Assembly line balancing and activity scheduling for customised products manufacturing

Nowadays, end customers require personalized products to match their specific needs. Thus, production systems must be extremely flexible. Companies typically exploit assembly lines to manufacture produces in great volumes. The development of assembly lines distinguished by mixed or multi models increases their flexibility concerning the number of product variants able to be manufactured. However, few scientific contributions deal with customizable products, i.e., produces which can be designed and ordered requiring or not a large set of available accessories. This manuscript proposes an original two-step procedure to deal with the multi-manned assembly lines for customized product manufacturing. The first step of the procedure groups the accessories together in clusters according to a specific similarity index. The accessories belonging to a cluster are typically requested together by customers and necessitate a significant mounting time. Thus, this procedure aims to split accessories belonging to the same cluster to different assembly operators avoiding their overloads. The second procedure step consists of an innovative optimization model which defines tasks and accessory assignment to operators. Furthermore, the developed model defines the activity time schedule in compliance with the task precedencies maximizing the operator workload balance. An industrial case study is adopted to test and validate the proposed procedure. The obtained results suggest superior balancing of such assembly lines, with an average worker utilization rate greater than 90%. Furthermore, in the worst case scenario in terms of customer accessories requirement, just 4 line operators out of 16 are distinguished by a maximum workload greater than the cycle time

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously. In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems

The 4.2 ka event in the central Mediterranean: new data from a Corchia speleothem (Apuan Alps, central Italy)

Abstract. We present new data on the 4.2 ka event in the central Mediterranean from Corchia Cave (Tuscany, central Italy) stalagmite CC27. The stalagmite was analyzed for stable isotopes (δ13C and δ18O) and trace elements (Mg, U, P, Y), with all proxies showing a coherent phase of reduced cave recharge between ca. 4.5 and 4.1 ka BP. Based on the current climatological data on cyclogenesis, the reduction in cave recharge is considered to be associated with the weakening of the cyclone center located in the Gulf of Genoa in response to reduced advection of air masses from the Atlantic during winter. These conditions, which closely resemble a positive North Atlantic Oscillation (NAO) type of configuration, are associated with cooler and wetter summers with reduced sea warming, which reduced the western Mediterranean evaporation during autumn–early winter, further reducing precipitation

Food Product Traceability by Using Automated Identification Technologies

Part 7: Perceptional SystemsInternational audienceFood product traceability from harvesting, through food processing to the final food product and through the retailer to the end consumer is a significant process that has to ensure food quality and safety. The traceability enables the end consumer to get information from all previous stages of the food product, leading back to the food origin. In this way, the consumer can get more information on the specific product, and thus make a decision on buying the product that suits his needs best. In each stage of the food product transformation, important data are generated for the subsequent chain participants. Every participant should have access to certain data of interest to them. This can be achieved by using automated identification technologies, like RFID (Radio Frequency IDentification) and two-dimensional barcode, which allow faster data acquisition, recording and reading processes than the traditional means, and provide up-to-date information in each product stage. Furthermore, these technologies allow the possibility to record large amounts of data for each specific product, and interconnect all the data in a database. This paper discusses the process of providing traceability of food products, recording, transmitting and reading of significant data in specific stages of food product chain, with the application of automated identification technologies, including the possibility of obtaining additional data from a database, according to appropriate access level of each participant in the chain. Advantages and disadvantages of automated identification technologies are discussed, with the proposition for using specific technologies in certain food product stages

Holocene Critical Zone dynamics in an Alpine catchment inferred from a speleothem multiproxy record : disentangling climate and human influences

Disentangling the effects of climate and human impact on the long-term evolution of the Earth CriticalZone is crucial to understand the array of its potential responses to the ongoing Global Change. This task requires natural archives from which local information about soil and vegetation can be linked directly to climate parameters. Here we present a high-resolution, well-dated, speleothem multiproxy record from the SW Italian Alps, spanning the last ~10,000 years of the present interglacial (Holocene). We correlate magnetic properties and the carbon stable isotope ratio to soil stability and pedogenesis, whereas the oxygen isotope composition is interpreted as primarily related to precipitation amount, modulated at different timescales by changes in precipitation source and seasonality. During the 9.7-2.8 ka period, when anthropic pressure over the catchment was scarce, intervals of enhanced soil erosion are related to climate-driven vegetation contractions and occurred during drier periods. Immediately following the onset of the Iron Age (ca. 2.8 ka), by contrast, periods of enhanced soil erosion coincided with a wetter climate. We propose that the observed changes in the soil response to climate forcing were related to early anthropogenic manipulations of Earth\u2019s surface, which made the ECZ more sensitive to climate oscillations

Aligning MIS5 proxy records from Lake Ohrid (FYROM) with independently dated Mediterranean archives: implications for core chronology

The DEEP site sediment sequence obtained during the ICDP SCOPSCO project at Lake Ohrid was dated using tephrostratigraphic information, cyclostratigraphy, and orbital tuning through marine isotope record. Although this approach is suitable for the generation of a general chronological framework of the long succession, it is insufficient to resolve more detailed paleoclimatological questions, such as leads and lags of climate events between marine and terrestrial records or between different regions. In this paper, we demonstrate how the use of different tie points can affect cyclostratigraphy and orbital tuning for the period between ca. 140 and 70 ka and how the results can be correlated with directly/indirectly radiometrically-dated Mediterranean marine and continental proxy records. The alternative age model obtained shows consistent differences with that proposed by Francke et al. (2015) for the same interval, in particular at the level of the MIS6-5e transition. According to this age model, different proxies from the DEEP site sediment record support an increase of temperatures between glacial to interglacial conditions, which is almost synchronous with a rapid increase in sea surface temperature observed in the western Mediterranean. The results show how important a detailed study of independent chronological tie points is for synchronizing different records and to highlight asynchronisms of climate events