Abstract
The liquefied natural gas (LNG) trade provides the means of trading gas globally and represents about 10% of the gas trade. The forecasts show the LNG business will grow, over the next 20 years, at about twice the rate of the whole gas trade. Although the current state of LNG trade is well studied, the literature on the future business structure of it is limited and conflictual. This work considers the future LNG business structure by comparing the development trajectories of the oil and LNG sectors. In addition, it assesses the conclusions drawn by researchers against this background and the current pattern of change in the industry. The comparison involves three stages: (1) trade flows-oil and LNG trade flows are very similar, mainly due to the common distribution of the oil and gas reserves. (2) Supply chain configuration-the international trade for both fuels is tanker based thus allowing for a similar market responsive trade policy, i.e., real-time destination selection (spot sale) at a global scale. (3) Institutional developments-the current transparent and competitive global oil trade, with prices dominated by physical and paper markets, was driven previously by long-term contracts, in the same manner as the current LNG business. This analysis, together with transaction cost economics, supports the argument that, in future, LNG spot trade will increase and give rise to a competitive and globally unified LNG market. Further-more, LNG pricing will become transparent and would be dominated by physical and paper markets benchmark prices. (C) 2016 John Wiley & Sons, Ltd.

Abstract
We consider a Unit Commitment Problem (UCP) addressing not only the economic objective of minimizing the total production costs-as is done in the standard UCP-but also addressing environmental concerns. Our approach utilizes a multi-objective formulation and includes in the objective function a criterion to minimize the emission of pollutants. Environmental concerns are having a significant impact on the operation of power systems related to the emissions from fossil-fuelled power plants. However, the standard UCP, which minimizes just the total production costs, is inadequate to address environmental concerns. We propose to address the UCP with environmental concerns as a multi-objective problem and use a metaheuristic approach combined with a non-dominated sorting procedure to solve it. The metaheuristic developed is a variant of an evolutionary algorithm, known as Biased Random Key Genetic Algorithm. Computational experiments have been carried out on benchmark problems with up to 100 generation units for a 24 h scheduling horizon. The performance of the method, as well as the quality, diversity and the distribution characteristics of the solutions obtained are analysed. It is shown that the method proposed compares favourably against alternative approaches in most cases analysed.

Abstract
The Unit Commitment Problem (UCP) is a well-known combinatorial optimization problem in power systems. The main goal in the UCP is to schedule a subset of a given group of electrical power generating units and also to determine their production output in order to meet energy demands at minimum cost. In addition, a set of technological and operational constraints must be satisfied. A large variety of optimization methods addressing the UCP is available in the literature. This panoply of methods includes exact methods (such as dynamic programming, branch-and-bound) and heuristic methods (tabu search, simulated annealing, particle swarm, genetic algorithms). This paper proposes two non-traditional formulations. First, the UCP is formulated as a mixed-integer optimal control problem with both binary-valued control variables and real-valued control variables. Then, the problem is formulated as a switching time dynamic optimization problem involving only real-valued controls.

Abstract
Given an edge-weighted graph, the maximum edge weight clique (MEWC) problem is to find a clique that maximizes the sum of edge weights within the corresponding complete subgraph. This problem generalizes the classical maximum clique problem and finds many real-world applications in molecular biology, broadband network design, pattern recognition and robotics, information retrieval, marketing, and bioinformatics among other areas. The main goal of this chapter is to provide an up-to-date review of mathematical optimization formulations and solution approaches for the MEWC problem. Information on standard benchmark instances and state-of-the-art computational results is also included. © Springer International Publishing AG 2017.

Abstract
We use the reinfection SIRI epidemiological model to analyze the impact of education programs and vaccine scares on individuals decisions to vaccinate or not. The presence of the reinfection provokes the novelty of the existence of three Nash equilibria for the same level of the morbidity relative risk instead of a single Nash equilibrium as occurs in the SIR model studied by Bauch and Earn (PNAS 101:13391-13394, 2004). The existence of three Nash equilibria, with two of them being evolutionary stable, introduces two scenarios with relevant and opposite features for the same level of the morbidity relative risk: the low-vaccination scenario corresponding to the evolutionary stable vaccination strategy, where individuals will vaccinate with a low probability; and the high-vaccination scenario corresponding to the evolutionary stable vaccination strategy, where individuals will vaccinate with a high probability. We introduce the evolutionary vaccination dynamics for the SIRI model and we prove that it is bistable. The bistability of the evolutionary dynamics indicates that the damage provoked by false scares on the vaccination perceived morbidity risks can be much higher and much more persistent than in the SIR model. Furthermore, the vaccination education programs to be efficient they need to implement a mechanism to suddenly increase the vaccination coverage level.

Abstract
We study a classic international trade model consisting of a strategic game in the tariffs of the governments. The model is a two-stage game where, at the first stage, governments of each country use their welfare functions to choose their tariffs either (i) competitively (Nash equilibrium) or (ii) cooperatively (social optimum). In the second stage, firms choose competitively (Nash) their home and export quantities. We compare the competitive (Nash) tariffs with the cooperative (social) tariffs and we classify the game type according to the coincidence or not of these equilibria as a social equilibrium, a prisoner's dilemma or a lose-win dilemma.