Abstract
This work considers sustainable scheduling of manufacturing operations and preventive maintenance activities in a single-machine environment where the machine works continuously in three eight-hour shifts per day. The jobs can be produced at different processing speeds, which reduces energy consumption and/or processing times. In a tri-objective mixed integer linear programming model, sustainability is attained through minimizing total weighted earliness/ tardiness - economic pillar, total energy consumption - environmental pillar, and number of undesired activities - social pillar. Moreover, a multi-objective genetic algorithm finds near optimal solutions in a timely manner. Numerical results will be presented at the conference.

Abstract

Abstract
For many years, industrial engineers and managers have differentiated their duties in the work environment. While this has allowed for the two specialities to operate in their respective domains, the all necessary integration required to deliver a seamless industrial operation and outcomes has been sub-optimal - particularly in cases of conflict of knowledge or power. Industrial engineering and management (IEM) has come to resolve this situation, creating a new professional field and profile, as well as a multifaceted specialisation with a practical character. The challenge to take the next step in the refinement of this relatively new reality in Portugal is placed upon the most recent IEM degree, at the University of Tras-os-Montes e Alto Douro (UTAD).

Abstract
There are extensive studies in the literature about the reorder point/order quantity policies for inventory management, also known as policies. Over time different algorithms have been proposed to calculate the optimal parameters given the demand characteristics and a fixed cost structure, as well as several heuristics and meta-heuristics that calculate approximations with varying accuracy. This work proposes a new meta-heuristic that evolves closed-form expressions for both policy parameters simultaneously - Cooperative Coevolutionary Genetic Programming. The implementation used for the experimental work is verified with published results from the optimal algorithm, and a well-known hybrid heuristic. The evolved expressions are compared to those algorithms, and to the expressions of previous Genetic Programming approaches available in the literature. The results outperform the previous closed-form expressions and demonstrate competitiveness against numerical methods, reaching an optimality gap of less than , while being two orders of magnitude faster. Moreover, the evolved expressions are compact, have good generalisation capabilities, and present an interesting structure resembling previous heuristics.

Abstract
Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state-of-the-art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Wind Power > Systems and Infrastructure Photovoltaics > Systems and Infrastructure

Abstract
The development of advanced driver assistance systems relies on an accurate estimation of the tire-road friction coefficient and cornering stiffness of the vehicle, which are closely linked to internal and external driving conditions. In this paper, an identification algorithm capable of simultaneously estimate the friction coefficient and cornering stiffness of the front and rear tires is pursued. A nonlinear adaptive law is proposed for the estimation of vehicle parameters under certain excitation conditions. It is shown that, by exploring the lateral dynamic of the vehicle, the convergence of the parameters to their true values can be guaranteed. A comprehensive study has been carried out in order to reveal the necessary conditions for convergence and observability of the parameters. Simulation results with a high fidelity full order Carsim model show a good performance of the proposed identification method.