• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.823-835
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• 2019

Purpose: This study deals with a production planning and scheduling problem to minimize the total weighted tardiness on hybrid flow shop with sets of non-identical parallel machines on stages, where parallel machines in the set are dedicated to perform specific subsets of jobs and sequence-dependent setup times are also considered. Methods: A two-stage approach, that applies MILP model in the 1st stage and dispatching rules in the 2nd stage, is proposed in this paper. The MILP model is used to assign jobs to a specific machine in order to equalize the workload of the machines at each stage, while new dispatching rules are proposed and applied to sequence jobs in the queue at each stage. Results: The proposed two-stage approach was implemented by using a commercial MILP solver and a commercial simulation software and a case study was developed based on the spark plug manufacturing process, which is an automotive component, and verified using the company's actual production history. The computational experiment shows that it can reduce the tardiness when used in conjunction with the dispatching rule. Conclusion: This proposed two-stage approach can be used for HFS systems with dedicated machines, which can be evaluated in terms of tardiness and makespan. The method is expected to be used for the aggregated production planning or shop floor-level production scheduling.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.115-121
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• 2015

In this paper, we proposed an iterative data-flow optimal scheduling algorithm based on genetic algorithm for high-performance multiprocessor. The basic hardware model can be extended to include detailed features of the multiprocessor architecture. This is illustrated by implementing a hardware model that requires routing the data transfers over a communication network with a limited capacity. The scheduling method consists of three layers. In the top layer a genetic algorithm takes care of the optimization. It generates different permutations of operations, that are passed on to the middle layer. The global scheduling makes the main scheduling decisions based on a permutation of operations. Details of the hardware model are not considered in this layer. This is done in the bottom layer by the black-box scheduling. It completes the scheduling of an operation and ensures that the detailed hardware model is obeyed. Both scheduling method can insert cycles in the schedule to ensure that a valid schedule is always found quickly. In order to test the performance of the scheduling method, the results of benchmark of the five filters show that the scheduling method is able to find good quality schedules in reasonable time.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.651-657
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• 1998

In this study, an intelligent scheduling with hybrid architecture, which integrates expert system and neural network, is proposed. Neural network is trained with the data acquired from simulation model of FMC to obtain the knowledge about the relationship between the state of the FMC and its best dispatching rule. Expert system controls the scheduling of FMC by integrating the output of neural network, the states of FMS, and user input. By applying the hybrid system to a scheduling problem, the human knowledge on scheduling and the generation of non-logical knowledge by machine teaming, can be processed in one scheduler. The computer simulation shows that comparing with MST(Minimum Slack Time), there is a little increment in tardness, 5% growth in flow time. And at breakdown, tardness is not increased by expert system comparing with EDD(Earliest Due Date).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.733-750
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• 2019

Software Defined Networking (SDN) is a new network paradigm, allowing administrators to manage networks through central controllers by separating control plane from data plane. So, one or more controllers must locate outside switches. However, this separation may cause delay problems between controllers and switches. In this paper, we therefore propose a Priority-based Scheduling policy for OpenFlow (PSO) to reduce the delay of some significant traffic. Our PSO is based on packet prioritization mechanisms in both OpenFlow switches and controllers. In addition, we have prototyped and experimented on PSO using a network simulator (ns-3). From the experimental results, PSO has demonstrated low delay for targeted traffic in the out-of-brand control network. The targeted traffic can acquire forwarding rules with lower delay under network congestion in control links (with normalized load > 0.8), comparing to traditional OpenFlow. Furthermore, PSO is helpful in the in-band control network to prioritize OpenFlow messages over data packets.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.801-803
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• 2005

Fundamentally, success of the competitive electricity market is dependent on efficient market design. However, since electricity incorporates various physical constraints as other commodities, the resource assignment (i.e., dispatch scheduling) is also one of requisites for the successful operation of electricity market. Therefore, efficient dispatch scheduling is an important issue to succeed in the deregulated electricity market and the efficiency of this electricity market may be considerably increased by systematic studies on dispatch scheduling algorithm and corresponding constraints, especially system security. Moreover, contrary to traditional vertically-integrated electric power industry condition, since various decision-makings in deregulated electricity market are directly connected with market participants' benefits, only rational dispatch scheduling algorithm can convince these participants. Therefore, it can provide a basis of grievance prevention. In this paper, we propose an algorithm for security constrained dispatch scheduling with respect to load curtailment. Proposed algorithm decomposes the dispatch problem into a master problem corresponding to basecase optimal power flow (OPF) and several subproblems corresponding a series of contingencies using two-stage optimization technique.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.39-44
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• 2005

Scheduling for digital system synthesis is assigning each operation in a control/data flow graph(CDFG) to a specific control step without violating precedence relation. It is one of the most important tasks due to its direct influence on the performance of the hardware synthesized. In this paper, we propose a resource-constrained scheduling algorithm. Our algorithm first analyzes the given CDFG to determine the number of functional units of each type, then assigns each operation to a control step while satisfying the constraints. It also tries to improve the solution iteratively by adjusting the number of functional units using the results collected from the previous scheduling. Experiments were performed to test the performance of the proposed algorithm, and results are presented

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.4
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• pp.257-265
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• 2009

This paper considers the hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. In hybrid flow shops, each job is processed through multiple production stages in series, each of which has multiple identical parallel machines. The problem is to determine the allocation of jobs to the parallel machines at each stage as well as the sequence of the jobs assigned to each machine. Due to the complexity of the problem, we suggest search heuristics, tabu search and simulated annealing algorithms with a new method to generate neighborhood solutions. In particular, to evaluate and select neighborhood solutions, three surrogate objectives are additionally suggested because not much difference in the number of tardy jobs can be found among the neighborhoods. To test the performances of the surrogate objective based search heuristics, computational experiments were performed on a number of test instances and the results show that the surrogate objective based search heuristics were better than the original ones. Also, they gave the optimal solutions for most small-size test instances.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.193-206
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• 2005

This paper considers a new flow-shop scheduling problem where a different WIP (work-in-process) state has different weight on the duration time. For the two machine case, the recognition version is NP-Complete in the strong sense. Several special cases are solved by different polynomial time algorithms. Finally, we develop a heuristic and provide an upper-bound on relative error which is tight in limit.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.13-18
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• 1986

This paper is concerned with flow-shop permutation scheduling problem. This paper presents an algorithm for the minimum makespan sequence. The efficiency of proposed algorithm is demonstrated by comparisons with the existing algorithms: Johnson's, branch & bound method, and heuristic algorithms. The proposed algorithm is more effective than the other algorithms. A numerical example is given to illustrate the procedure.

• IE interfaces
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• v.9 no.2
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• pp.75-82
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• 1996

A scheduling system is developed for a parallel machine flow shop which reflects the situations of the manufacturing processes in the tire industry. The schedule is generated via simulation using heuristic rules to get satisfaction on such constraints as due dates, demands, work-in-process, process capacity, set-up times and scheduling speed. This system, constructed with relational DB, SQL and C language, consumes less than 10 minutes of PC simulation to handle real size problems.