• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.124-132
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• 2019

Scheduling problems can be classified into offline and online ones. This paper considers an online scheduling problem to minimize makespan on the identical parallel machines. For dynamically arrived jobs with their ready times, we show that the sequencing order according to the ERD (Earliest Ready Date) rule is optimal to minimize makespan. This paper suggests an algorithm by using the MIP(Mixed Integer Programming) formulation periodically to find a good periodic schedule and evaluates the required computational time and resulted makespan of the algorithm. The comparition with an offline scheduling shows our algorithm makes the schedule very fast and the makespan can be reduced as the period time reduction, so we can conclude that our algorithm is useful for scheduling the jobs under online environment even though the number of jobs and machines is large. We expect that the algorithm is invaluable one to find good schedules for the smart factory and online scheduler using the blockchain mechanism.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.25-29
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• 2001

There are so many researches about the search method for the most compatible dispatching rule to a manufacturing system state. Most of researches select the dispatching rule using simulation results. This paper touches upon two research topics: the clustering method for manufacturing system states using simulation, and the search method for the most compatible dispatching rule to a manufacturing system state. The manufacturing system state variables are given to ART II neural network as input. The ART II neural network is trained to cluster the system state. After being trained, the ART II neural network classifies any system state as one state of some clustered states. The simulation results using clustered system state information and those of various dispatching rules are compared and the most compatible dispatching rule to the system state is defined. Finally there are made two knowledge bases. The simulation experiments are given to compare the proposed methods with other scheduling methods. The result shows the superiority of the proposed knowledge base.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.1-22
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• 2005

This paper presents new scheduling heuristics, namely Mean Progressive Weighted Tardiness Estimator (MPWT) Heuristic Method and modified priority rules with sequence-dependent setup times consideration. These are designed to solve job shop scheduling problems with new performance measures - progressive weighted tardiness penalties. More realistic constraints, which are inter-machine overlapping sequence-dependent setup times, are considered. In real production environments, inter-machine overlapping sequence-dependent setups are significant. Therefore, modified scheduling generation algorithms of active and nondelay schedules for job shop problems with inter-machine overlapping sequence-dependent setup times are proposed in this paper. In addition, new customer-based measures of performance, which are total earliness and progressive weighted tardiness, and total progressive weighted tardiness, are proposed. The objective of the first experiment is to compare the proposed priority rules with the consideration of sequence-dependent setup times and the standard priority rules without setup times consideration. The results indicate that the proposed priority rules with setup times consideration are superior to the standard priority rules without the consideration of setup times. From the second experiment and the third experiment to compare the proposed MPWT heuristic approach with the efficient priority rules with setup times consideration, the MPWT heuristic method is significantly superior to the Batched Apparent Tardiness Cost with Sequence-dependent Setups (BATCS) rule, and other priority rules based on total earliness and progressive weighted tardiness, and total earliness and tardiness.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2725-2730
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• 2003

In this study we present a vibration controller for articulated robots that has flexible joints modeled as a 2-mass system. Most of articulated robots have time varying load inertias for each axis according to its motion. Moreover, the inertias vary drastically; for the base axis of articulated robots it may vary about 10 times of its minimum value. But, for industrial robots and many mechatronic devices, it is desirable to maintain control performance in spite of load inertia variation. So we propose a control gain adjustment rule considering the time-varying nature of load inertia. In this gain-adjusting algorithm, the pole locations are in proportion to the anti-resonance frequency of the 2-mass system. The simulation and experimental results show uniform properties in overshoot in spite of the variation of load.

• IE interfaces
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• v.22 no.1
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• pp.10-16
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• 2009

In this paper, we propose an efficient search algorithm for finding an optimal schedule to minimize makespan, while avoiding deadlock situation in Flexible Manufacturing Systems (FMS) with finite capacity, in which each job needs to be processed in several job stages for completion. The proposed algorithm uses a modeling and control method based on Petri-net. Especially, we improve the efficiency of the search algorithm by using a priority rule and an efficient bounding function during the search procedure. The performance of the proposed algorithm is evaluated through a numerical experiment, showing that it holds considerable promise for providing an optimal solution efficiently comparing to past work.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.517-522
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• 2005

This paper addresses scheduling heuristics for an assembly job-shop that includes at least an assembly process throughout its processes. The assembly job shop has certain characteristics not only considering the precedence relationship between the processes but also considering the processing progress between the parts. In addition, it probably presents a different processing time for the same product according to the order of processes and the point of workable time, due to the difference in the availability of equipments. The paper proposes several priority-based dispatching rules that consider these characteristics of the assembly job-shop, aiming to minimize the total tardiness of products in the shop floor. Computational tests showed that job due date based priority rules significantly outperform existing priority rules in terms of total tardiness.

• IE interfaces
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• v.15 no.4
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• pp.338-348
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• 2002

In this paper, we present a restricted tabu search(RTS) algorithm that schedules jobs on identical parallel machines in order to minimize the maximum lateness of jobs. Jobs have release times and due dates. Also, sequence-dependent setup times exist between jobs. The RTS algorithm consists of two main parts. The first part is the MATCS(Modified Apparent Tardiness Cost with Setups) rule that provides an efficient initial schedule for the RTS. The second part is a search heuristic that employs a restricted neighborhood generation scheme with the elimination of non-efficient job moves in finding the best neighborhood schedule. The search heuristic reduces the tabu search effort greatly while obtaining the final schedules of good quality. The experimental results show that the proposed algorithm gives better solutions quickly than the existing heuristic algorithms such as the RHP(Rolling Horizon Procedure) heuristic, the basic tabu search, and simulated annealing.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.307-309
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• 1996

This paper analyses a deterministic scheduling problem concerned with manufacturing two types of components at a pre-assembly stage which consists of two independent feeding machines each producing its own type of component. Each type represents a unique component which may have variations in its size or quality. Therefore, the completion time of each component depends on both its type and quality (size) variations. Such manufactured components are subsequently assembled into various component dependent products. The problem has the objective measure of minimizing the total weighted completion time of a finite number of jobs(products) where the completion time of each job is measured by the latest completion time of its two components at the pre-assembly stage. The problem is shown to be NP-complete in the strong sense. A WSPT rule coupled with a machine-aggregation idea is developed for good heuristics which show the error bound of 2.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.138-141
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• 2006

This paper considers a two-machine flow-shop scheduling problem for minimizing mean flow time. Each job has three non-preemptive operations, where the first and third operations must be Processed on the first and second machines, respectively, but the second operation can be processed on either machine. A lower bound based on SPT rule is derived, which is then used to develop a branch-and-bound algorithm. Also, an efficient simple heuristic algorithm is developed to generate a near-optimal schedule. Numerical experiments are performed to evaluate the performances of the proposed branch-and-bound and the heuristic algorithm

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.825-827
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• 1989

A heuristic model which determines the scheduling of serial flowshops with minimization of the makespan is proposed for an idealized batch chemical plant. It generates an initial sequence by heuristic reasoning and improves it recursively until no improvement is possible. The heuristic reasoning is based on Johnson's Rule which gives the sequence with the minimum makespan for a two-unit flowshop. The evolutionary step searches the neighborhood of the current sequence for sequences with lower makespan. The robustness of this model is also examined by comparing the minimum makespan of literature examples with the theoretical one.