sum of earliness and tardiness with different release times and due dates. To solve the problem, we propose a genetic algorithm with new crossover and mutation operators to find the job sequencing. For the proposed genetic algorithm, the optimal pair of crossover and mutation rates is investigated. To illustrate the suitability of genetic algorithm, solutions of genetic algorithm are compared with solutions of exhaustive enumeration method in small size problems and tabu search method in large size problems. Computational results demonstrate that the proposed genetic algorithm provides the near-optimal job sequencing in the real world problem.