• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.235-240
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• 2013

Cloud systems are popular computing environment because they can provide easy access to computing resources for users as well as efficient use of resources for companies. The resources of cloud computing are heterogeneous and jobs have various characteristics. One such issue is effective job scheduling. Scheduling in the cloud system may be defined as a multiple criteria decision model. To address this issue, this paper proposes a priority-based two-step job scheduling algorithm. On the first level, jobs are classified based on preference. Resources are dedicated to a job if a deadline failure would cause severe results or critical business losses. In case of only minor discomfort or slight functional impairment, the job is scheduled using a best effort approach. On the second level, jobs are allocated to adequate resources through their priorities that are calculated by the analytic hierarchic process model. We then analyze the proposed algorithm and make a scheduling example to confirm its efficiency.

• Journal of the military operations research society of Korea
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• v.24 no.2
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• pp.162-169
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• 1998

This study presents an single machine scheduling algorithm minimize lateness of product by controlling machining speed. Generally, production scheduling uses the information of process planning. But the production scheduling algorithm has not considered the control of machining speed in its procedures. Therefore, the purpose of this study is to consider the machining speed in production scheduling algorithm for efficient production scheduling. Machining time and machining cost required to manufacture a piece of a product are expressed as a unimodal convex function with respect to machining speed, so it has minimal point at minimum time speed or the minimum cost speed. Therefore, because of considering the machining cost, the control of machining speed for the algorithm is executed between minimum speed and maximum speed. An example is demonstrated to explain the algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1804-1809
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• 2003

In a large-scale chemical plant, there are scheduling problems in inventory and packing process although production process is stabilized. The profit of the plant is restricted by these problems. In order to improve these problems, integrated scheduling model, which is concerned with whole processes from production to shipment, has been developed in this paper. In this model, decision variables are production sequence, silo allocation, amounts of bulk shipment and packing amounts. In case of a real plant, it is hard to solve by deterministic methods because there are too many decision variables to solve. In this paper, genetic algorithm is presented to solve a PVC process scheduling model within an hour with PCs.

• 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.

• 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.2
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• pp.323-341
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• 1997

Traditionally, the Process Planning problems and the Scheduling problems have been considered as independent ones. However, we can take much advantages by solving the two problems simultaneously. In this paper, we deal with the enlarged problem that takes into account both the process planning and the scheduling problems. And we present a solution algorithm for the problem assuming that the given process plan data is represented by AND/OR graph. A mathematical model(mixed ILP model) whose objective is the minimization of the makespan, is formulated. We found that we can get the optimal solutions of the small-size problems within reasonable time limits, but not the large-size problems. So we devised an algorithm based on the decomposition strategy to solve the large-scale problems (realistic problems) within practical time limits.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.310-330
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• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.811-821
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• 1998

A new scheduling algorithm for large scale semiconductor processes is addressed. The difficulties of scheduling for semiconductor fabrication processes are mainly due from repeating production of wafers that experience reentrant flows. Sequence branch algorithm (SBA) is proposed for large real scheduling problems when all processing times are deterministic. The SBA is based on the reachability graph of Petri net of which the several defects such as memory consumption and system deadlock are complemented. Though the SBA shows the solution deviating a little from the optimal solution of mixed integer programming, it is adjustable for large size scheduling problems. Especially, it shows a potential that is capable of handling commercial size problems that are intractable with mathematical programming.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.319-322
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• 1995

As a trend toward multi-product batch processes is increasing in Chemical Process Industry (CPI), multi-product batch scheduling has been actively studied. But the optimal production scheduling problems for multi-product batch processes are known as NP-complete. Recently Ku and Karimi [5] have studied Simulated Annealing(SA) and Jung et al.[6] have developed Modified Simulated Annealing (MSA) method which was composed of two stage search algorithms for scheduling of batch processes with UIS and NIS. Jung et al.[9] also have studied the Common Intermediate Storage(CIS) policy which have accepted as a high efficient intermediate storage policy. It can be also applied to pipeless mobile intermediate storage pacilities. In spite of these above researches, there have been no contribution of scheduling of CIS policy for chemical batch processes. In this paper, we have developed another MSA for scheduling chemical batch processes with searching the suitable control parameters for CIS policy and have tested the this algorithm with randomly generated various scheduling problems. From these tests, MSA is outperformed to general SA for CIS batch process system.

• ETRI Journal
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• v.26 no.2
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• pp.185-188
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• 2004

In this paper, we design a two-step scheduling algorithm to support multiple bandwidth allocation policies for upstream channel access in an Ethernet passive optical network. The proposed scheduling algorithm allows us a simultaneous approach for multiple access control policies: static bandwidth allocation for guaranteed bandwidth service and dynamic bandwidth allocation for on-demand, dynamic traffic services. In order to reduce the scheduling complexity, we separate the process of the transmission start- time decision from the process of grant generation. This technique does not require the timing information of other bandwidth allocation modules, so respective modules are free from a heavy amount of timing information or complex processing.