Abstract
Due to its combinatorial nature, the Unit Commitment problem has for long been an important research challenge, with several optimization techniques, from exact to heuristic methods, having been proposed to deal with it. In line with one current trend of research, metaheuristic approaches have been studied and some interesting results have already been achieved and published. However, a successful utilization of these methodologies in practice, when embedded in Energy Management Systems, is still constrained by the reluctance of industrial partners in using techniques whose performance highly depends on a correct parameter tuning. Therefore, the application of metaheuristics to the Unit Commitment problem does still justify further research. In this paper we propose a new search strategy, for Local Search based metaheuristics, that tries to overcome this issue. The approach has been tested in a set of instances, leading to very good results in terms of solution cost, when compared either to the classical Lagrangian Relaxation or to other metaheuristics. It also drastically reduced the computation times. Furthermore, the approach proved to be robust, always leading to good results independently of the metaheuristic parameters used.

Abstract
The use of optimization techniques is well established in forest short-term planning and decision-making. Yet, existing techniques may pose some limitations for tackling with stochastic factors impacting in the execution of forest operations, such as delays, equipment breakdowns and other unexpected events. This paper explores the potential of using optimization techniques in combination with discrete-event simulation (DES) models for planning harvesting and logistics operations acknowledging uncertainty. DES models may be useful for assessing the performance and identifying bottlenecks associated with the execution of the deterministic plans retrieved with optimization techniques, when such stochastic events occur. This paper further presents an approach for the combination of a heuristic and a DES model developed in SIMIO. This approach was used to solve the raw material reception problem (RMRP) at a Portuguese pulp mill. This paper concludes with the analysis of the performance of deterministic schedules for the wood trucks considering uncertainty in their arrival at the mill. © 2013 © 2013 Taylor & Francis.

Abstract

Abstract
Temporary opportunities for studying and working abroad have been growing globally and intensifying the movement of highly skilled temporary populations. To attract this group, cities need to address their residential and mobility needs. This study focuses on factors influencing residential and travel satisfaction of transnational temporary residents, highlighting the occurrence of residential selfselection, its impacts on residential and travel choices and on derived levels of satisfaction. We have estimated a Bayesian Structural Equations Model and found that lower levels of residential satisfaction (residential dissonance) are associated with lower rents, living farther away from the place of study or work, and having higher transport expenditures. In contrast, higher levels of residential satisfaction (residential consonance) are related to individuals' stronger preferences for active modes, lower levels of public transport use, and reduced transport monthly expenditures, which suggest shorter commuting distances. These findings reveal the tradeoffs involving residential location, monthly rent, and transport expenditures, highlighting that providing good public transport connections can reduce the burden of commuting distances. Our results indicate that better transport supply and land-use balance near the residence can improve both residential and travel satisfaction.

Abstract
Travel behavior adaptations resulting from international temporary relocation is understudied, despite their increasing relevance. The scarce published literature on the subject overlooks the local contexts and ignores aspects related to the adaptation processes and motivations. This study aims to partially fill this gap by addressing the travel behavior adaptation of international students and researchers, focusing on public transport (PT) frequency of use and satisfaction. To investigate this, a Bayesian Structural Equation Model was estimated using data collected from a tailor-made online survey answered by temporary international exchange students and researchers. The model confirms that (i) travel behavior habit in the city of origin influences the residential location choice in the host city; (ii) the higher the frequency of PT use in the city of origin, the higher the PT use in the host city; (iii) the residential location in the host city affects individuals' frequency of PT use and satisfaction; (iv) perceiving technology as helpful to move around leads to perceiving the PT system as easier to use at the beginning of the stay; (v) perceiving the PT as easier to use, leads to a higher frequency of its use and a higher level of satisfaction with the PT system.