• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.64
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• pp.63-76
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• 2001

This paper presents a job-shop type flexible manufacturing system(FMS) scheduling problem and examines the effects of scheduling rules on the performance of FMS. Several machine and AGV scheduling rules are tested against the mean flow-time criterion. In this paper, I compare the rules under various experimental conditions by using an FMS simulation model. One of the objectives of this study is to discuss how the simulation-based scheduling problem can be operated. The other is to measure sensitivity of the rules to changes at inter arrival time, queue capacity, breakdown rates for machines and AGV, and AGV speed. Therefore, the results of simulation experiments were considered on FMS design and operating stages. A comprehensive bibliograph? is also presented in the paper.

• The Journal of Information Systems
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• v.25 no.4
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• pp.105-119
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• 2016

Purpose The conventional solution methods for production scheduling problems typically focus on the active schedules, which result in short makespans. However, the active schedules are more difficult to generate than the semi active schedules. In other words, semi active schedule based search strategy may help to reduce the computational costs associated with production scheduling. In this context, this paper aims to compare the performances of active schedule based and semi active schedule based search methods for production scheduling problems. Design/methodology/approach Two decoding approaches, active schedule decoding and semi active schedule decoding, are introduced in this paper, and they are used to implement genetic algorithms for classical job shop scheduling problem. The permutation representation is adopted by the genetic algorithms, and the decoding approaches are used to obtain a feasible schedule from a sequence of given operations. Findings The semi active schedule based genetic algorithm requires slightly more iterations in order to find the optimal schedule, while its execution time is quite shorter than active schedule based genetic algorithm. Moreover, the operations of semi active schedule decoding is easy to understand and implement. Consequently, this paper concludes that semi active schedule based search methods also can be useful if effective search strategies are given.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2993-3010
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• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.383-391
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• 2005

The paper is devoted to some flow shop scheduling problems, where job processing times are defined by functions dependent on their positions in the schedule. An example is constructed to show that the classical Johnson's rule is not the optimal solution for two different models of the two-machine flow shop scheduling to minimize makespan. In order to solve the makespan minimization problem in the two-machine flow shop scheduling, we suggest Johnson's rule as a heuristic algorithm, for which the worst-case bound is calculated. We find polynomial time solutions to some special cases of the considered problems for the following optimization criteria: the weighted sum of completion times and maximum lateness. Some furthermore extensions of the problems are also shown.

• IE interfaces
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• v.11 no.3
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• pp.1-13
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• 1998

This paper addresses a scheduling scheme for Flexible Flow Shop(FFS) in the case that a factory is a sub-plant of an electronic device manufacturing plant. Under this environment, job orders for the sub-plants in the production route are generated together with job processing time bucket when the customer places orders for final product. The processing time bucket for each job is a duration from possible release date to permissible due date. A sub-plant modeled FFS should schedule these jobs orders within time bucket. Viewing a Printed Circuit Board(PCB) assembly line as a FFS, the developed scheme schedules an incoming order along with the orders already placed on the scheduled. The scheme consists of the four steps, 1)assigning operation release date and due date to each work cells in the FFS, 2)job grouping, 3)dispatching and 4)machine allocation. Since the FFS scheduling problem is NP-complete, the logics used are heuristic. Using a real case, we tested the scheme and compared it with the John's algorithm and other dispatching rules.

• Management Science and Financial Engineering
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• v.9 no.2
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• pp.47-68
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• 2003

We consider the customer order scheduling problem with job capacity restriction where the number of jobs in the shop at the same time is fixed. In the customer order scheduling problem, each job is part of some batch (customer order) and the composition of the jobs (product) in the batch is pre-specified. The objective function is associated with the completion time of the batches instead of the completion time of the jobs. We first summarize the known results for the general customer order scheduling problems. Then, we establish some new properties for the problems with job capacity restriction. For the case of unit processing time with the objective of minimizing makespan, we develop a polynomial-time optimal procedure for the two machine case. For the same problem with a variation of no batch alternation, we also develop a polynomial-time optimal procedure. Then, we show that the problems with the objectives of minimizing makespan and minimizing average batch completion time become NP-hard when there exist arbitrary number of machines. Finally, We propose optimal solution procedures for some special cases.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.13-18
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• 1986

This paper is concerned with flow-shop permutation scheduling problem. This paper presents an algorithm for the minimum makespan sequence. The efficiency of proposed algorithm is demonstrated by comparisons with the existing algorithms: Johnson's, branch & bound method, and heuristic algorithms. The proposed algorithm is more effective than the other algorithms. A numerical example is given to illustrate the procedure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.14 no.23
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• pp.65-70
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• 1991

This paper is concerned with a flow-shop scheduling problem for all jobs having the common due date using V-Shape penalty cost function for earliness and lateness. The objective of the paper is to develop an efficient heuristic scheduling algorithm for minimizing total penalty cost function and for determining the optimal common due date. In addition, the between-job-delay for two machines are considered for developing the algorithm. A numerical example is given for illustrating the proposed algorithm.