Notwithstanding its disruptive potential, which has been the object of considerable debate, Industry4.0 (I4.0) operationalisation still needs significant study. Specifically, scheduling is a key process that should be explored from this perspective. The purpose of this study is to shed light on the issues regarding scheduling that need to be considered in the new I4.0 framework. To achieve this, a two-stage cascade literature review is performed. The review begins with an analysis regarding the opportunities and challenges brought by I4.0 to the scheduling field, outputting a set of critical scheduling areas (CSA) in which development is essential. The second-stage literature review is performed to understand which steps have been taken so far by previous research in the scheduling field to address those challenges. Thus, a first contribution of this work is to provide insight on the influence and expected changes brought by I4.0 to scheduling, while showcasing relevant research. Another contribution is to identify the most promising future lines of research in this field, in which relevant challenges such as holistic scheduling, or increased flexibility requirements are highlighted. Concurrently, CSA such as decentralised decision-making, and human–robot collaboration display large gaps between current practice and the required technological level of development.

Nowadays, pavement management systems (PMS) are mainly based on monitoring processes that have been established for a long time, and strongly depend on acquired experience. However, with the emergence of smart technologies, such as internet of things and artificial intelligence, PMS could be improved by applying these new smart technologies to their decision support systems, not just by updating their data collection methodologies, but also their data analysis tools. The application of these smart technologies to the field of pavement monitoring and condition evaluation will undoubtedly contribute to more efficient, less costly, safer, and environmentally friendly methodologies. Thus, the main drive of the present work is to provide insight for the development of future decision support systems for smart pavement management by conducting a systematic literature review of the developed works that apply smart technologies to this field. The conclusions drawn from the analysis allowed for the identification of a series of future direction recommendations for researchers. In fact, future PMS should tend to be capable of collecting and analyzing data at different levels, both externally at the surface or inside the pavement, as well as to detect and predict all types of functional and structural flaws and defects.

Firms have available many forms of collaboration, including cooperatives or joint ventures, in this way leveraging their market power. Customers, however, are atomic agents with few mechanisms for collaborating, leading to an unbalanced buyer-supplier relationship and economic surpluses that shift to producers. Some group buying websites helped alleviate the problem by offering bulk discounts, but more advancements can be made with the emergence of technologies, such as the blockchain. In this article, we propose a customer-push e-marketplace built on top of Ethereum, where customers can aggregate their proposals, and suppliers try to outcompete each other in reverse auction bids to fulfil the order. Furthermore, smart contracts make it possible to automate many operational activities, such as payment escrows/release upon delivery confirmation, increasing the efficiency along the supply chain. The implementation of this network is expected to improve market efficiency by reducing transaction costs, time delays, and information asymmetry. Furthermore, concepts such as increased bargaining power and economies of scale, and their effects in buyer-supplier relationships, are also explored.

Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines have a great variability of fuel consumption depending on the scenario of utilization. This paper describes the creation of a framework aiming to estimate the fuel consumption of construction trucks depending on the carried load, the slope, the distance, and the pavement type. Having a more accurate estimation will increase the benefit of these optimization tools. The fuel consumption estimation model was developed using Machine Learning (ML) algorithms supported by data, which were gathered through several sensors, in a specially designed datalogger with wireless communication and opportunistic synchronization, in a real context experiment. The results demonstrated the viability of the method, providing important insight into the advantages associated with the combination of sensorization and the machine learning models in a real-world construction setting. Ultimately, this study comprises a significant step towards the achievement of IoT implementation from a Construction 4.0 viewpoint, especially when considering its potential for real-time and digital twins applications.

Earthworks are part of the construction of any type of ground transport infrastructure. In many road and railway infrastructures earthworks represent up to 30 to 50% of total cost of the construction. Moreover, earthworks involve the use of heavy mechanical equipment (e.g., excavators, dumper trucks, bulldozers and rollers) and repetitive activities that are responsible for large amounts of carbon emissions with negative impact to the environment.In this context, the optimization of earthworks construction activities is becoming increasingly important in recent years, while effective and practical integrated solutions have not been established so far. As such, this work introduces a novel optimization integrated system for earthwork tasks. In this integrated system, the optimization is carried out on various fronts, namely minimization of execution cost and duration, while attempting to reduce environmental impacts, such as carbon emissions. In order to achieve this, the integration of a wide array of technologies is required, so as to allow for a proper adjustment to reality. These range from evolutionary computation and data mining (i.e., soft computing), to geographic information systems and linear programming. The former are used firstly to provide realistic estimates of the productivity of available resources (i.e., equipment), and secondly to perform their optimal allocation throughout the construction site. Concurrently, the latter are employed for supporting the optimization of resource and material management, as well as of the trajectories associated with transportation of material from excavation to embankment fronts.The system has been validated using real-world data stemming from a Portuguese road construction site. Results show that the proposed system is very competitive when compared with the manual allocation methodologies currently used for the design and construction of earthworks. In fact, the system can output several different resource distribution solutions, which comprehend a trade-off between the referred optimization objectives, enhancing the flexibility of design by allowing the user to select the solution that best fits the project restrictions (e.g., deadline, budget). As such, the system is capable of allocating the available equipment in a way that maximizes its potential and productivity, while indirectly guaranteeing minimum carbon emissions in each possible solution. These results emphasize the importance of using this kind of decision support/optimization tools in the design and construction of earthworks