The Car Resequencing Problem with Pull-Off Tables

The car sequencing problem determines sequences of different car models launched down a mixed-model assembly line. To avoid work overloads of workforce, car sequencing restricts the maximum occurrence of labor-intensive options, e.g., a sunroof, by applying sequencing rules. We consider this problem in a resequencing context, where a given number of buffers (denoted as pull-off tables) is available for rearranging a stirred sequence. The problem is formalized and suited solution procedures are developed. A lower bound and a dominance rule are introduced which both reduce the running time of our graph approach. Finally, a real-world resequencing setting is investigated.mixed-model assembly line, car sequencing, resequencing

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

Customers 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 stop 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, powertrain, color, and special equipment

Modeling and Solution Methodologies for Mixed-Model Sequencing in Automobile Industry

The global competitive environment leads companies to consider how to produce high-quality products at a lower cost. Mixed-model assembly lines are often designed such that average station work satisfies the time allocated to each station, but some models with work-intensive options require more than the allocated time. Sequencing varying models in a mixed-model assembly line, mixed-model sequencing (MMS), is a short-term decision problem that has the objective of preventing line stoppage resulting from a station work overload. Accordingly, a good allocation of models is necessary to avoid work overload. The car sequencing problem (CSP) is a specific version of the MMS that minimizes work overload by controlling the sequence of models. In order to do that, CSP restricts the number of work-intensive options by applying capacity rules. Consequently, the objective is to find the sequence with the minimum number of capacity rule violations. In this dissertation, we provide exact and heuristic solution approaches to solve different variants of MMS and CSP. First, we provide five improved lower bounds for benchmark CSP instances by solving problems optimally with a subset of options. We present four local search metaheuristics adapting efficient transformation operators to solve CSP. The computational experiments show that the Adaptive Local Search provides a significant advantage by not requiring tuning on the operator weights due to its adaptive control mechanism. Additionally, we propose a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provide improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. We also provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high-quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances. To the best of our knowledge, this is the first study that considers stochastic failures of products in MMS. Moreover, we propose a two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. We present a bi-objective evolutionary optimization algorithm, a two-stage bi-objective local search algorithm, and a hybrid local search integrated evolutionary optimization algorithm to tackle the proposed problem. Numerical experiments over a case study show that while the hybrid algorithm provides a better exploration of the Pareto front representation and more reliable solutions in terms of waiting time of failed vehicles, the local search algorithm provides more reliable solutions in terms of work overload objective. Finally, dynamic reinsertion simulations are executed over industry-inspired instances to assess the quality of the solutions. The results show that integrating the reinsertion process in addition to considering vehicle failures can keep reducing the work overload by around 20\% while significantly decreasing the waiting time of the failed vehicles

ALGORITHMS FOR CORRECTING NEXT GENERATION SEQUENCING ERRORS

The advent of next generation sequencing technologies (NGS) generated a revolution in biological research. However, in order to use the data they produce, new computational tools are needed. Due to significantly shorter length of the reads and higher per-base error rate, more complicated approaches are employed and still critical problems, such as genome assembly, are not satisfactorily solved. We therefore focus our attention on improving the quality of the NGS data. More precisely, we address the error correction issue. The current methods for correcting errors are not very accurate. In addition, they do not adapt to the data. We proposed a novel tool, HiTEC, to correct errors in NGS data. HiTEC is based on the suffix array data structure accompanied by a statistical analysis. HiTEC’s accuracy is significantly higher than all previous methods. In addition, it is the only tool with the ability of adjusting to the given data set. In addition, HiTEC is time and space efficient

DEVELOPMENT AND APPLICATION OF MASS SPECTROMETRY-BASED PROTEOMICS TO GENERATE AND NAVIGATE THE PROTEOMES OF THE GENUS POPULUS

