Abstract
This paper presents a new solution approach to solve the resource-constrained project scheduling problem in the presence of three types of logical constraints. Apart from the traditional AND constraints with minimal time-lags, these precedences are extended to OR constraints and bidirectional (BI) relations. These logical constraints extend the set of relations between pairs of activities and make the RCPSP definition somewhat different from the traditional RCPSP research topics in literature. It is known that the RCPSP with AND constraints, and hence its extension to OR and BI constraints, is NP-hard. The new algorithm consists of a set of network transformation rules that removes the OR and BI logical constraints to transform them into AND constraints and hereby extends the set of activities to maintain the original logic. A satisfiability (SAT) solver is used to guarantee the original precedence logic and is embedded in a metaheuristic search to resource feasible schedules that respect both the limited renewable resource availability as well as the precedence logic. Computational results on two well-known datasets from literature show that the algorithm can compete with the multi-mode algorithms from literature when no logical constraints are taken into account. When the logical constraints are taken into account, the algorithm can report major reductions in the project makespan for most of the instances within a reasonable time.

Abstract
When defining the layout for a production line, it is necessary to assign tasks to workstations, so that the work is performed in a feasible sequence and approximately equal amounts of time are needed at each workstation, a process called line balancing. Therefore, the need for balancing production lines involves the distribution of sequential activities for jobs in order to allow high labour and equipment utilisation and minimise the idle time. Line balancing problems are complex to treat, being used distinct methodologies to perform it. This paper describes an application for line balancing using two genetic algorithms (the first obtains solutions to the problem and the second optimises those solutions), associated with a graphical interface for the problem data input and visualisation of results. Results demonstrate advantages over heuristic methods as it is possible to obtain more than one solution and it is more practical to use the developed application. Copyright © 2016 Inderscience Enterprises Ltd.

Abstract

Abstract
Cognitive robots, able to adapt their actions based on sensory information and the management of uncertainty, have begun to find their way into manufacturing settings. However, the full potential of these robots has not been fully exploited, largely due to the lack of vertical integration with existing IT infrastructures, such as the manufacturing execution system (MES), as part of a large-scale cyber-physical entity. This paper reports on considerations and findings from the research project STAMINA that is developing such a cognitive cyber-physical system and applying it to a concrete and well-known use case from the automotive industry. Our approach allows manufacturing tasks to be performed without human intervention, even if the available description of the environment-the world model-suffers from large uncertainties. Thus, the robot becomes an integral part of the MES, resulting in a highly flexible overall system.

Abstract
Purpose - In a context of international economic crisis the improvement in the efficiency and productivity of public services is seen as a way to maintain high-quality levels at lower costs. Increased productivity can be promoted through benchmarking exercises, where key performance indicators (KPIs), individually or aggregated, are used to compare health units. The purpose of this paper is to describe a benchmarking platform, called Hospital Benchmarking (HOBE), where hospital's services are used as the unit of analysis. Design/methodology/approach - HOBE platform includes a set of managerial indicators through which hospital services' are compared. The platform also benchmarks services through aggregate service indicators, and provides an aggregate measure of hospital's performance based on a composite indicator of the service's performances. These aggregate indicators were obtained through data envelopment analysis (DEA). Findings - Some results are presented for Portuguese hospitals for the trial years of 2008 and 2009, for which data is publicly available. Details for the service-level analysis are provided for a sample hospital, as well as details on the aggregate performance resulting from services performances. Practical implications - HOBE's features and outcomes show that the platform can be used to guide management actions and to support the design of health policies by administrative authorities, provided that good quality and timely data are available, and that hospitals are involved in the design of the KPIs. Originality/value - The platform is innovative in the sense that it bases its analysis on hospital's services, which are in general more comparable among hospitals than indicators of hospital overall performance. In addition, it makes use of DEA to aggregate performance indicators, allowing for user choice in the inputs and outputs to be aggregated, and it proposes a novel model to aggregate service's efficiencies into a single measure of hospital performance.

Abstract
Private individual transportation is becoming cumbersome and expensive, as urban traffic turns more chaotic, fuel prices increase and the effects of pollutant emissions become evident. Public buses are an attractive approach to reducing the cars in use, as they mostly depend on preexistent infrastructure. Making these buses electric would mean even less tailpipe emissions and cheaper consumption costs, when compared to conventional vehicle fleets. However, fully electric bus fleets can prove disadvantageous. We can tackle this with a more conservative approach - using mixed bus fleets, comprised by both electric and conventional buses. This work intends on studying how to obtain a good balance of the different vehicle typologies in the fleet. To fulfill these goals, real data of a bus network in Porto, Portugal, is studied and an evolutionary algorithm devises mixed fleet arrangements, with a brief sensitivity analysis giving us an overview of how to improve our results. As a means of decision support, this work contributes not only with an approach to configure optimized mixed bus fleets, but also with general considerations for managing public transit with electric vehicle fleets.