• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.187-192
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• 2014

Lot-streaming is the process of splitting a job (lot) into sublots to allow the overlapping of operations between successive machines in a multi-stage production system. A new genetic algorithm (NGA) is proposed for an n-job, m-machine, lot-streaming flow shop scheduling problem with equal-size sublots in which the objective is to minimize the total stretch. The stretch of a job is the ratio of the amount of time the job spent before its completion to its processing time. NGA replaces the selection and mating operators of genetic algorithms (GAs) by marriage and pregnancy operators and incorporates the idea of inter-chromosomal dominance and individuals' similarities. Extensive computational experiments for medium to large-scale lot-streaming flow-shop scheduling problems have been conducted to compare the performance of NGA with that of GA.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.10a
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• pp.137-141
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• 1998

본 연구는 TPN unfolding을 이용하여 WIP의 FMS(Flexible Manufacturing System)를 분석하는 방법을 제시한다. PN의 unfolding은 상태폭발이 발생하지 않는 concurrent system의 검증에 사용되는 순서기반방법이다. 본 연구는 일반적으로 발생하는 순환상태스케쥴문제에서 가장 그 작업과정 시간을 최적화함을 위하여 원래의 net을 동일한 비순환 net으로 바꾸어 줄 수 있는 unfolding 개념을 기반으로 한 것이다.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.3
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• pp.55-65
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• 2005

In recent manufacturing systems, most jobs may have several process plans, such as alternative sequence of operations, alternative machine etc. A few researches have addressed the necessity for the integration of process planning and scheduling function for efficient use of manufacturing resources. But the integration problem is very difficult and complex. Many prior researches considered them separately or sequentially. It introduces overlapping or partial duplications in solution efforts. In this paper, Integration problem of jobs with multiple process plans in a job shop environment Is addressed. In order to achieve an efficient integration between process planning and scheduling by taking advantage of the flexibility that alternative process plans offer, we designed GA(Genetic Algorithm)-based scheduling method. The performance of proposed GA is evaluated through comparing integrated scheduling with separated scheduling in real world company with alternative machines and sequences of operations. Also, a couple of benchmark problems are used to evaluate performance. The integrated scheduling method in this research can be effectively epplied to the real case.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.563-570
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• 2004

Genetic algorithms have been successfully applied to various optimization problems belonging to NP-hard problems. The sequential ordering problems(SOP) and the job shop scheduling problems(JSP) are well-known NP-hard problems with strong influence on industrial applications. Both problems share some common properties in that they have some imposed precedence constraints. When genetic algorithms are applied to this kind of problems, it is desirable for genetic operators to be designed to produce chromosomes satisfying the imposed precedence constraints. Several genetic operators applicable to such problems have been proposed. We call such genetic operators precedence-preserving genetic operators. This paper presents three existing precedence-preserving genetic operators: Precedence -Preserving Crossover(PPX), Precedence-preserving Order-based Crossover (POX), and Maximum Partial Order! Arbitrary Insertion (MPO/AI). In addition, it proposes two new operators named Precedence-Preserving Edge Recombination (PPER) and Multiple Selection Precedence-preserving Order-based Crossover (MSPOX) applicable to such problems. It compares the performance of these genetic operators for SOP and JSP in the perspective of their solution quality and execution time.

• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.11-18
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• 2016

This paper considers a scheduling problem in a flexible job shop with a reconfigurable manufacturing cell. The flexible job shop has both operation and routing flexibilities, which can be represented in the form of a multiple process plan, i.e. each part can be processed through alternative operations, each of which can be processed on alternative machines. The scheduling problem has three decision variables: (a) selecting operation/machine pairs for each part; (b) sequencing of parts to be fed into the reconfigurable manufacturing cell; and (c) sequencing of the parts assigned to each machine. Due to the reconfigurable manufacturing cell's ability of adjusting the capacity, functionality and flexibility to the desired levels, the priority scheduling approach is proposed in which the three decisions are made at the same time by combining operation/machine selection rules, input sequencing rules and part sequencing rules. To show the performances of various rule combinations, simulation experiments were done on various instances generated randomly using the experiences of the manufacturing experts, and the results are reported for the objectives of minimizing makespan, mean flow time and mean tardiness, respectively.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.4
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• pp.386-394
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• 2012