Historically, there has been tremendous synergy between biology and analytical technology, such that one drives the development of the other. Over the past two decades, their interrelatedness has catalyzed entirely new experimental approaches and unlocked new types of biological questions, as exemplified by the advancements of the field of mass spectrometry (MS)-based proteomics. MS-based proteomics, which provides a more complete measurement of all the proteins in a cell, has revolutionized a variety of scientific fields, ranging from characterizing proteins expressed by a microorganism to tracking cancer-related biomarkers. Though MS technology has advanced significantly, the analysis of complicated proteomes, such as plants or humans, remains challenging because of the incongruity between the complexity of the biological samples and the analytical techniques available. In this dissertation, analytical methods utilizing state-of-the-art MS instrumentation have been developed to address challenges associated with both qualitative and quantitative characterization of eukaryotic organisms. In particular, these efforts focus on characterizing Populus, a model organism and potential feedstock for bioenergy. The effectiveness of pre-existing MS techniques, initially developed to identify proteins reliably in microbial proteomes, were tested to define the boundaries and characterize the landscape of functional genome expression in Populus. Although these approaches were generally successful, achieving maximal proteome coverage was still limited by a number of factors, including genome complexity, the dynamic range of protein identification, and the abundance of protein variants. To overcome these challenges, improvements were needed in sample preparation, MS instrumentation, and bioinformatics. Optimization of experimental procedures and implementation of current state-of-the-art instrumentation afforded the most detailed look into the predicted proteome space of Populus, offering varying proteome perspectives: 1) network-wide, 2) pathway-specific, and 3) protein-level viewpoints. In addition, we implemented two bioinformatic approaches that were capable of decoding the plasticity of the Populus proteome, facilitating the identification of single amino acid polymorphisms and generating a more accurate profile of protein expression. Though the methods and results presented in this dissertation have direct implications in the study of bioenergy research, more broadly this dissertation focuses on developing techniques to contend with the notorious challenges associated with protein characterization in all eukaryotic organisms

Whole genome assembly and gap closure of the toxic bloom-forming cyanobacterium Anabaena sp. strain 90

Anabaena is a common member of the phytoplankton in lakes, reservoirs and ponds throughout the world. This is a filamentous, nitrogen-fixing cyanobacterial genus and is frequently present in the lakes of Finland. Anabaena sp. strain 90 was isolated from Lake Vesijärvi and produces microcystins, anabaenopeptilides and anabaenopeptins. A whole genome shotgun sequencing project was undertaken to obtain the complete genome of this organism in order to better understand the physiology and environmental impact of toxic cyanobacteria. This work describes the genome assembly and finishing, the genome structure, and the results of intensive computational analysis of the Anabaena sp. strain 90 genome. Altogether 119,316 sequence reads were generated from 3 genomic libraries with 2, 6 and 40 kb inserts from high throughput Sanger sequencing. The software package Phred/Phrap/Consed was used for whole genome assembly and finishing. A combinatorial PCR method was used to establish relationships between remaining contigs after thorough scaffolding and gap-filling. The final assembly results show that there is a single 4.3 Mb circular chromosome and 4 circular plasmids with sizes of 820, 80, 56 and 20 kb respectively. Together, these 4 plasmids comprise nearly one-fifth of the total genome. Genomic variations in the form of 79 single nucleotide polymorphisms and 3 sequence indels were identified from the assembly results. Sequence analysis revealed that 7.5 percent of the Anabaena sp. strain 90 genome consists of repetitive DNA elements. The genome sequence of Anabaena sp. strain 90 provides a more solid basis for further studies of bioactive compound production, photosynthesis, nitrogen fixation and akinete formation in cyanobacteria

