Modeling sequence scrambling and related phenomena in mixed-model production lines

2014 ◽  
Vol 237 (1) ◽  
pp. 177-195 ◽  
Author(s):  
Gábor Rudolf ◽  
Nilay Noyan ◽  
Vincent Giard
Keyword(s):  
Mixed Model ◽  
Production Lines ◽  
Sequence Scrambling

scholarly journals SEQUENCING IN A NON-PERMUTATION FLOWSHOP WITH CONSTRAINED BUFFERS: APPLICABILITY OF GENETIC ALGORITH VERSUS CONSTRAINT LOGIC PROGRAMMING

2008 ◽  
pp. 69-78
Author(s):  
Gerrit Farber ◽  
Said Salhi ◽  
Anna M. Coves Moreno
Keyword(s):  
Genetic Algorithm ◽  
Logic Programming ◽  
Production Line ◽  
Mixed Model ◽  
Hybrid Approach ◽  
Constraint Logic Programming ◽  
Arbitrary Sequence ◽  
Production Lines ◽  
Permutation Flowshop ◽  
The Difference

Mixed model production lines consider more than one model being processed on the same production line in an arbitrary sequence. Nevertheless, the majority of publications in this area are limited to solutions which determine the job sequence before the jobs enter the line and maintains it without interchanging jobs until the end of the production line, which is known as permutation flowshop. This paper considers a nonpermutation flowshop. Resequencing is permitted where stations have access to intermediate or centralized resequencing buffers. The access to the buffers is restricted by the number of available buffer places and the physical size of the products. Two conceptually different approaches are presented in order to solve the problem. The first approach is a hybrid approach, using Constraint Logic Programming (CLP), whereas the second one is a Genetic Algorithm (GA). Improvements that come with the introduction of constrained resequencing buffers are highlighted. Characteristics such as the difference between the intermediate and the centralized case are analyzed, and the special case of semi dynamic demand is studied. Finally, recommendations are presented for the applicability of the hybrid approach, using CLP, versus the Genetic Algorithm.

scholarly journals Multiobjective Fuzzy Mixed Assembly Line Sequencing Optimization Model

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Farzad Tahriri ◽  
Siti Zawiah Md Dawal ◽  
Zahari Taha
Keyword(s):  
Optimization Model ◽  
Assembly Line ◽  
Mixed Model ◽  
Job Shop ◽  
Setup Time ◽  
Assembly Lines ◽  
Production Lines ◽  
Travelling Time ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

It can be deduced from previous studies that there exists a research gap in assembly line sequencing optimization model for mixed-model production lines. In particular, there is a lack of studies which focus on the integration between job shop and assembly lines using fuzzy techniques. Hence, this paper is aimed at addressing the multiobjective mixed-model assembly line sequencing problem by integrating job shop and assembly production lines for factories with modular layouts. The primary goal is to minimize the make-span, setup time, and cost simultaneously in mixed-model assembly lines. Such conflicting goals arise when switching between different products. A genetic algorithm (GA) approach is used to solve this problem, in which trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data.

scholarly journals AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line

2021 ◽  
Author(s):  
Maximilian Stauder ◽  
Niklas Kühl
Keyword(s):  
Automotive Industry ◽  
Mixed Model ◽  
Manufacturing Sector ◽  
Early Stage ◽  
Classification Model ◽  
Control Mechanisms ◽  
Body Type ◽  
Algorithm Selection ◽  
Special Equipment ◽  
Sequence Scrambling

AbstractCustomers in the manufacturing sector, especially in the automotive industry, have a high demand for individualized products at price levels comparable to traditional mass-production. The contrary objectives of providing a variety of products and operating at minimum costs have introduced a high degree of production planning and control mechanisms based on a stable order sequence for mixed-model assembly lines. A major threat to this development is sequence scrambling, triggered by both operational and product-related root causes. Despite the introduction of Just-in-time and fixed production times, the problem of sequence scrambling remains partially unresolved in the automotive industry. Negative downstream effects range from disruptions in the Just-in-sequence supply chain, to a discontinuation of the production process. A precise prediction of sequence deviations at an early stage allows the introduction of counteractions to stabilize the sequence before disorder emerges. While procedural causes are widely addressed in research, the work at hand requires a different perspective involving a product-related view. Built on unique data from a real-world global automotive manufacturer, a supervised classification model is trained and evaluated. This includes all the necessary steps to design, implement, and assess an AI-artifact, as well as data gathering, preprocessing, algorithm selection, and evaluation. To ensure long-term prediction stability, we include a continuous learning module to counter data drifts. We show that up to 50% of the major deviations can be predicted in advance. However, we do not consider any process-related information, such as machine conditions and shift plans, but solely focus on the exploitation of product features like body type, power train, color, and special equipment.

scholarly journals Sequencing for Mixed-model Production Lines

1978 ◽  
Vol 44 (518) ◽  
pp. 191-197
Author(s):  
Kenjiro OKAMURA ◽  
Hajime YAMASHINA ◽  
Hideo OHNO
Keyword(s):  
Mixed Model ◽  
Production Lines

Computer Simulation for Mixed-Model Production Lines

1973 ◽  
Vol 20 (3) ◽  
pp. 341-348 ◽  
Author(s):  
J. L. C. Macaskill
Keyword(s):  
Computer Simulation ◽  
Mixed Model ◽  
Production Lines

Effects of Aging on the Subcortical Encoding of Stop Consonants

2020 ◽  
Vol 29 (3) ◽  
pp. 391-403
Author(s):  
Dania Rishiq ◽  
Ashley Harkrider ◽  
Cary Springer ◽  
Mark Hedrick
Keyword(s):  
Older Adults ◽  
Repeated Measures ◽  
Mixed Model ◽  
Auditory Brainstem ◽  
Auditory Brainstem Responses ◽  
Aging Effects ◽  
Formant Frequency ◽  
Place Of Articulation ◽  
Second Formant ◽  
Effects Of Aging

Purpose The main purpose of this study was to evaluate aging effects on the predominantly subcortical (brainstem) encoding of the second-formant frequency transition, an essential acoustic cue for perceiving place of articulation. Method Synthetic consonant–vowel syllables varying in second-formant onset frequency (i.e., /ba/, /da/, and /ga/ stimuli) were used to elicit speech-evoked auditory brainstem responses (speech-ABRs) in 16 young adults ( M age = 21 years) and 11 older adults ( M age = 59 years). Repeated-measures mixed-model analyses of variance were performed on the latencies and amplitudes of the speech-ABR peaks. Fixed factors were phoneme (repeated measures on three levels: /b/ vs. /d/ vs. /g/) and age (two levels: young vs. older). Results Speech-ABR differences were observed between the two groups (young vs. older adults). Specifically, older listeners showed generalized amplitude reductions for onset and major peaks. Significant Phoneme × Group interactions were not observed. Conclusions Results showed aging effects in speech-ABR amplitudes that may reflect diminished subcortical encoding of consonants in older listeners. These aging effects were not phoneme dependent as observed using the statistical methods of this study.

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