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

Mixed-model Sequencing with Reinsertion of Failed Vehicles: A Case Study for Automobile Industry

In the automotive industry, some vehicles, failed vehicles, cannot be produced according to the planned schedule due to some reasons such as material shortage, paint failure, etc. These vehicles are pulled out of the sequence, potentially resulting in an increased work overload. On the other hand, the reinsertion of failed vehicles is executed dynamically as suitable positions occur. In case such positions do not occur enough, either the vehicles waiting for reinsertion accumulate or reinsertions are made to worse positions by sacrificing production efficiency. This study proposes a bi-objective two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. Moreover, an evolutionary optimization algorithm, a two-stage local search algorithm, and a hybrid approach are developed. Numerical experiments over a case study show that while the hybrid algorithm better explores the Pareto front representation, the local search algorithm provides more reliable solutions regarding work overload objective. Finally, the results of the dynamic reinsertion simulations show that we can decrease the work overload by ~20\% while significantly decreasing the waiting time of the failed vehicles by considering vehicle failures and integrating the reinsertion process into the mixed-model sequencing problem.Comment: 26 pages, 6 figures, 5 table

INVESTIGATION OF CLASSROOM TEACHER CANDIDATES’ COGNITIVE STRUCTURES ON SOME BASIC SCIENCE CONCEPTS

In this study, it was aimed to investigate the cognitive structures of classroom teacher candidates on some basic science concepts. Word association test (WAT) technique was used to gather data. Twelve keywords related to basic physics, chemistry, and biology concepts were determined and used in the formation of WAT’s. Forty-three classroom teacher candidates studying at 2nd classes at an education faculty were the participants of this study. Data obtained by WAT were examined by using number of different responses given to each keyword, and by drawing concept maps according to both frequencies and relatedness coefficients. A cut-off point technique was used when drawing the concept maps. Because of this study, it can be said that participants have moderate cognitive structures on the investigated science concepts and their cognitive structure was strongest on chemistry concepts and weakest on biology concepts.   Article visualizations

Mixed-model Sequencing with Stochastic Failures: A Case Study for Automobile Industry

In the automotive industry, the sequence of vehicles to be produced is determined ahead of the production day. However, there are some vehicles, failed vehicles, that cannot be produced due to some reasons such as material shortage or paint failure. These vehicles are pulled out of the sequence, and the vehicles in the succeeding positions are moved forward, potentially resulting in challenges for logistics or other scheduling concerns. This paper proposes a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provides 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. Moreover, we 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.Comment: 30 pages, 9 figure

Coexistence of MACC1 and NM23-H1 dysregulation and tumor budding promise early prognostic evidence for recurrence risk of early-stage colon cancer

© 2017 APMIS. Published by John Wiley & Sons Ltd The tumor-node-metastasis (TNM) classification, the presence of a mucinous component, and signet ring cells are well-known criteria for identifying patients at a high risk for recurrence and determining the therapeutic approach for early-stage colon cancer (eCC). Nevertheless, recurrence can unexpectedly occur in some eCC cases after surgical resection. The aims of the present study were to evaluate the relation of dysregulated MACC1, c-MET, and NM23-H1 expression with the histopathological features of tumors in recurrence formation in eCC cases. A total of 100 sporadic eCC patients without poor prognosis factors were evaluated in this study. The relationship between the altered expression of MACC1, c-MET, and NM23-H1 and pathological microenvironmental features, including the presence of tumor budding and desmoplasia, were assessed. The primary outcomes, including 5-year overall survival (OS) and disease-free survival (DFS), were also measured. Compared with nonrecurrent patients, the expression level of MACC1 was 8.27-fold higher, and NM23-H1 was 11.36-fold lower in patients with recurrence during the 5-year follow-up (p = 0.0345 and p = 0.0301, respectively). In addition, the coexistence of high MACC1 and low NM23-H1 expression and tumor budding was associated with short OS (p < 0.001). We suggest that the combination of reduced NM23-H1, induced MACC1, and the presence of tumor budding are promising biomarkers for the prediction of recurrence and may aid the stratification of patients with stage II colon cancer for adjuvant chemotherapy

A unique cause of hemoperitoneum: spontaneous rupture of a splenic hemangiopericytoma

Non-traumatic hemoperitoneum may be catastrophic if it is not promptly diagnosed and treated. It is critical to identify this clinical picture and treat any active bleeding. We report the first case in the literature (to our knowledge) of spontaneous hemoperitoneum caused by a cystic splenic hemangiopericytoma. Hemangiopericytomas represent a small subset of soft tissue sarcomas. They rarely originate in the spleen as a primary tumor, with only ten cases having been previously described. The difficulty of predicting the prognosis and clinical behavior of these lesions has been repeatedly stressed. The literature concerning this rare and unusual neoplasm is reviewed

Comparison of prognostic scores and surgical approaches to treat spinal metastatic tumors: A review of 57 cases

Surgical treatment of metastatic spinal cord compression with or without neural deficit is controversial. Karnofsky and Tokuhashi scores have been proposed for prognosis of spinal metastasis. Here, we conducted a retrospective analysis of Karnofsky and modified Tokuhashi scores in 57 consecutive patients undergoing surgery for secondary spinal metastases to evaluate the value of these scores in aiding decision making for surgery. Comparison of preoperative Karnofsky and modified Tokuhashi scores with the type of the surgical approach for each patient revealed that both scores not only reliably estimate life expectancy, but also objectively improved surgical decisions. When the general status of the patient is poor (i.e., Karnofsky score less than 40% or modified Tokuhashi score of 5 or greater), palliative treatments and radiotherapy, rather than surgery, should be considered