• Title/Summary/Keyword: Due-date Management

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Predicting Due Dates under Various Combinations of Scheduling Rules in a Wafer Fabrication Factory

  • Sha, D.Y.;Storch, Richard;Liu, Cheng-Hsiang
    • Industrial Engineering and Management Systems
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    • v.2 no.1
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    • pp.9-27
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    • 2003
  • In a wafer fabrication factory, the completion time of an order is affected by many factors related to the specifics of the order and the status of the system, so is difficult to predict precisely. The level of influence of each factor on the order completion time may also depend on the production system characteristics, such as the rules for releasing and dispatching. This paper presents a method to identify those factors that significantly impact upon the order completion time under various combinations of scheduling rules. Computer simulations and statistical analyses were used to develop effective due date assignment models for improving the due date related performances. The first step of this research was to select the releasing and dispatching rules from those that were cited so frequently in related wafer fabrication factory researches. Simulation and statistical analyses were combined to identify the critical factors for predicting order completion time under various combinations of scheduling rules. In each combination of scheduling rules, two efficient due date assignment models were established by using the regression method for accurately predicting the order due date. Two due date assignment models, called the significant factor prediction model (SFM) and the key factor prediction model (KFM), are proposed to empirically compare the due date assignment rules widely used in practice. The simulation results indicate that SFM and KFM are superior to the other due date assignment rules. The releasing rule, dispatching rule and due date assignment rule have significant impacts on the due date related performances, with larger improvements coming from due date assignment and dispatching rules than from releasing rules.

Next station selection rules for FMS scheduling against due-date (납기를 고려한 FMS 일정계획에서의 기계선정규칙)

  • 문일경;김태우
    • Korean Management Science Review
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    • v.13 no.2
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    • pp.147-161
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    • 1996
  • Due-date is an important factor in Flexible Manufacturing System scheduling. Even though most of researchers have focused part selection and loading problem using fixed due-date assignment rules, FMSs consist of multi-function machines which facilitate alternative processes. This research investigates interactions of three dispatching mechanisms, three NSS (Next Station Selection) rules and four due-date assignment rules using simulation. Both cost-based and time-based performance measures are considered in this research.

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A Study For Improvement of Due Date Rate by Supplementing Defects of MRP Using DBR (DBR을 이용한 MRP 단점 보완에 따른 납기 준수율 향상에 관한 연구)

  • 조중현;양광모;강경식
    • Proceedings of the Safety Management and Science Conference
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    • 2004.05a
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    • pp.299-302
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    • 2004
  • Today, several manufacture enterprises are endeavoring constantly to receive order winners of subsidiary company product. There are tendencies to occupy competitive advantage in high position in price competition and in quality etc. But, it is not easy to keep it even if price has been cheap recently. Also, it is hard to be competitive advantage element more, because production smoothing was made much even if there is in quality. To keep or improve present competitive power, the due date rate is becoming importance. Several techniques with MRP, MRP II appeared in the 1970s by method to improve the these due date rate. These techniques have some defects to due date. Therefore, in this paper, MRP wishes to receive the due date rate that is improved more by supplementing having these defect by DBR of TOC.

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Common Due-Date Assignment and Scheduling on Parallel Machines with Sequence-Dependent Setup Times

  • Kim, Jun-Gyu;Yu, Jae-Min;Lee, Dong-Ho
    • Management Science and Financial Engineering
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    • v.19 no.1
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    • pp.29-36
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    • 2013
  • This paper considers common due-date assignment and scheduling on parallel machines. The main decisions are: (a) deter-mining the common due-date; (b) allocating jobs to machines; and (c) sequencing the jobs assigned to each machine. The objective is to minimize the sum of the penalties associated with common due-date assignment, earliness and tardiness. As an extension of the existing studies on the problem, we consider sequence-dependent setup times that depend on the type of job just completed and on the job to be processed. The sequence-dependent setups, commonly found in various manufacturing systems, make the problem much more complicated. To represent the problem more clearly, a mixed integer programming model is suggested, and due to the complexity of the problem, two heuristics, one with individual sequence-dependent setup times and the other with aggregated sequence-dependent setup times, are suggested after analyzing the characteristics of the problem. Computational experiments were done on a number of test instances and the results are reported.

Scheduling Generation Model on Parallel Machines with Due Date and Setup Cost Based on Deep Learning (납기와 작업준비비용을 고려한 병렬기계에서 딥러닝 기반의 일정계획 생성 모델)

  • Yoo, Woosik;Seo, Juhyeok;Lee, Donghoon;Kim, Dahee;Kim, Kwanho
    • The Journal of Society for e-Business Studies
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    • v.24 no.3
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    • pp.99-110
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    • 2019
  • As the 4th industrial revolution progressing, manufacturers are trying to apply intelligent information technologies such as IoT(internet of things) and machine learning. In the semiconductor/LCD/tire manufacturing process, schedule plan that minimizes setup change and due date violation is very important in order to ensure efficient production. Therefore, in this paper, we suggest the deep learning based scheduling generation model minimizes setup change and due date violation in parallel machines. The proposed model learns patterns of minimizing setup change and due date violation depending on considered order using the amount of historical data. Therefore, the experiment results using three dataset depending on levels of the order list, the proposed model outperforms compared to priority rules.

