Abstract
The nesting problem is a two-dimensional cutting and packing problem where the small pieces to cut have irregular shapes. A particular case of the nesting problem occurs when congruent copies of one single shape have to fill, as much as possible, a limited sheet. Traditional approaches to the nesting problem have difficulty to tackle with high number of pieces to place. Additionally, if the orientation of the given shape is not a constraint, the general nesting approaches are not particularly successful. This problem arises in practice in several industrial contexts such as footwear, metalware and furniture. A possible approach is the periodic placement of the shapes, in a lattice way. In this paper, we propose three heuristic approaches to solve this particular case of nesting problems. Experimental results are-compared with published results in literature and additional results obtained from new instances are also provided.

Abstract
Real-world distribution problems raise some practical considerations that usually are not considered in a realistic way in more theoretical studies. One of these considerations is related to the vehicle capacity, not only in terms of cubic meters or weight capacity but also in terms of the cargo physical arrangements. In a distribution scene, two combinatorial optimization problems, the vehicle routing problem with time windows and the container loading problem, are inherently related to each other. This work presents a framework to integrate these two problems using two different resolution methods. The first one treats the problem in a sequential approach, while the second uses a hierarchical approach. To test the quality and efficiency of the proposed approaches, some test problems were created based on the well-known Solomon, Bischoff and Ratcliff test problems. The results of the integrated approaches are presented and compared with results of the vehicle routing problem with time windows and the container loading problem applied separately.

Abstract
In this paper we present Khronos, a framework for Discrete Event Simulation (DES) in Python. Its essential objective is to provide a powerful, general, and easy-to-use tool for simulation programmers/users. In the form of a programming library, Khronos accomplishes a seamless combination of DES with Python, taking advantage of many of the language's features in addition to maintaining its generality, power, and simplicity. The final product is a framework which releases the user from the low-level details of DES (e.g., managing the event schedule, simulation clock, or real-time synchronization), also providing base classes for entities/resources, and a flexible set of primitives for describing the dynamic behaviors of these elements. Most importantly, the resulting simulation programs are intuitive to write, and inherently very readable. We highlight the main features of the Khronos library in a tutorial manner, presenting a simple illustrative example.

Abstract
A bilevel facility location problem in which the clients choose suppliers based on their own preferences is studied. It is shown that the coopertative and anticooperative statements can be reduced to a particular case in which every client has a linear preference order on the set of facilities to be opened. For this case, various reductions of the bilevel problem to integer linear programs are considered. A new statement of the problem is proposed that is based on a family of valid inequalities that are related to the problem on a pair of matrices and the set packing problem. It is shown that this formulation is stronger than the other known formulations from the viewpoint of the linear relaxation and the integrality gap. © 2009 Pleiades Publishing, Ltd.

Abstract
In BSc/MSc engineering programmes at Faculty of Engineering of the University of Porto (FEUP), the need to provide students with teamwork experiences close to a real world environment was identified as an important issue. A new group-formation method that aims to provide an enriching teamwork experience is proposed. Students are asked to answer a questionnaire to evaluate their teamwork profiles and are assigned to groups by an algorithm aiming to achieve maximum diversity within groups and homogeneity among groups. The profile diversity/complementarity within a group is an important factor to promote members' commitment and coordination in order to achieve the proposed goals. The proposed method is compared to a standard self-selection method for three engineering programmes in three academic years. The results show that, with the new method, there are a higher number of medium ranked groups which surpass the expectation and that, contrary to some students' beliefs, the method does not have a negative impact on the overall final marks. © 2009 SEFI.

Abstract
The widespread usage of technology for service provision to customers has created a new and challenging environment for the design of interactive systems, with the emergence of technology enabled multi-channel services. Requirements engineers involved in the design of such service systems must actively work together with interaction designers and service managers to better integrate customer service experience and technology components, requiring unifying methods and tools within the emerging field of service science management and engineering. This paper proposes the service experience blueprint (SEB), a multidisciplinary method for the design of technology enabled multi-channel service systems and illustrates its application in two examples of redesign of banking services that involved an extensive study with more than 4,000 bank customers. The SEB method is based on concepts and tools from RE and interaction design, such as goal-oriented analysis and conceptual modeling, but also uses methods developed in the service and marketing fields, such as service blueprinting. SEB brings marketing research methods to the requirements process, as they can provide a useful contribution for the elicitation of customer experience requirements in service environments. By bringing together goal-oriented modeling and use case modeling from requirements engineering, with service blueprinting from service design, the SEB method contributes to creating a shared understanding and a unifying language to better support the design of new technology enabled multi-channel service systems, where technology and service issues are deeply intertwined.