Abstract
Collaborative networks (CNs) of organizations are nowadays complex and intertwined compositions of technological, cognitive and social artifacts. The design of such compositions should be addressed as a socio-technical endeavor as a way to maximize the success probability. In despite of intensive research in this community, much has to be explored to achieve sound contributions to a design theory of CNs. In this paper, we make use of the context intervention -mechanism-outcome logic (CIMO-logic) as a way to improve the design propositions component of a CN design theory. Variations of the concept of "mechanism" are explored with the goal of making clearer the socio-technical perspective in the design propositions. This theoretical exploration is illustrated with a case of transforming an industrial business association (IBA) in a digital collaborative network.

Abstract
A Dynamic Manufacturing Network (DMN) is the manufacturing industry application of the Virtual Enterprise (VE) business model based on real time information sharing and process integration. DMNs are normally formed and supported by a collaborative platform previously designed and built by a preexisting strategic partnership. The collaborative platform forms and tracks each DMN through all phases of its life cycle which leads to the accumulation and storage of large historical datasets on partner and customer characteristics and actions. This data holds the key to customer and manufacturer behavioral patterns and performances that can further be used in the decision making processes. In this study, we have focused on tackling this widely neglected research opportunity, by integrating manufacturer, order and customer data and characteristics into DMN formation and planning. The developed big data analytics approach consists of TOPSIS, fuzzy inference system and multi objective optimization techniques. Initially, by integrating the TOPSIS multi criteria decision making technique with a fuzzy inference system (FIS) we have computed indices for Manufacturer reliability and Order priority. