Multi-energy retail market simulation with autonomous intelligent agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2005. Faculdade de Engenharia. Universidade do Port

Aquaculture production optimisation in multicage farms subject to commercial and operational constraints

The new advances in production methods have led to an increase in aquaculture production to the extent that the industry can now aid traditional fishing in meeting the growing global demand for fish within the context of the depletion of fisheries' resources. In this new context, market competition has increased and the complexity of managing industrial-scale production processes involving biological systems is still a growing problem. This has also led, in many cases, to a lack of management capacity that increases when it comes to setting long-term strategic plans. This study presents a methodology that aims to help aquaculture managers in decision making. It integrates a multi-criteria model and a Particle Swarm Optimisation (PSO) technique in order to provide a production strategy that optimises the value of multiple objectives at a fish farm with multiple cages, batches, feeding alternatives and products. This multi-criteria approach takes into account not only the effect of biological performance on economic profitability, but also the effect on environmental sustainability and aspects of product quality. In addition, it enables consideration of new operational and commercial constraints, such as the maximum volume of fish harvested per week, based on labour and marketing constraints, or the minimum necessary volume of fish harvested on specific dates to comply with commercial agreements. Results obtained demonstrate the utility of this novel approach to decision-making optimisation in aquaculture both when establishing overall strategic planning and when adopting new ways of producing.info:eu-repo/grantAgreement/EC/H2020/727315/EU/Mediterranean Aquaculture Integrated Development/MedAID

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

Resolving forward-reverse logistics multi-period model using evolutionary algorithms

© 2016 Elsevier Ltd In the changing competitive landscape and with growing environmental awareness, reverse logistics issues have become prominent in manufacturing organizations. As a result there is an increasing focus on green aspects of the supply chain to reduce environmental impacts and ensure environmental efficiency. This is largely driven by changes made in government rules and regulations with which organizations must comply in order to successfully operate in different regions of the world. Therefore, manufacturing organizations are striving hard to implement environmentally efficient supply chains while simultaneously maximizing their profit to compete in the market. To address the issue, this research studies a forward-reverse logistics model. This paper puts forward a model of a multi-period, multi-echelon, vehicle routing, forward-reverse logistics system. The network considered in the model assumes a fixed number of suppliers, facilities, distributors, customer zones, disassembly locations, re-distributors and second customer zones. The demand levels at customer zones are assumed to be deterministic. The objective of the paper is to maximize the total expected profit and also to obtain an efficient route for the vehicle corresponding to an optimal/near optimal solution. The proposed model is resolved using Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) algorithms. The findings show that for the considered model, AIS works better than the PSO. This information is important for a manufacturing organization engaged in reverse logistics programs and in running units efficiently. This paper also contributes to the limited literature on reverse logistics that considers costs and profit as well as vehicle route management