Stable schedule matching under revealed preference

Baiou and Balinski (Math. Oper. Res., 27 (2002) 485) studied schedule matching where one determines the partnerships that form and how much time they spend together, under the assumption that each agent has a ranking on all potential partners. Here we study schedule matching under more general preferences that extend the substitutable preferences in Roth (Econometrica 52 (1984) 47) by an extension of the revealed preference approach in Alkan (Econom. Theory 19 (2002) 737). We give a generalization of the GaleShapley algorithm and show that some familiar properties of ordinary stable matchings continue to hold. Our main result is that, when preferences satisfy an additional property called size monotonicity, stable matchings are a lattice under the joint preferences of all agents on each side and have other interesting structural properties

A method to assess assembly complexity of industrial products in early design phase

Complexity is one of the factors, inducing high cost, operational issues, and increased lead time for product realization and continues to pose challenges to manufacturing systems. One solution to reduce the negative impacts of complexity is its assessment, which can help designers to compare and rationalize various designs that meet the functional requirements. In this paper, a systemic approach is proposed to assess complexity of a product's assembly. The approach is based on Hückel's molecular orbital theory and defines complexity as a combination of both the complexity of product entities and their topological connections. In this model, the complexity of product entities (i.e., components and liaisons) is defined as the degree to which the entity comprises structural characteristics that lead to challenges during handling or fitting operations. The characterization of entity complexities is carried out based on the widely used DFA principles. Moreover, the proposed approach is tested on two case studies from electronics industry for its validity. The results showed that the approach can be used at initial design stages to improve both the quality and assemblability of products by reducing their complexity and accompanying risks

Pilot To Full-Scale Production: A Battery Module Assembly Case Study

Electric vehicles are currently on the rise due to environmental and legal concerns. Furthermore, improvements made in battery assembly steadily boosts the efficiency of electric vehicles. A well-prevalent method to overcome the uncertainties that emerge from the ever-changing battery technology, is to assemble products using pilot production lines. However, literature pertaining to the scale-up of pilot production lines for full scale production is scarce. Therefore, in this paper, potential scale-up scenarios for battery module assembly line are proposed in a discrete event simulation software and results are compared. Furthermore, the benefits of the proposed method are discussed with a test case

CARBOXYLATED-CELLULOSE NANOCRYSTALS PRODUCED FROM LIGNOCELLULOSIC SOURCES AND USED AS BARRIER COATING FOR PET FILMS

The obtainment of Cellulose nanocrystal (CNC) from different cellulosic sources recently gained a great attention due to their physical and mechanical properties. CNC can be extracted from variety of bio-based and renewable sources, such as wood, cotton, bacterial cellulose, tunicate cellulose and softwood cellulose and used as reinforced nanocomposites, functional materials and oxygen-barrier layers. The most important factor affecting the nanocellulose production is the relative proportion of cellulose, hemicellulose, and lignin in the source material. The extraction of CNC from cellulosic materials started with a pre-treatment of biomass involving the complete or partial removal of hemicelluloses, lignin, etc. and isolation of cellulose component. After the separation of matrix materials, controlled hydrolysis of the cellulosic fibers is performed in order to produce colloidal suspensions of cellulose crystals. Using of ammonium persulfate (APS) has recently achieved the obtainment of CNC from different cellulose sources without any pre-treatment. APS, a strong oxidizing agent with high water solubility, is able to produce carboxylated CNC having high crystallinity and active carboxyl groups. The conversion of the carboxylic acid groups to the carboxylate form offers active sites for surface modification and templates for the synthesis of nanoparticles. Incorporation of carboxylated–CNC as a functional layer on different substrates have enhanced the barrier (oxygen permeability), mechanical, optical, and antifog properties of the nanocomposites. The aim of this work was to obtain the CNC from two lignocellulosic sources and to characterize them in terms of morphology, crystallinity, charge density and coating properties in order to use CNC as barrier coating for PET films

Complexity in manufacturing systems and its measures: a literature review

Complexity in manufacturing systems still remains a challenge and leads to operational issues and increased production cost. In this paper, drivers of complexity and typical symptoms of complex manufacturing systems are identified. A comprehensive review of studies published within the last two decades to assess manufacturing system complexity are presented. The key contributions of this review are: 1) a classification of complexity assessment methods based on perceived complexity symptoms; 2) a comprehensive review of assessment methods with cross-evaluation to identify appropriate use based on available data; 3) recommendations for the wider academic and industrial community, based on research trends identified in the literature, as to how complexity assessment should be addressed in the future. It is concluded that the assessment of complexity is necessary so that it can be controlled effectively, however the industry suffers from a lack of practical tools to support in this endeavour