Application of Genetic Algorithms to a Job Scheduling Problem (작업 일정계획문제 해결을 위한 유전알고리듬의 응용)

  • ;;Lee, Chae Y.
    • Journal of the Korean Operations Research and Management Science Society
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    • v.17 no.3
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    • pp.1-12
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    • 1992
  • Parallel Genetic Algorithms (GAs) are developed to solve a single machine n-job scheduling problem which is to minimize the sum of absolute deviations of completion times from a common due date. (0, 1) binary scheme is employed to represent the n-job schedule. Two selection methods, best individual selection and simple selection are examined. The effect of crossover operator, due date adjustment mutation and due date adjustment reordering are discussed. The performance of the parallel genetic algorithm is illustrated with some example problems.

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A study on Flow Shop Scheduling Problems with Different Weighted Penalties and a Common Due Date (차별 벌과금과 공통납기를 고려한 흐름작업 일정 계획에 관한 연구)

  • Lee, Jeong-Hwan;No, In-Gyu
    • Journal of Korean Society for Quality Management
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    • v.19 no.2
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    • pp.125-132
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    • 1991
  • This paper is concerned with the flow shop scheduling problems considering different weighted penalty costs for earliness and lateness, and a common due date. The objective of the paper is to develop an efficient heuristic scheduling algorithm for minimizing total penalty costs and for determining the optimal common due date. The positional weight index and, the product sum method are used. A numerical example is given for illustrating the proposed algorithm.

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Common Due-Date Assignment and Scheduling with Sequence-Dependent Setup Times: a Case Study on a Paper Remanufacturing System

  • Kim, Jun-Gyu;Kim, Ji-Su;Lee, Dong-Ho
    • Management Science and Financial Engineering
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    • v.18 no.1
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    • pp.1-12
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    • 2012
  • In this paper, we report a case study on the common due-date assignment and scheduling problem in a paper remanufacturing system that produces corrugated cardboards using collected waste papers for a given set of orders under the make-to-order (MTO) environment. Since the system produces corrugated cardboards in an integrated process and has sequence-dependent setups, the problem considered here can be regarded as common due-date assignment and sequencing on a single machine with sequence-dependent setup times. The objective is to minimize the sum of the penalties associated with due-date assignment, earliness, and tardiness. In the study, the earliness and tardiness penalties were obtained from inventory holding and backorder costs, respectively. To solve the problem, we adopted two types of algorithms: (a) branch and bound algorithm that gives the optimal solutions; and (b) heuristic algorithms. Computational experiments were done on the data generated from the case and the results show that both types of algorithms work well for the case data. In particular, the branch and bound algorithm gave the optimal solutions quickly. However, it is recommended to use the heuristic algorithms for large-sized instances, especially when the solution time is very critical.

Dynamic Lot-Sizing Model with Production Time Windows under Nonspeculative Cost Structure (비모의성 비용구조와 생산납기구간 환경에서의 동적롯사이징 모델)

  • Hwang Hark-Chin
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2006.05a
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    • pp.133-136
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    • 2006
  • We consider dynamic lot-sizing model with production time windows where each of n demands has earliest due date and latest due date and it must be satisfied during the given time window. For the case of nonspeculative cost structure, an O(nlog n) time procedure is developed and it is shown to run in O(n) when demands come in the order of latest due dates.

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Simulation-based Delivery Date Determination Algorithm (효율적 제조자원의 활용을 고려한 생산일정 및 납기일 결정기법)

  • 박창규
    • Korean Management Science Review
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    • v.17 no.2
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    • pp.125-134
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    • 2000
  • Keeping the promised delivery date for a customer order is crucial for a company to promote customer satisfaction and generate further businesses. For this, a company should be able to quote the delivery date that can be achieved with the capacity available on the shop floor. In a dynamic make-to-order manufacturing environment, the problem of determining a delivery date for an incoming order with consideration of resource capacity, workload, and finished-product inventory can hardly be solved by an analytical solution procedure. This paper considers a situation in which a delivery date for a customer order is determined based on a job schedule, and presents the SimTriD algorithm that provides the best scheduling for determining a delivery date of customer order through the job schedule that efficiently utilizes manufacturing resources with consideration of interacting factors such as resource utilization, finished-product inventory, and due date.

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