• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.286-289
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• 1989

The purpose of this paper is to design a job scheduling algorithm utilizing intelligent control technique. Rulebase is built through the evaluation of rule-set scheduling. 24 scheduling rule-sets and meta-rules are employed. An appropriate scheduling rule-set is selected based on this rulebase and current manufacturing system status. Six criteria have been used to evaluate the performance of scheduling. The performance of sheduling is dependent on random breakdown of the major FMS components during simulation.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.1
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• pp.87-96
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• 2001

This paper considers the transfer batch scheduling problem for a two-machine flow shop with setup times consisting of attached setup time and separate setup time. The attached setup is attached to the processing and can be scheduled in anticipation of arriving work. Two cases of the separate setup are addressed. One is the case that the separate setup can be done only when a machine is idle. The other is the case that the separate setup can be done even though the machine is idle. the other is the case that the separate setup can be done even though the machine is operating. A scheduling rule similar to Johnson\`s rule is suggested to minimize makespan. the scheduling rule developed can be applied to most of the two-machine flow shop scheduling problems if some parameters are adjusted. A numerical example is presented to illustrate the scheduling rule.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.47
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• pp.35-45
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• 1998

Scheduling problem in Flexible Manufacturing Systems(FMS) is complex because of various situation of Manufacturing Systems. Especially, in case of short-term scheduling demanding high efficiency, low cost at short-period, efficient scheduling is a serious problem. To solve this problem, many dispatching rules are developed. But, it leave much to be desired, because real situation in shop floor is complex and real-time scheduling is needed in real manufacturing shop floor. In this paper, search algorithm that allocate different dispatching rules to each machine is presented to complement lack of dispatching rule and develop practical real-time scheduling system. The search algorithm is described in detail. First, algorithm detect machine breakdown, evaluate each dispatching rule. dispatching rules for each machine meeting performance criteria are ranked. The algorithm selects new dispatching nile for bottleneck machine. The effectivenes and efficiency of the mixed dispatching rule and search algorithm is demonstrated.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.1057-1058
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• 2009

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1564-1569
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• 1991

In this paper we describe Flexible Manufacturing Systems (FMS) using Petri nets, since Petri nets provide a powerful tool for modeling dynamical behavior of discrete concurrent processes. We deal with off-Line and on-Line rule-based scheduling of FMS. The role of the rule-base is to generate appropriate priority rule for resolving conflicts, that is, for selecting one of enabled transitions to be fired in a conflict set of the Petri nets. This corresponds to select a part type to be processed in the FMS. Towards developing more Intelligent Manufacturing Systems (IMS) we propose a conceptual framework of a futuristic intelligent scheduling system.

• IE interfaces
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• v.11 no.1
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• pp.119-129
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• 1998

The relative importance of Logistics in steelworks industry is rather higher among other business activities. The objective of the paper is to propose the methodology for real-time vehicle scheduling for scrap disposal in the steelworks industry. Currently, the rule necessary to assign vehicles to a specific job is strictly fixed. The paper adopts the multi-pass rule selector (MPRS) that suggests a promising rule used for vehicle dispatching for a period of time. The MPRS is regularly invoked if necessary and then evaluates a set of rule candidates to select the best rule with respect to the system performance criteria. The experiment shows that the proposed approach outperforms the current single-pass strategy.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.97-109
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• 2002

Manufacturing environments in the real world are subject to many sources of change and uncertainty, such as new job releases, job cancellations, a chance in the processing time or start time of some operation. Thus, the realistic scheduling method should Properly reflect these dynamic environment. Based on the release times of jobs, JSSP (Job Shoe Scheduling Problem) can be classified as static and dynamic scheduling problem. In this research, we mainly consider the dynamic JSSP with continually arriving jobs. The goal of this research is to develop an efficient scheduling method based on GA (Genetic Algorithm) to address dynamic JSSP. we designed scheduling method based on SGA (Sing1e Genetic Algorithm) and PGA (Parallel Genetic Algorithm) The scheduling method based on GA is extended to address dynamic JSSP. Then, This algorithms are tested for scheduling and rescheduling in dynamic JSSP. The results is compared with dispatching rule. In comparison to dispatching rule, the GA approach produces better scheduling performance.

• Korean Management Science Review
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• v.13 no.3
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• pp.173-186
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• 1996

This paper presents an approach for classifying scheduling problems and selecting a heuristic rule to yield best solution in terms of certain performance measure(s). Classification parameters are employed from previous studies on job shop scheduling and project scheduling. Neural network is used for learning and estimating the performance of heuristic rules. In addition, multi-criteria decision making techniques are employed to combine the preferences for each performance measure and heuristic rule for the problems with multi-objectives.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.3
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• pp.322-333
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• 2012

AESA radar is able to instantaneously and adaptively position and control the beam, and such adaptive beam pointing of AESA radar enables to remarkably improve the multi-mission capability. For this reason, Radar Resource Management(RRM) becomes new challenging issue. RRM is a technique efficiently allocating finite resources, such as energy and time to each task in an optimal and intelligent way. Especially radar beam scheduling is the most critical component for the success of RRM. In this paper, we proposed a rule-based scheduling algorithm and Simulated Annealing(SA) based scheduling algorithm, which are alternatively selected and applied to beam scheduler according radar load status in real-time. The performance of the proposed algorithm was evaluated on the multi-function radar scenario. As a result, we showed that our proposed algorithm can process a lot of beams at the right time with real time capability, compared with applying only rule-based scheduling algorithm. Additionally, we showed that the proposed algorithm can save scheduling time remarkably, compared with applying only SA-based scheduling algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.197-213
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• 1997

It is not easy to find a good production schedule which can be used in practice. Therefore, production scheduling simulation with a simple dispatching rule or a set of dispatching rules is used. However, a simple dispatching rule may not create a robust schedule, for the same rule is blindly applied to all internal production processes. The presumption is that there might be a specific combination of appropriate rules that can improve the efficiency of a total production system for a certain type of orders. In order to acquire a better set of dispatching rules, simulation is used to examine the performance of various combinations of dispatching rule sets. There are innumerable combination of rule sets. Hence it takes too much computer simulation time to find a robust set of dispatching rule for a specific production system. Therefore, we propose a concept of the knowledge based simulation to circumvent the problem. The knowledge based simulation consists of knowledge bases, an inference engine and a simulator. The knowledge base is made of rule sets that is extracted from both simulation and human intuition obtained by the simulation studies. For a certain type of orders, the proposed system provides several sets of dispatching rules that are expected to generate better results. Then the scheduler tries to find the best by simulating all proposed set of rules with the simulator. The knowledge-based simulator armed with the acquired knowledge has produced improved solutions in terms of time and scheduling performance.