The objective of this paper is to summarize the impact of Industry 4.0 on the most representative methods and tools of Lean Six Sigma methodology. General information about the most important Lean Six Sigma methods is given so that the reader can easily understand possible scenarios when comparing the original Lean Six Sigma tools with new evolving ones. Main references to this paper were systematically analyzed and compared to Industry 4.0 principles. The assumptions about survival of Lean Six Sigma in Industry 4.0 are based on the rapid progress of Big Data and High-Performance Computing. Lean and Six Sigma will survive Industry 4.0. Lean should retain its universality with little modification or update requirements, but Six Sigma will need some adaptations. The Six Sigma will be relevant in some cases, but to survive the Big Data and smart plants, it will require some changes in its analysis, methods, and tools. The paper provides useful insight into the adaptation of Lean Six Sigma methods to Industry 4.0, by explaining possible scenarios under which the original Lean Six Sigma tools will evolve into adapted ones.

In this report the conclusions by the team of experts that took the ”Trans- portation Organization of the Nicosia District (OSEL)” challenge are provided. The challenge was to identify ways to improve efficiency of the bus network and increase the utilization of the network by the public. A thorough analysis of the various factors that affect bus route planning is provided. Moreover, a demonstration of a simplified route planning problem is described in order to motivate further work on this topic. Recommendations are provided to the company on the way to move forward towards solving the problem of creating a bus network with increased efficiency and grater appeal to the public. Specific recommendations include the collection of a larger amounts of data that can be used to generate models used in simulation analysis. Data include demographic data on bus usage and bus usage preferences by the public. In addition, data is required on bus travel times, walking distance to the nearest bus stop by the commuter, and traffic data.

Transportation in urban areas poses big challenges related to sustainability, safety and health of residents. A key step to improving policymaking in these respects is to collect and analyse data on how current resources are used, so that inefficiencies may be identified and addressed. The abundance of mobile devices makes it very attractive to harness the advanced data collection abilities of smartphones to tackle this question.

In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related systems. Other relevant domains of data include scheduled activities on a grid, large events and conventions in the area, equipment duty cycle schedule, data from call centers, real-time traffic, Facebook, Twitter, and other social networks feeds, and variety of city or region websites. All these distributed data sources pose information collection, integration and analysis challenges. Our approach is concentrated on complex non-cyclic events detection where detected events have a human crowd magnitude that is influencing power requirements. The proposed methodology deals with computation, transformation, modeling, and patterns detection over large volumes of partially ordered, internet based streaming multimedia signals or text messages. We are claiming that traditional approaches can be complemented and enhanced by new streaming data inclusion and analyses, where complex event detection combined with Webbased technologies improves short term load forecasting. Some preliminary experimental results, using Gowalla social network dataset, confirmed our hypothesis as a proof-of-concept, and they paved the way for further improvements by giving new dimensions of short term load forecasting process in a smart grid.

We used nine complete genome sequences, from grape, poplar, Arabidopsis, soybean, lotus, apple, strawberry, cacao, and papaya, to investigate the paleohistory of rosid crops. We characterized an ancestral rosid karyotype, structured into 7/21 protochomosomes, with a minimal set of 6,250 ordered protogenes and a minimum physical coding gene space of 50 megabases. We also proposed ancestral karyotypes for the Caricaceae, Brassicaceae, Malvaceae, Fabaceae, Rosaceae, Salicaceae, and Vitaceae families with 9, 8, 10, 6, 12, 9, 12, and 19 protochromosomes, respectively. On the basis of these ancestral karyotypes and present-day species comparisons, we proposed a two-step evolutionary scenario based on allohexaploidization involving the newly characterized A, B, and C diploid progenitors leading to dominant (stable) and sensitive (plastic) genomic compartments in any modern rosid crops. Finally, a new user-friendly online tool, “DicotSyntenyViewer” (available from http://urgi.versailles.inra.fr/synteny-dicot), has been made available for accurate translational genomics in rosids.

This paper presents a heuristic approach combining constraint satisfaction, local search and a constructive optimization algorithm for a large-scale energy management and maintenance scheduling problem. The methodology shows how to successfully combine and orchestrate different types of algorithms and to produce competitive results. We also propose an efficient way to scale the method for huge instances. A large part of the presented work was done to compete in the ROADEF/EURO Challenge 2010, organized jointly by the ROADEF, EURO and Electricite de France. The numerical results obtained on official competition instances testify about the quality of the approach. The method achieves 3 out of 15 possible best results.

This paper presents a heuristic approach combining constraint satisfaction, local search and a constructive optimization algorithm for a large-scale energy management and maintenance scheduling problem. The methodology shows how to successfully combine and orchestrate different types of algorithms and produce competitive results. The local search for production assignment is a simple yet optimal solution for the relaxed initial problem. We also propose an efficient way to scale the method for huge instances. A large part of the presented work is done to compete in the ROADEF/EURO Challenge 2010, organized jointly by the ROADEF, EURO and the Électricité de France. The numerical results obtained for the official competition instances testify about the quality of the approach. The method achieves 3 out of 15 possible best results.

An out-of-stock (OOS) event is referred as one of the biggest supply-chain management problem concerning retailers, distributors and consumers. We present available PCG data and discuss how to determine the importance of some features (fields), their interconnections and compare them with standard data fields used in other publicly accessible studies and recommendations from Efficient Consumer Response (ECR). We propose several models and algorithms to predict and solve Out of stock problem and at the end the computational results of these models are presented.