Recent trends in the flexible manufacturing systems are morphing cell control for the shop-wide production operation system and providing the integrated operation and execution system together with vendor-specific FMC/FMS platform. In these requirements, the shop-floor level operation system plays a role of coordinating the control activity of each cell, and has to provide flexibility for the complexity of mixed operations of various cells. This paper suggests a system architecture for the mixed environments of multi-cells and job shop, its corresponding enabling technologies based on comparative studies with other related studies and commercialized systems. This approach includes a process definition model considering the integration with upper BOM-BOP and external service modules, and reconfigurable device-level interface which provides dynamic interconnections with machine tools and cell controllers. The function modules and their implementation results are also described to provide the feasibility of the proposed approaches as the flexible shop-floor operation system for the multi-cell environments.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1987.10a
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• pp.35-35
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• 1987

Consider a job shop that is modelled as an open queueing network of the Jackson(l957) type. All work stations in the shop have the same number of parallel servers. Two problems are studied : the loading of stations and the assignment of servers, which are represented by loading and assingment vectors, respectively. Ma jorization and arrangement orderings are established to order, respectively, the loading and the assignment vectors. It is shown that reducing the loading vector under ma jorizat ion or increasing the assignment vector under arrangement ordering will reduce the congestion in the shop in terms of reducing the total number of jobs(in the sense of likelihood ratio ordering), the maximum queue length(in the sense of stochastic ordering), and the queue-length vector( in the sense of stochastic majorization). The results can be used to supprot production planning in certain job shops, and to aid the desing of storage capacity. (OPEN QUEUEING NETWORK; WJORIZATION; ARRANGEMENT ORDERINC; LIKELIHOOD RATIO ORDERINC; STOCHASTIC ORDERING)

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.6
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• pp.642-647
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• 2014

In this paper, a hybrid genetic algorithm (HGA) approach is proposed for an n-job, m-machine flow shop scheduling problem with limited capacity buffers with blocking in which the objective is to minimize the total stretch. The stretch of a job is the ratio of the amount of time the job spent before its completion to its processing time. HGA adopts the idea of seed selection and development in order to improve the exploitation and exploration power of genetic algorithms (GAs). Extensive computational experiments have been conducted to compare the performance of HGA with that of GA.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.3
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• pp.501-514
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• 1997

Manufacturing is fast entering a new age of industrial excellence that is being called "Agile Manufacturing." The goal of Agile Manufacturing is to link customers, suppliers, and the manufacturing system into a super-efficient confederation to produce a variety of products quickly and at a low cost. In order to improve the quality of the study of production input control(PIC) in a job shop manufacturing system by reducing the significant gap between research models and models of actual manufacturing systems, the previous line of research on PICs in a job shop manufacturing system is extended by integrating customers and suppliers with the manufacturing system. Then, a set of measures is developed to evaluate PICs, measures that reflect concerns of customers and suppliers as well as concerns of the manufacturer. Also, a weighted overall measure (with various cases to represent different possible weights of manufacturer's emphasis on the performance measures) is used to synthesize all the performance measures. Then, for each case, various existing PICs are evaluated in combination with various priority dispatching rules(PDRs).

• Industrial Engineering and Management Systems
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• v.13 no.1
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• pp.43-51
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• 2014

This study presents an application of adaptive particle swarm optimization (APSO) to solving the bi-level job-shop scheduling problem (JSP). The test problem presented here is $10{\times}10$ JSP (ten jobs and ten machines) with tribottleneck machines formulated as a bi-level formulation. APSO is used to solve the test problem and the result is compared with the result solved by basic PSO. The results of the test problem show that the results from APSO are significantly different when compared with the result from basic PSO in terms of the upper level objective value and the iteration number in which the best solution is first identified, but there is no significant difference in the lower objective value. These results confirmed that the quality of solutions from APSO is better than the basic PSO. Moreover, APSO can be used directly on a new problem instance without the exercise to select parameters.