• 한국경영과학회지
• /
• 제26권2호
• /
• pp.59-68
• /
• 2001

The job shop scheduling problem is not only NP-hard, but is one of the well known hardest combinatorial optimization problems. The goal of this research is to develop an efficient scheduling method based on hybrid genetic algorithm to address job shop scheduling problem. In this scheduling method, generating method of initial population, new genetic operator, selection method are developed. The scheduling method based on genetic algorithm are tested on standard benchmark job shop scheduling problem. The results were compared with another genetic algorithm0-based scheduling method. Compared to traditional genetic, algorithm, the proposed approach yields significant improvement at a solution.

• International Journal of Precision Engineering and Manufacturing
• /
• 제1권2호
• /
• pp.124-131
• /
• 2000

A shop floor can be considered as and importand level to develop a Computer Integrated Manufacturing system (CIMs). The shop foor is a dynamic environment where unexpected events contrinuously occur, and impose changes to planned activities. The shop floor should adopt an appropriate control system that is responsible for scheduling coordination and moving the manufacturing material and information flow. In this paper, the architecture of the hybrid control model identifies three levels; i.e., the shop floor controller (SFC), the cell controller(CC) and the equipment controller (EC). The methodology for developing these controller is employ an object-oriented approach for static models and IDEF0 for function models for dispatching a job. SFC and CC are coordinated by employing a multi-factors bidding and an adapted Analytic Hierarchy Process(AHP) prove applicability of the suggested method. Test experiment has been conducted by with the shopfloor, consisting of six manufacturing cells.

• Industrial Engineering and Management Systems
• /
• 제14권1호
• /
• pp.22-31
• /
• 2015

In this paper, a two-stage hybrid flow shop problem is considered. Specifically, there exist identical parallel machines at stage 1 and two dedicated machines at stage 2, and the objective of the problem is to minimize makespan. After being processed by any machine at stage 1, a job must be processed by a specific machine at stage 2 depending on the job type, and one type of jobs can have different processing times on each machine. First, we introduce the problem and establish complexity of several variations of the problem. For some special cases, we develop optimal polynomial time solution procedures. Then, we establish some simple lower bounds for the problem. In order to solve this NP-hard problem, three heuristics based on simple rules such as the Johnson's rule and the LPT (Longest Processing Time first) rule are developed. For each of the heuristics, we provide some theoretical analysis and find some worst case bound on relative error. Finally, we empirically evaluate the heuristics.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
• /
• pp.609-612
• /
• 2000

Job-shop scheduling problem(JSSP) is one of the best-known machine scheduling problems and essentially an ordering problem. A new encoding scheme which always give a feasible schedule is presented, by which a schedule directly corresponds to an assigned-operation ordering string. It is initialized with G&T algorithm and improved using the developed genetic operator; APMX or BPMX crossover operator and mutation operator. and the problem of infeasibility in genetic generation is naturally overcome. Within the framework of the newly designed genetic algorithm, the NP-hard classical job-shop scheduling problem can be efficiently solved with high quality. Moreover the optimal solutions of the famous benchmarks, the Fisher and Thompson's 10${\times}$10 and 20${\times}$5 problems, are found.

• Management Science and Financial Engineering
• /
• 제20권2호
• /
• pp.33-37
• /
• 2014

A flow shop scheduling problem involves scheduling jobs on multiple machines in series in order to optimize a given criterion. The flow time of a job is the amount of time the job spent before its completion and the stretch of the job is the ratio of its flow time to its processing time. In this paper, a hybrid genetic algorithm (HGA) approach is proposed for minimizing the total stretch in flow shop scheduling. HGA adopts the idea of seed selection and development in order to reduce the chance of premature convergence that may cause the loss of search power. The performance of HGA is compared with that of genetic algorithms (GAs).

