Abstract
Commonly in container terminals, the containers are stored in yards on top of each other using yard cranes. The split-platform storage/retrieval system (SP-AS/RS) has been invented to store containers more efficiently and to access them more quickly. The integrated scheduling of quay cranes, automated guided vehicles and handling platforms in SP-AS/RS has been formulated and solved using the simulated annealing algorithm in previous literatures. This paper presents a genetic algorithm (GA) to solve this problem more accurately and precisely. The GA includes a new operator to make a random string of tasks observing the precedence relations between the tasks. For evaluating the performance of the GA, 10 small size test cases were solved by using the proposed GA and the results were compared to those from the literature. Results show that the proposed GA is able to find fairly near optimal solutions similar to the existing simulated annealing algorithm. Moreover, it is shown that the proposed GA outperforms the existing algorithm when the number of tasks in the scheduling horizon increases (e.g. 30 to 100).

Abstract
The use of process discovery algorithms is in practice hindered by many factors, being the algorithm's representational bias, parameter configuration and algorithm's capabilities the most important ones. Nowadays, a user of these algorithms needs an expert knowledge in order to successfully apply them. In this demo, we present the RS4PD, a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Copyright © 2014 for this paper by its authors. Copying permitted for private and academic purposes.

Abstract
Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances. © 2014 Springer International Publishing Switzerland.

Abstract
This paper describes the major elements of the Goldratt's framework - the Theory of Constraints (TOC) - in the banking sector, and examines the factors involved in the decision to adopt the TOC by companies in this sector. Through a deep literature review, analyzing similar cases that apply the Goldratt's framework in services and in manufacturing and the several views of its components, we aim at formulating a framework specifically for the banking system. The study uses a qualitative methodology supported by the information extracted from reality as it is framed in a multi-case study model. As part of the quantitative approach, we test several research hypotheses raised from the review of existing studies in the area. The main factors that influence the decision to adopt the TOC are the nature and the characteristics of the banking service, the attitude towards change, the leadership and the commitment of the entire institution. By using the Goldratt's approach outlined in this article, through the location of the constraints and develop practical measurement to facilitate the banking process improvements, banks can improve resource utilization, revenues and employee satisfaction.

Abstract
E-government has become a priority for many governments around the world and one of the main change drivers in the provision of new services in the public administration context. As public services are part of a complex network in which citizens are the main players, public authorities are increasingly trying to identify and deploy programmes that promote the modernisation and simplification of public services based on knowledge management. This chapter aims to understand the impact of e-government initiatives on consumer behaviour, as well as the importance of knowledge management for value creation in the public administration context. The Simplex programme, the main initiative in Portugal to develop administrative simplification and e-government, is analysed within two contexts: the central administration and the local municipality of Oporto. This chapter highlights that e-government has been an important factor contributing to both the administrative simplification and the improvement in the quality of public services.

Abstract
Drivers routing decisions can be influenced to minimize environmental impacts by using, for instance, dynamic and intelligent road pricing schemes. However, some previous research studies have shown that often different pollutants can dictate different traffic assignment strategies which makes necessary to assign weights to these pollutants so they become comparable. In this chapter, a tool for traffic assignment taking into account eco-routing purposes is presented. The main goal of this work is to identify the best traffic volume distribution that allows a minimization of environmental costs for a given corridor with predetermined different alternative routes. To achieve this, an integrated numerical computing platform was developed by integrating microscopic traffic and emission models. The optimization tool employs non-linear techniques to perform different traffic assignment methods: User Equilibrium (UE), System Optimum (SO) and System Equitable (SE). For each method, different strategies can be assessed considering: (i) individual pollutants and traffic performance criteria; and (ii) all pollutants simultaneously. For the latter case, three different optimization approaches can be assessed based on: (i) economic costs of pollutants once released into the air; (ii) human health impacts according to the Eco-Indicator 99; and (iii) real time atmospheric pollutant concentration levels. The model was applied to a simple network, simulating three levels of traffic demand and three different strategies for traffic assignment. The system is developed in Microsoft Excel and offers a user friendly access to optimization algorithms by including a dynamic user interface. © Springer International Publishing Switzerland 2014.