Otomotiv montaj hatlarında montaj öncesi ara stok içeriğinin belirlenmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Karma modelli montaj hatlarında hattın düzgün ilerlemesi, üretim kısıtları ve müşteri talepleri doğrultusunda belirlenmiş olan çizelgelenmiş sıraya uyuma bağlıdır. Fakat çizelgelenmiş sıra kasıtlı ve kasıtsız araç sıra değişiklikleri nedeniyle bozulabilmektedir. Bozulan sırayı onarmak için boyahane ile montaj departmanları arasında araç yeniden sıralanma ara stoğu bulunmaktadır. Bu ara stok bozulan sırayı üç farklı şekilde onarmaktadır: Bunlar (i) araç sıralarının değiştirilmesi, araçların yeniden sıralanması, (ii) hatalı araçların ara stokta tutulan araçlarla değiştirilmesi, (iii) son olarak da ara stok kapasitesi ve araç hatalarına bağlı olarak yeniden sıralamayla onarmanın mümkün olmadığı durumda ara stoktan montaj hattına araç beslenmesidir. Tezde, boyahanede rastgele oluşan hataları göz önünde bulunduran iki-aşamalı stokastik programlama modeli geliştirilmiştir. Modelin ilk aşamasında, bozulan sıranın onarılması için yeniden sıralama ara stoğunda tutulması gereken optimal model-renk kombinasyonlarına ait miktarlar belirlenmektedir. İkinci aşamada ise boyahanede oluşan hatalar gözlendikten sonra, araçların montaj giriş sıralarına karar verilmektedir. Problemin çözümü ara stok depolama sistemine bağlı olduğundan, otomatik depolama ve çekme sistemleri (AS/RS) ve yeniden sıralama hatları (mix-bank) için iki ayrı model kurulmuştur. Geliştirilen iki-aşamalı stokastik modelin çözümü örneklem ortalaması yaklaşımı (SAA) ile yapılmış, ara stokta tutulması gereken optimal model-renk kombinasyonlarının miktarları belirlenmiştir. Ayrıca hata oranı, ara stok kapasitesi, araçların boya giriş sıralarının çizelgelenmiş sıraya uyumu gibi problem parametrelerinin çözüme etkisini incelemek için bir sayısal çalışma yapılmıştır. Boya hatalarına bağlı araç sıra değişiklikleri anlık karar vermeye dayandığı için, araç üreticilerinin kolaylıkla uygulayabileceği kural tabanlı, sezgisel alternatif bir model kurulmuştur. Geliştirilmiş olan kural tabanlı model matematiksel modele yakın sonuçlar vermiştir. Son olarak, AS/RS ve yeniden sıralama hatlarında karşılaşılan büyük ölçekli problemleri de çözebilmek için, sunulan model genetik algoritma kullanılarak geliştirilmiştir.In mixed model assembly lines, smooth operation of the line depends on adherence to the scheduled sequence which is determined according to production constraints and customer demand. However, the scheduled sequence is scrambled due to intentional and unintentional sequence alterations. A resequencing buffer between paint and final assembly is located to restore the altered sequence. Restoring the altered sequence requires three distinct operations of this buffer: (i) Changing the positions of vehicles (i.e., resequencing), (ii) replacing spare vehicles with paint defective vehicles, (iii) lastly inserting spare vehicles to final assembly from the buffer when restoring the altered sequence is not possible by resequencing due to paint defects and limited buffer capacity. In this thesis, a two-stage stochastic programming model which considers the stochastic nature of paint defect occurrences is developed. In the first stage of the model, optimal number of model-color types placed into resequencing buffer to restore the scheduled sequence is determined. In the second stage after defect occurrences, the assembly entrance sequences of the vehicles are decided. Since the solution of the problem depends on the resequencing buffer type, two different models for automated storage and retrieval system (AS/RS) and mix-bank are built. The developed two-stage stochastic program is solved by sample average approximation (SAA) algorithm to and the optimal number of model-color types to be placed in the buffer is found. Also a numerical study is performed to investigate the problem parameters to the solution such as paint defect rate, capacity of buffer, adherence ratio of vehicles entering paint shop to scheduled sequence. Since vehicle resequencing due to the paint defects requires instant decision making, another heuristic rule based model to resequence vehicles easily by car manufacturers is developed. The proposed heuristic model performs as good as mathematical model. Lastly, to solve the large scale problems for both AS/RS and mix-bank resequencing buffers the purposed model is enhanced with genetic algorithm

Graphical pangenomics

Completely sequencing genomes is expensive, and to save costs we often analyze new genomic data in the context of a reference genome. This approach distorts our image of the inferred genome, an effect which we describe as reference bias. To mitigate reference bias, I repurpose graphical models previously used in genome assembly and alignment to serve as a reference system in resequencing. To do so I formalize the concept of a variation graph to link genomes to a graphical model of their mutual alignment that is capable of representing any kind of genomic variation, both small and large. As this model combines both sequence and variation information in one structure it serves as a natural basis for resequencing. By indexing the topology, sequence space, and haplotype space of these graphs and developing generalizations of sequence alignment suitable to them, I am able to use them as reference systems in the analysis of a wide array of genomic systems, from large vertebrate genomes to microbial pangenomes. To demonstrate the utility of this approach, I use my implementation to solve resequencing and alignment problems in the context of Homo sapiens and Saccharomyces cerevisiae. I use graph visualization techniques to explore variation graphs built from a variety of sources, including diverged human haplotypes, a gut microbiome, and a freshwater viral metagenome. I find that variation aware read alignment can eliminate reference bias at known variants, and this is of particular importance in the analysis of ancient DNA, where existing approaches result in significant bias towards the reference genome and concomitant distortion of population genetics results. I validate that the variation graph model can be applied to align RNA sequencing data to a splicing graph. Finally, I show that a classical pangenomic inference problem in microbiology can be solved using a resequencing approach based on variation graphs.Wellcome Trust PhD fellowshi

Rescheduling unrelated parallel machines with total flow time and total disruption cost criteria

In this paper, we consider a rescheduling problem where a set of jobs has already been assigned to unrelated parallel machines. When a disruption occurs on one of the machines, the affected jobs are rescheduled, considering the efficiency and the schedule deviation measures. The efficiency measure is the total flow time, and the schedule deviation measure is the total disruption cost caused by the differences between the initial and current schedules. We provide polynomial-time solution methods to the following hierarchical optimization problems: minimizing total disruption cost among the minimum total flow time schedules and minimizing total flow time among the minimum total disruption cost schedules. We propose exponentialtime algorithms to generate all efficient solutions and to minimize a specified function of the measures. Our extensive computational tests on large size problem instances have revealed that our optimization algorithm finds the best solution by generating only a small portion of all efficient solutions