• 한국시뮬레이션학회논문지
• /
• 제25권4호
• /
• pp.1-12
• /
• 2016

항공기 엔진부품을 생산하는 제조산업은 전통적인 주문형 제조방식을 따르며, 다품종 소량생산시스템이다. 또한 생산공장의 배치는 유사한 기능을 가지는 병렬 기계들이 공정별 배치 형태로 구성되어 있고 조립공정이 포함되는 Hybrid Job Shop이다. 더 나아가 다양한 대체장비를 가지고 있으며, 공정 경로도 다양하고, 조립공정도 포함되어 있다. 이와 같은 복잡한 시스템의 생산능력을 검증하기 위해서는 시뮬레이션이 가장 우수한 방법이며, 정확한 모델의 개발을 위해 고도의 시뮬레이션 모델링기술이 필요하다. 이 논문에서는 위에서 언급한 복잡한 특성을 가진 생산시스템을 효과적으로 모델링하기 위한 방법론을 소개하고, 실제 항공기부품 생산업체를 대상으로 시뮬레이션 연구를 수행한 사례에 대해 소개한다.

• 대한산업공학회지
• /
• 제40권6호
• /
• pp.642-647
• /
• 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.

• 대한산업공학회지
• /
• 제33권2호
• /
• pp.213-220
• /
• 2007

This paper considers a two-stage hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. Each job is processed through the two production stages in stages, each of which has multiple identical parallel machines. The problem is to determine the allocation and sequence of jobs at each stage. A branch and bound algorithm that gives the optimal solutions is suggested that incorporates the methods to obtain the lower and upper bounds. Dominance properties are also suggested to reduce the search space. To show the performance of the algorithm, computational experiments are done on randomly generated problems, and the results are reported.

• 대한산업공학회지
• /
• 제35권4호
• /
• pp.257-265
• /
• 2009

This paper considers the hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. In hybrid flow shops, each job is processed through multiple production stages in series, each of which has multiple identical parallel machines. The problem is to determine the allocation of jobs to the parallel machines at each stage as well as the sequence of the jobs assigned to each machine. Due to the complexity of the problem, we suggest search heuristics, tabu search and simulated annealing algorithms with a new method to generate neighborhood solutions. In particular, to evaluate and select neighborhood solutions, three surrogate objectives are additionally suggested because not much difference in the number of tardy jobs can be found among the neighborhoods. To test the performances of the surrogate objective based search heuristics, computational experiments were performed on a number of test instances and the results show that the surrogate objective based search heuristics were better than the original ones. Also, they gave the optimal solutions for most small-size test instances.

• 한국정보처리학회논문지
• /
• 제6권4호
• /
• pp.959-968
• /
• 1999

기존의 많은 논문에서는 병렬휴리스틱법(Parallel Heuristics) 또는 몇 개의 휴리스틱법을 결합하는 하이브리드 시스템(Hybrid system)이 보다 양질의 탐색 결과를 얻을 수 있음을 보고하고 있다. 그러나 단일 프로세스를 가진 범용 컴퓨터상에서의 병렬화와 하이브리드화는 지나치게 많은 탐색시간을 요구한다. 이러한 경우 탐색의 고속화를 위해서는 탐색법간의 보다 적절한 결합법이 요구된다. 이를 위한 방법론으로서 병렬프리스틱법의 고속화모델(Fast Model of Parallel Heuristics : FMPH)을 제안하고 있다. 유전적 알고리즘(Genetic algorithms)의 섬모델(Island Model)에 의거, 다양한 탐색 공간에서 선택된 우수한 엘리트해에 대해서만 선택적으로 국소탐색(local search)의 능력이 뛰어난 타부 탐색법을 도입한다. 본 논문에서는 NP-hard문제 중에서도 가장 어려운 문제로 평가되는 잡숍 스케쥴링 문제(Job-Shop scheduling Problem)에 대해 적용한 병렬프리스틱법의 고속화 모델을 소개하며 모델의 범용성을 입증하기 위해 유명한 벤치마크 문제에 적용하여 얻은 뛰어난 결과를 보고한다.