• Journal of Korean Institute of Industrial Engineers
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• v.15 no.2
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• pp.11-22
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• 1989

The problem of scheduling n-jobs on m-uniform parallel machines is considered, in which each job has a release time, a deadline, and a processing requirement. The job processing requirements are allocated to the machines so that the maximum of the load differences between time periods is minimized. Based on Federgruen's maximum flow network model to find a feasible schedule, a polynomially bounded algorithm is developed. An example to show the effectiveness of our algorithm is presented.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.1-12
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• 1992

Parallel Genetic Algorithms (GAs) are developed to solve a single machine n-job scheduling problem which is to minimize the sum of absolute deviations of completion times from a common due date. (0, 1) binary scheme is employed to represent the n-job schedule. Two selection methods, best individual selection and simple selection are examined. The effect of crossover operator, due date adjustment mutation and due date adjustment reordering are discussed. The performance of the parallel genetic algorithm is illustrated with some example problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.44-50
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• 2007

This paper deals with the problem of scheduling jobs and rate-modifying activities on parallel machines. A rate-modifying activity is an activity that changes the production rate of equipment such as maintenance and readjustment. If a job is scheduled after the rate-modifying activity, then the processing time varies depending on the modifying rate of the activity. In this study, we extend the single machine problem to parallel machines problem and propose algorithms is to schedule the rate-modifying activities and jobs to minimize the makespan on parallel machines which is NP-hard. We propose a branch and bound algorithm with three lower bounds to solve medium size problems optimally. Also we develop three heuristics, Modified Longest Processing Time, Modified MULTIFIT and Modified COMBINE algorithms to solve large size problems. The test results show that branch and bound algorithm finds the optimal solution in a reasonable time for medium size problems (up to 15 jobs and 5 machines). For large size problem, Modified COMBINE and Modified MULTIFIT algorithms outperform Modified LPT algorithm in terms of solution quality.

• 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.

• ETRI Journal
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• v.45 no.2
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• pp.318-328
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• 2023

Recently, embedded systems, such as mobile platforms, have multiple processing units that can operate in parallel, such as centralized processing units (CPUs) and neural processing units (NPUs). We can use deep-learning compilers to generate machine code optimized for these embedded systems from a deep neural network (DNN). However, the deep-learning compilers proposed so far generate codes that sequentially execute DNN operators on a single processing unit or parallel codes for graphic processing units (GPUs). In this study, we propose PartitionTuner, an operator scheduler for deep-learning compilers that supports multiple heterogeneous PUs including CPUs and NPUs. PartitionTuner can generate an operator-scheduling plan that uses all available PUs simultaneously to minimize overall DNN inference time. Operator scheduling is based on the analysis of DNN architecture and the performance profiles of individual and group operators measured on heterogeneous processing units. By the experiments for seven DNNs, PartitionTuner generates scheduling plans that perform 5.03% better than a static type-based operator-scheduling technique for SqueezeNet. In addition, PartitionTuner outperforms recent profiling-based operator-scheduling techniques for ResNet50, ResNet18, and SqueezeNet by 7.18%, 5.36%, and 2.73%, respectively.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.339-339
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• 1999

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.2
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• pp.47-57
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• 2000

In this paper we consider an n-job non-preemptive and identical parallel machine scheduling problem of minimizing the sum of earliness and tardiness with different release times and due dates. In the real world this problem is more realistic than the problems that release times equal to zero or due dates are common. The problem is proved to be NP-complete. Thus a heuristic is developed to solve this problem To illustrate its suitability a proposed heuristic is compared with a genetic algorithm for a large number of randomly generated test problems. Computational results show the effectiveness and efficiency of proposed heuristic. In summary the proposed heuristic provides good solutions than genetic algorithm when the problem size is large.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.3
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• pp.431-446
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• 1998

In this paper, we consider the problem of scheduling n jobs with sequence-dependent processing times on a set of parallel-identical machines. The processing time of each job consists of a pure processing time and a sequence-dependent setup time. The objective is to maximize the total remaining machine available time which can be used for other tasks. For the problem, we first propose a dynamic programming(DP) algorithm for sequencing jobs processed on a single machine. The algorithm is then extended to handle jobs on parallel-identical machines. Finally, we developed an improved version of the algorithm which generates optimal solutions using much smaller amount of memory space and computing time. Computational results are provided to illustrate the performance of the DP algorithms.

• IE interfaces
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• v.7 no.3
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• pp.171-180
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• 1994

Defined is a Multi-level Parallel Machine Flow-Shop (MPMFS) which reflects some real world manufacturing situations. Just-In-Time (JIT) philosophy is applied to the MPMFS scheduling in order to achieve lowering work-in-process inventory level as well as meeting due dates. A schedule generating simulator is developed. The latest start time of each operation is determined by a backward simulation followed by another forward simulation to analyze the schedule feasibility and actual inventory level. Reasonable schedules are available through adjusting some parameters for allowance factors such as set-up times of machines and other environmental changes. The SLAMSYSTEM under Window is employed for this processing with some input/output data handling processes devised under DOS.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.10a
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• pp.72-72
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• 1999