A Lightweight Approach for Human Factor Assessment in Virtual Assembly Designs: An Evaluation Model for Postural Risk and Metabolic Workload

© 2016 The Authors. The assessment and optimisation of postural stress and physical fatigue can be challenging and is typically conducted only after the design of manual operations has been finalised. However early assessment of manual operations and identification of critical factors that are deemed outside of an appropriate envelope can avoid the time and costs often associated with re-designing machines and layout for operator work processes. This research presents a low cost software solution based on a simplified skeleton model that uses operator position and workload data extracted from a simulation model used for virtual manufacturing process planning. The developed approach aims to assess postural stress and physical fatigue scores of assembly operations, as they are being designed and simulated virtually. The model is based on the Automotive Assembly Worksheet and the Garg's metabolic rate prediction model. The proposed research focuses on the integration of virtual process planning, ergonomic and metabolic analysis tools, and on automating human factor assessment to enable optimisation of assembly operations and workload capabilities at early design stage

Design Evaluation of Automated Manufacturing Processes Based on Complexity of Control Logic

Complexity continues to be a challenge in manufacturing systems, resulting in ever-inflating costs, operational issues and increased lead times to product realisation. Assessing complexity realizes the reduction and management of complexity sources which contributes to lowering associated engineering costs and time, improves productivity and increases profitability. This paper proposes an approach for evaluating the design of automated manufacturing processes based on the structural complexity of the control logic. Six complexity indices are introduced and formulated: Coupling, Restrictiveness, Diameter, Branching, Centralization, and Uncertainty. An overall Logical Complexity Index (CL) which combines all of these indices is developed and demonstrated using a simple pick and place automation process. The results indicate that the proposed approach can help design automation logics with the least complexity and compare alternatives that meet the requirements during initial design stages

A Framework for Pilot Line Scale-up using Digital Manufacturing

Pilot lines are essential test-beds for process and product validation before the establishment of production lines. However, there is a lack of well-defined methodology for pilot line scale-up. To better support this transition, Virtual Models can be integrated with Discrete-Event Simulation (DES) models for potential production-line configurations. However, the validation of the developed models is hardly possible due to the absence of a physical counterpart. Therefore, this paper proposes a framework to increase the accuracy of the DES scale-up models with Virtual Modelling tools and Ontology. Subsequently, a test-case is used to explain the concept

SEGMENTATION OF LANDSAT-8 IMAGES FOR BURNED AREA DETECTION WITH DEEP LEARNING

Fires damage nature and living beings. Detection of this damage is important for future. In this study, it was aimed to determine burned areas. For this purpose, Landsat-8 images and U-Net model were used. Python language was preferred. Band combinations 7,5,4; 5,3,7; 5,4,3; 4,3,2; 4,3,2,5 and 2,3,4,5,6,7 have been tried. Train and test processes were carried out separately for each band combination. After the train and test processes were completed, a probability result consisting of values between 0-1 was obtained. Then, a threshold value was used. Thus, binary results consisting of 0 and 1 values were obtained. Three different values were preferred for the threshold: 0.1, 0.5 and 0.9. Thus, the effect of threshold value selection on the test results was examined. The prediction results were evaluated using the masks. For this, general accuracy, recall, precision, F1-score and Jaccard score metrics were used. Recall, precision, and F1-score values were calculated for both burned areas and unburned areas. In addition, minimum, maximum, mean, and standard deviation values were calculated for each metric. When the results are examined, it is seen that the model gives better results when the threshold value is 0.1 and 0.5. Among the band combinations, it is seen that the 7,5,4 combination gave better results than the others. For this band combination, the highest mean accuracy is 0.9743 with the 0.5 threshold value. For this threshold mean recall, mean precision and mean F1-score for burned areas are 0.7203, 0.8411 and 0.7601, respectively. And Jaccard score is 0.6328

Hydrogen Fuel Cell Pick and Place Assembly Systems: Heuristic Evaluation of Reconfigurability and Suitability

Proton Exchange Membrane Fuel Cells (PEMFCs) offer numerous advantages over combustion technology but they remain economically uncompetitive except for in niche applications. A portion of this cost is attributed to a lack of assembly expertise and the associated risks. To solve this problem, this research investigates the assembly systems that do exist for this product and systematically decomposes them into their constituent components to evaluate reconfigurability and suitability to product. A novel method and set of criteria are used for evaluation taking inspiration from heuristic approaches for evaluating manufacturing system complexity. It is proposed that this can be used as a support tool at the design stage to meet the needs of the product while having the capability to accept potential design changes and variants for products beyond the case study presented in this work. It is hoped this work develops a new means to support in the design of reconfigurable systems and form the foundation for fuel cell assembly best practice, allowing this technology to reduce in cost and find its way into a commercial space