• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(B)
• /
• pp.17-20
• /
• 2011

This paper presents an approach for integrating u-Life care applications into the Cloud computing based on virtual resources to support real-time services and to improve quality of service (QoS) requirement. We propose an architecture for virtualization resources scheduling. The proposed is based on the concepts of Cloud computing and Wireless sensor networks. In this paper, we focus on the scheduling u-Life care applications run on the virtual machine (VM) resources in Cloud computing.

• 대한산업공학회지
• /
• 제25권3호
• /
• pp.360-368
• /
• 1999

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, a hybrid genetic algorithm is proposed. The algorithm combines a genetic algorithm for global search and a heuristic for local optimization to improve the speed of evolution convergence. The genetic operators are developed such that parallel machines can be handled in an efficient and effective way. For local optimization, the adjacent pairwise interchange method is used. The proposed hybrid genetic algorithm is compared with two heuristics, the nearest setup time method and the maximum penalty method. Computational results for a series of randomly generated problems demonstrate that the proposed algorithm outperforms the two heuristics.

• 한국경영과학회:학술대회논문집
• /
• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
• /
• pp.517-522
• /
• 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.

• 대한산업공학회지
• /
• 제20권2호
• /
• pp.31-38
• /
• 1994

We consider a single-machine scheduling problem dealing with the manufacture of components for subsequent assembly into end products. Each product requires both unique components and common components, and each production requires a setup. By making some assumptions on the data and the availability of the components for assembly, Baker provides on efficient dynamic programming algorithm for obtaining the optimal schedule. In this paper we do not impose any requirement on the data, and we solve the more complicated batching and sequencing problem. We suggest a simple heuristic method that is efficient and finds solutions that are optimal or close to the optimal solution.

• 대한산업공학회지
• /
• 제24권1호
• /
• pp.37-50
• /
• 1998

This paper considers the nonidentical parallel machine scheduling problem in which n jobs having different due dates are to be scheduled on m nonidentical parallel machines. For the make-to-order manufacturing environment, the objective is to minimize the number of tardy jobs. A 0-1 nonlinear programming model is formulated and a heuristic algorithm that allocates and sequences jobs to machines is developed. The proposed algorithm makes use of the concept of assignment problem based on the suitability measure as the cost coefficient. Computational experiments show that the proposed algorithm is superior to the existing one in some performance measures such as number of tardy jobs. In addition, this algorithm is appropriate for solving real industrial problems efficiently.

• 경영과학
• /
• 제17권1호
• /
• pp.41-54
• /
• 2000

in the real world situations that some jobs need be processed only on certain limited machines frequently occur due to the capacity restrictions of machines such as tools fixtures or material handling equipment. In this paper we consider n-job non-preemptive and m parallel machines scheduling problem having two machines group. The objective function is to minimize the sum of earliness and tardiness with different release times and due dates. The problem is formulated as a mixed integer programming problem. The problem is proved to be Np-complete. Thus a heuristic is developed to solve this problem. To illustrate its suitability and efficiency a proposed heuristic is compared with a genetic algorithm and tabu search for a large number of randomly generated test problems in ship engine assembly shop. Through the experimental results it is showed that the proposed algorithm yields good solutions efficiently.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
• /
• pp.310-313
• /
• 1996

Mathematical programming method for finding optimal solution of job shop scheduling is inadequate to real situation because fo too much computation time. In contrast, dispatching rule is helpful for reducing compuation time but is not guaranted to find optimal solution. The purpose of this paper is to develop a new dispatching rule and procedure to minimize mean flow time whose result is near the optimal solution for job shop scheduling. First step is to select machine which have shortest finishing operation time among the schedulable operations. Second step is to select operation with regard to estimated remaining operation time. The suggested rule is compared with nondelay and MWKR rule for three examples, and is confirmed to be most effective to minimize mean flow time.

• 산업공학
• /
• 제11권3호
• /
• pp.65-75
• /
• 1998

This research considers the problem of scheduling jobs on parallel machines with non-common due dates and additional resource constraints. The objective is to minimize the total absolute deviation of job completion times about the due dates. Job processing times are assumed to be the same. This problem is motivated by restrictions that occur in the handling and processing of jobs in certain phases of semiconductor manufacturing and other production systems. We examine two problems. For the first of these, the number of different types of additional: resources and resource requirements per job are arbitrary. The problem is formulated as a zero-one integer linear programming and the Lagrangian relaxation approach is used. For the second case, there exists one single type of additional resource and the resource requirements per job are zero or one. We show how to formulate the problem as an assignment problem.

• Industrial Engineering and Management Systems
• /
• 제7권3호
• /
• pp.204-213
• /
• 2008

Companies that produce capital goods need to schedule the production of products that have complex product structures with components that require many operations on different machines. A feasible schedule must satisfy operation and assembly precedence constraints. It is also important to avoid deadlock situations. In this paper a Genetic Algorithm (GA) has been developed that includes a new repair process that rectifies infeasible schedules that are produced during the evolution process. The algorithm was designed to minimise the combination of earliness and tardiness penalties and took into account finite capacity constraints. Three different sized problems were obtained from a collaborating capital goods company. A design of experimental approach was used to systematically identify that the best genetic operators and GA parameters for each size of problem.

• 산업경영시스템학회지
• /
• 제21권48호
• /
• pp.213-222
• /
• 1998

The objective of this study is to develop a knowledge-based scheduler applying simulation and knowledge base. This study utilizes a machine induction to build knowledge base which enables knowledge acquisition without domain expert. In this study, the best job dispatching rule for each order is selected according to the specifications of the order information. And these results are built to the fact base and knowledge base using the attribute-oriented induction method and simulation. When a new order enters in the developed system, the scheduler retrieves the knowledge base in order to find a matching record. If there is a matching record, the scheduling will be carried out by using the job dispatching rule saved in the knowledge base. Otherwise the best rule will be added to the knowledge base as a new record after scheduling to all the rules. When all these above steps finished the system will furnish a learning function.