• The Journal of Information Systems
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• v.25 no.1
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• pp.159-182
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• 2016

Purpose This paper aims to develop a novel tabu search algorithm for solving the sequencing problems with precedence constraints. Due to constraints, the traditional meta heuristic methods can generate infeasible solutions during search procedure, which must be carefully dealt with. On the contrary, the candidate order based tabu search (COTS) is based on a novel neighborhood structure that guarantees the feasibility of solutions, and can dealt with a wide range of sequencing problems in flexible manner. Design/methodology/approach Candidate order scheme is a strategy for constructing a feasible sequence by iteratively appending an item at a time, and it has been successfully applied to genetic algorithm. The primary benefit of the candidate order scheme is that it can effectively deal with the additional constraints of sequencing problems and always generates the feasible solutions. In this paper, the candidate order scheme is used to design the neighborhood structure, tabu list and diversification operation of tabu search. Findings The COTS has been applied to the single machine job sequencing problems, and we can see that COTS can find the good solutions whether additional constraints exist or not. Especially, the experiment results reveal that the COTS is a promising approach for solving the sequencing problems with precedence constraints. In addition, the operations of COTS are intuitive and easy to understand, and it is expected that this paper will provide useful insights into the sequencing problems to the practitioners.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.69-74
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• 2004

A sequencing problem is to find an ordered sequence of some entities which maximizes (or minimize) the domain specific objective function. As some typical examples of sequencing problems, there are traveling salesman problem, job shop scheduling, flow shop scheduling, and so on. This paper introduces a new type of sequencing problems, named a sequencing problem with fuzzy preference relation, where a fuzzy preference relation is provided for the evaluation of the quality of sequences. It presents how such a problem can be formulated in terms of objective function. It also proposes a genetic algorithm applicable to such a sequencing problem.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.1
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• pp.139-157
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• 1993

This paper presents possibilistic job scheduling based on the membership function as an alternative to probabilistic job scheduling and illustrates a methodology for solving job sequencing problem which the opinions of experts greatly disagree in each processing time. Triangular fuzzy numbers are used to represent the processing times of experts. Here, the comparison method is based on the dominance property. The criteria for dominance are presented. By the dominance criteria, for each job, a mojor TFN and a minor TFN are selected and apessimistic sequence with mojor TFNs and an optimistic sequence with minor TFNs are computed. The three-machine flow shop problem is considered as an example to illustrate the approach.

• 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 Operations Research and Management Science Society
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• v.24 no.3
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• pp.73-82
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• 1999

In this paper, we address a single machine non-preemptive n-job scheduling problem to minimize the sum of earliness and tardiness with different release times and due dates. To solve the problem, we propose a genetic algorithm with new crossover and mutation operators to find the job sequencing. For the proposed genetic algorithm, the optimal pair of crossover and mutation rates is investigated. To illustrate the suitability of genetic algorithm, solutions of genetic algorithm are compared with solutions of exhaustive enumeration method in small size problems and tabu search method in large size problems. Computational results demonstrate that the proposed genetic algorithm provides the near-optimal job sequencing in the real world problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.82-88
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• 2011

A typical job sequencing problem is studied in this research to improve productivities in manufacturing companies. The problem consists of two-stage parts and assembly processes. Two parts are provided independently each other and then two sequential assembly processes are followed. A new heuristic is developed to solve the new type of sequencing problem. Initial solution is developed in the first stage and then the initial solution is improved in the second stage. In the first stage, a longer part manufacturing time for each job is selected between two, and then a sequence is determined by descending order of the times. This initial sequence is compared with Johnson's sequence obtained from 2-machine assembly times. Any mismatches are tried to switch as one possible alternative and completion time is calculated to determine whether to accept the new sequence or not to replace the current sequence. Searching process stops if no more improvement can be made.

• Journal of Korean Institute of Industrial Engineers
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• v.9 no.1
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• pp.23-31
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• 1983

A group scheduling problem, which is production scheduling problem associated with the concept of group technology, is studied under due date constraints in multi-stage manufacturing systems. The purpose of this paper is to develop and evaluate a practical heuristic procedure for determining group sequence and job sequence within each group to minimize total tardiness in multi-stage manufacturing systems. A heuristic algorithm has been developed by introducing the concept of relative measures of job tardiness and group tardiness for job sequencing and group sequencing, respectively. A numerical example is shown to illustrate the proposed procedure. The heuristic algoirthm is tested by comparisons with problems with known optimal solutions and also with random group schedules for a set of large-size problems. Results indicate that the proposed heuristic algorithm provides good solutions with small computational requirements, and thus is viable for large size problems in practice.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.2
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• pp.209-229
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• 1996

Scheduling problems which determine the sequence of jobs are one of the Important issues to many industries. This paper deals with a single-machine job sequencing problem which has complex constraints and an objective function. To solve the problem, an expressive constraint description language and an automatic code generator are developed for our scheduling system. The user just needs to describe the scheduling problem using the constraint description language that allows to express both quantitative and qualitative constraints as well as an objective function in real world semantics. Then, a complete scheduling program based on constraint satisfaction technique is automatically generated through the code generator. Advantage of this approach is that models of the scheduling problems are easily developed and maintained because models ore formulated by using the language which reflects real world semantics.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.3
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• pp.281-287
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• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.421-422
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• 2018

작업 순서 결정 문제의 목표는 다양한 작업들에 대한 최적의 처리 순서를 결정하여 총 처리시간이나 납기 지연과 관련된 지표들을 최소화하는 것이다. 나아가, 실제 생산 현장에서는 작업 처리 순서를 결정할 때, 긴급도나 고객과의 관계 등과 같은 요인을 고려하여 일부 작업들을 특정 시간 내에 완료해야 할 수 있으며, 이 같은 제약 조건을 완료 시간 제약이라 한다. 본 논문에서는 완료 시간 제약을 갖는 작업 순서 결정 문제의 개념과 특성에 대해 살펴보고, 이러한 문제를 풀이하기 위한 알고리즘 개발에 후보 순위 접근법을 적용할 것을 제안한다.