Abstract
This study proposes a model for (re-)designing machine layouts in already existing facilities with a multi-period time planning horizon. The model can be applied in several situations and at different moments of a layout life cycle, for example to design the initial layout of an existing facility, or to make some specific and local reconfigurations. This dynamic multiobjective model minimizes costs (production, material handling and reconfiguration costs), maximizes adjacency between machines, minimizes unsuitability (to combine characteristics of the machines and of the existing locations), and can allow changes between periods on the product mix or on the machine layout requirements (e.g., required area). The performance of the model was tested with a case study based on a real first-tier supplier of the automotive industry, thus showing the practical potential of the proposed approach.

Abstract
In the Mediterranean region, water scarcity has already prompted concern in the wine sector due to the strong impact it has on vineyard productivity and wine quality. Water footprint is an indicator that takes account of all the water involved in the creation of a product and may help producers to identify hotspots, and reduce water consumption and the corresponding production costs. In recent years several studies have been reported on wine water footprint determination, but mostly focused on the viticulture phase or assuming no grey water footprint at the winery since it has a treatment system. In the framework of the WineWaterFootprint project a medium-size winery was monitored, with direct measurements, regarding determination of the blue and grey components of water footprint. The determined winery water footprint ranged from 9.6 to 12.7 L of water per wine bottle of 0.75 L, the wastewater produced being responsible for about 98%, which means that the grey component cannot be disregarded. The developed scenarios show that a potential reduction of 87% in winery water footprint can be obtained with almost no investment. The challenge of reducing the grey footprint is not in technology development, but rather in the proper maintenance and monitoring of treatment systems.

Abstract
Governmental investments on the development of high-tech clusters are among the main policies for socioeconomic development, enabling countries to be part of global value networks. Our objective is to identify which are the strategies of countries that want to join global aerospace value networks, by means of an abductive case research. Countries were divided in 3 groups (A; B: C) according to their global aerospace exports share. The analytical framework used to identify the strategies has 3 dimensions: network structure, network governance, and network dynamics. Results show different strategies according to the country's global exports share. While for countries in group A (exports above 1%), a strategy focused on the dimension network structure indicated a sustained high-tech sector. Countries in group C tend to focus on specialization, taking advantage of shifts in technological paradigms to upgrade their development level. The dimension network governance is mainly related to governmental efforts toward the creation of clusters and associations, promoting specialization and collaborative work. Finally, the dimension network dynamics describes the attraction of foreign companies to qualify the clusters at countries who belong to group C, while countries at group A reinforce their research and development activities. The comparison between countries is helpful for governmental representatives who want to develop strategies toward increasing participation in an industrial global value network and for supply chain managers to help selecting the locations for their operations.

Abstract
When planning a selling season, a car rental company must decide on the number and type of vehicles in the fleet to meet demand. The demand for the rental products is uncertain and highly price-sensitive, and thus capacity and pricing decisions are interconnected. Moreover, since the products are rentals, capacity "returns". This creates a link between capacity with fleet deployment and other tools that allow the company to meet demand, such as upgrades, transferring vehicles between locations or temporarily leasing additional vehicles. We propose a methodology that aims to support decision-makers with different risk profiles plan a season, providing good solutions and outlining their ability to deal with uncertainty when little information about it is available. This matheuristic is based on a co-evolutionary genetic algorithm, where parallel populations of solutions and scenarios co-evolve. The fitness of a solution depends on the risk profile of the decision-maker and its performance against the scenarios, which is obtained by solving a mathematical programming model. The fitness of a scenario is based on its contribution in making the scenario population representative and diverse. This is measured by the impact the scenarios have on the solutions. Computational experiments show the potential of this methodology regarding the quality of the solutions obtained and the diversity and representativeness of the set of scenarios generated. Its main advantages are that no information regarding probability distributions is required, it supports different decision-making risk profiles, and it provides a set of good solutions for an innovative complex application.

Abstract
Typical simultaneous lotsizing and scheduling models consider the limited capacity of the production system by respecting a maximum time the respective machines or production lines can be available. Further limitations of the production quantities can arise by the scarce availability of, e.g., setup tools, setup operators or raw materials which thus cannot be neglected in optimization models. In the literature on simultaneous lotsizing and scheduling, these production factors are called secondary resources. This paper provides a structured overview of the literature on simultaneous lotsizing and scheduling involving secondary resources. The proposed classification yields for the first time a unified view of scarce production factors. The insights about different types of secondary resources help to develop a new model formulation generalizing and extending the currently used approaches that are specific for some settings. Some illustrative examples demonstrate the functional principle and flexibility of this new formulation which can thus be used for a wide range of applications.

Abstract
In face of the continuously growing process of the fourth industrial revolution, the industries are forced to innovate their industrial manufacturing process to remain competitive and active in the market, perfecting the manufacturing process through new interconnected and autonomous technologies. Since time reduction, quality enhancement and cost reduction for manufacturing of industrial products are the major catalysts of a successful company for the current Era called Maintenance 4.0, this paper proposes to illustrate an Information Systems Architecture where, using the Viable Systems Model, it is possible to perform automatic adjustments in the subsystems related to Cyber-Physical Systems and Manufacturing Execution Systems (MES) within the Digital Manufacturing model and, in addition, to mitigate machine failures through predictive analyzes of massive volumes of data using algorithms with intelligent functions and Data Mining (DM) in order to automatically stabilize the entire system chain quickly and efficiently. © 2019 IEEE.