• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.59
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• pp.37-42
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• 2000

The parallel machines scheduling problems is one of the combinatorial optimization problems that often occurs in the real world. This problem is classified into two cases, one of which is the case which processing time are identical and the other, nonidentical. Not so much researches have been made on the case that nonidentical parallel machines scheduling problem. This study proposes Tabu Search methods for solving parallel machines scheduling problems related to due dates: minimizing mean tardiness, minimizing the number of tardy jobs, minimizing the maximum tardiness.

• 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 of Industrial and Systems Engineering
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• v.38 no.2
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• pp.47-55
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• 2015

This paper deals with rescheduling on unrelated parallel-machines with compressible processing times, assuming that the arrival of a set of new jobs triggers rescheduling. It formulates this rescheduling problem as an assignment problem with a side constraint and proposes a heuristic to solve it. Computational tests evaluate the efficacy of the heuristic.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.876-879
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• 1995

Parallel NC machines are a new generation of machine tools aimed at increasing maching accuracy and reducing part cycle time. In addition to their capacity to perform both milling and turning operations, these machine tools can perform multiple machining operations simultaneously,involving one or more parts at a time, and can completely finish a part in a single setup. Due to the lack of a computer-aided process planning system, these machines are used in industry today as dedicated, mass-production machines. This pape presents methodology for generating optimal process plan for each parallel machine tool using a genetic algorithm.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.471-474
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• 2011

In parallel job scheduling, each job can be executed simultaneously on multiple machines at a time. Thus in the input instance, a job $J_i$ requires the number $m_i$ of machines on which it shall be processed. The algorithm should determine not only the execution order of jobs but also the machines on which the jobs are executed. In this paper, when the jobs have deadlines, the problem is to maximize the total work of jobs which is completed by their deadlines. The problem is known to be strongly NP-hard [5] and we investigate the approximation algorithms for the problem. We consider a model in which the algorithm can have more machines than the adversary. With this advantage, the problem is how good solution the algorithm can produce against the optimal algorithm.

• Management Science and Financial Engineering
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• v.13 no.2
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• pp.47-77
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• 2007

This paper considers a scheduling problem where a customer orders multiple products(jobs) from a production facility. The objective is to minimize the sum of the order(batch) completion times. While a machine can process only one job at a time, multiple machines can simultaneously process jobs in a batch. Although each job has a unique processing time, we consider the case where batch processing times are identical. This simplification allows us to develop heuristics with improved performance bounds. This problem was motivated by a real world problem encountered by foreign electronics manufacturers. We first establish the complexity of the problem. For the two parallel machine case, we introduce two simple but intuitive heuristics, and find their worst case relative error bounds. One bound is tight and the other bound goes to 1 as the number of orders goes to infinity. However, neither heuristic is superior for all instances. We extend one of the heuristics to an arbitrary number of parallel machines. For a fixed number of parallel machines, we find a worst case bound which goes to 1 as the number of orders goes to infinity. Then, a tighter bound is found for the three parallel machine case. Finally, the heuristics are empirically evaluated.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.7-12
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• 2006

This study considers the problem of scheduling jobs on uniform parallel machines with a common due date. The objective is to minimize the total absolute deviation of job completion times about the common due date. This problem is motivated by the fact that a certain phase of printed circuit board manufacturing is bottleneck and the processing speeds of parallel machines in this phase are uniformly different for all jobs. Optimal properties are proved and a simple polynomial time optimal algorithm is developed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.2
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• pp.35-41
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• 2003

This paper proposes a heuristic scheduling algorithm to satisfy the customer's due date in the production process under make to order environment. The goal is to achieve the machine scheduling in the transformer winding process, in which consists of parallel machines with different machine performances. The winding is important production process in the transformer manufacturing company. The efficiency of the winding machines is different according to the voltage capacity and the winding type. This paper introduces a heuristic approach in the transformer winding process where the objective function is to minimize the total tardiness of jobs over due dates. The numerical experiment is illustrated to evaluate the performance.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.54 no.12
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• pp.576-585
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• 2005

This paper deals with the electromagnetic field analysis of slotless brushless permanent magnet machines with three different magnetization patterns such as Halbach, parallel and radial magnetization. The magnetization modeling of Halbach, parallel and radial magnetization is performed analytically. And then, analytical solutions for open-circuit field distributions, armature reaction field distributions, flux linkages due to PMs and stator windings, torque, back-emf and inductance are derived in terms of magnetic vector potential and two-dimensional (2-d) polar coordinate systems. The analytical results are validated extensively by finite element (FE) analyses. The magnet volume required in order to produce identical flux density is compared with each magnetization. Finally, analytical solutions and derivation procedures of those presented in this paper can be applied to slotless and slotted brushless permanent magnet AC and DC machines.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1B
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• pp.27-32
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• 2003

This paper presents the Genetic Algorithm based Task Scheduling (GATS) method for the scheduling of programs with diverse embedded parallelism types in Distributed Parallel Systems, which consist of a set of loosely coupled parallel and vector machines connected via high speed networks The distributed parallel processing tries to solve computationally intensive problems that have several types of parallelism, on a suite of high performance and parallel machines in a manner that best utilizes the capabilities of each machine. When scheduling in distributed parallel systems, the matching of the parallelism characteristics between tasks and parallel machines rather than load balancing should be carefully handled with the minimization of communication cost in order to obtain more speedup. This paper proposes the based initialization methods for an initial population and the knowledge-based mutation methods to accommodate the parallelism type matching in genetic algorithms.