• 산업경영시스템학회지
• /
• 제20권41호
• /
• pp.231-242
• /
• 1997

This paper describe an approximation method for solving the minimum makespan problem of job shop scheduling with fuzzy processing time. We consider the multi-part production scheduling problem in a job shop scheduling. The job shop scheduling problem is a complex system and a NP-hard problem. The problem is more complex if the processing time is imprecision. The Fuzzy set theory can be useful in modeling and solving scheduling problems with uncertain processing times. Lee-Li fuzzy number comparison method will be used to compare processing times that evaluated under fuzziness. This study propose heuristic algorithm solving the job shop scheduling problem under fuzzy environment. In This study the proposed algorithm is designed to treat opinions of experts, also can be used to solve a job shop environment under the existence of alternate operations. On the basis of the proposed method, an example is presented.

• 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.

• Industrial Engineering and Management Systems
• /
• 제10권3호
• /
• pp.203-208
• /
• 2011

Job shop scheduling is well-known as one of the hardest combinatorial optimization problems and has been demonstrated to be NP-hard problem. In the past decades, several researchers have devoted their effort to develop evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for job shop scheduling problem. Differential Evolution (DE) algorithm is a more recent evolutionary algorithm which has been widely applied and shown its strength in many application areas. However, the applications of DE on scheduling problems are still limited. This paper proposes a one-stage differential evolution algorithm (1ST-DE) for job shop scheduling problem. The proposed algorithm employs random key representation and permutation of m-job repetition to generate active schedules. The performance of proposed method is evaluated on a set of benchmark problems and compared with results from an existing PSO algorithm. The numerical results demonstrated that the proposed algorithm is able to provide good solutions especially for the large size problems with relatively fast computing time.

• 한국산업융합학회 논문집
• /
• 제4권3호
• /
• pp.281-287
• /
• 2001

Traditional job shop scheduling is supposed that machine has a fixed processing job type. But actually the machine has a highly utilization or long processing time is occurred delay. Therefore product system is difficult to respond quickly to the change of products or loads or machine failure etc. Here we use flexible job shop which is supposed that a machine has several jobs by tool change. The heuristic for the flexible job shop scheduling has to solve two problems. One is a routing problem which is determine a machine to process job. The other is sequencing problem which is determine processing sequence. The approach to solve two problems arc a hierarchical approach which is determined routing and then schedule, and a concurrence approach which is solved concurrently two problems by considering routing when it is scheduled. In this study, we simulate for flexibility efficiency fo flexible job shop scheduling with machine failure using hierarchical approach.

• 한국컴퓨터산업학회논문지
• /
• 제6권5호
• /
• pp.813-820
• /
• 2005

대체공정을 고려한 Job Shop 일정계획을 수립하기 위한 유전알고리즘을 개발하기 위하여 다음과 같이 유전알고리즘 효율분석을 실시하였다. 첫째, 대체공정을 고려한 job shop 일정계획을 수립하기 위한 유전 알고리즘을 제시하고 둘째, 전통적인 job shop 일정계획에 대한 벤치마크 문제에 대해 유전 알고리즘의 타당성을 확인하고 셋째, Park[3] 문제에 대해 유전알고리즘과 작업배정규칙을 적용한 결과를 비교하였다.

• 한국지능시스템학회논문지
• /
• 제11권3호
• /
• pp.191-199
• /
• 2001

유전자알고리듬(Genetic Algorithm)은 확률적인 집단 탐색법이고 적응도함수의 형태에 관계없는 직접 탐색법이기 때문에 최근 최적화 방법으로 주목을 받고 있다. 본 논문에서는 Job-shop Schedule Problem에 대하여 교배방법으로 JOX를 사용하며, 효율적인 탐색을 위하여 탐색범위를 축소시키는 강제조작을 형질유전을 고려한 형질유전GT법을 제안하고, 세대교체에 있어 모집단의 다양성을 유지하기 위하여 집단 내에 동일한 개체를 배제하는 방법을 제안한다. 제안 알고리듬을 Fisher & Thompson의 FT10$\times$10 및 FT20$\times$5 문제에 적용하여 유효성을 실험적으로 검증한다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
• /
• pp.1095-1098
• /
• 1993

This paper presents linear programming neural networks for job-shop scheduling. The starting times of tasks and constraints are formulated as the linear programming problem. A modified Hopfield neural network is proposed for solving job-shop scheduling.

• 대한안전경영과학회지
• /
• 제6권2호
• /
• pp.141-154
• /
• 2004

The objective of this paper is to develop the efficient heuristic method for solving the minimum makespan problem of the job shop scheduling. The proposed heuristic method is based on a constraint satisfaction problem technique and a improved randomizing search algorithm. In this paper, ILOG programming libraries are used to embody the job shop model, and a constraint satisfaction problem technique is developed for this model to generate the initial solution. Then, a improved randomizing search algorithm is employed to overcome the increased search time of constrained satisfaction problem technique on the increased problem size and to find a improved solution. Computational experiments on well known MT and LA problem instances show that this approach yields better results than the other procedures.

• 경영과학
• /
• 제20권1호
• /
• pp.51-64
• /
• 2003

The JSSP (Job Shop Scheduling Problem) Is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. we design scheduling method based on SGA (Single Genetic Algorithm) and PGA (Parallel Genetic Algorithm). In the scheduling method, the representation, which encodes the job number, is made to be always feasible, initial population is generated through integrating representation and G&T algorithm, the new genetic operators and selection method are designed to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The scheduling method based on genetic algorithm are tested on five standard benchmark JSSPs. The results were compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed approach yields significant improvement at a solution. The superior results indicate the successful Incorporation of generating method of initial population into the genetic operators.

• 대한산업공학회지
• /
• 제21권3호
• /
• pp.409-428
• /
• 1995

Job shop scheduling with m different machines and n different jobs is a NP-hard problem of combinatorial optimization. The purpose of the paper is to develop the heuristic method using tabu search for job shop scheduling to minimize makespan or mean flowtime. To apply tabu search to job shop scheduling problem, in this paper we propose the several move methods that employ insert moves in order to generate the neighbor solutions, and present the efficient rescheduling procedure that yields active schedule for a changed operation sequence by a move of operations. We also discuss the tabu search techniques of diversifying the search of solution space as well as the simple tabu search. By experiments, we find the appropriate tabu list size and tabu attributes, and analyze the proposed tabu search techniques with respect to the quality of solutions and the efforts of computation. The experimental results show that the proposed tabu search techniques using long-term memory function have the ability to search a good solution, and are more efficient in the mean flowtime minimization problem than in the makespan minimization.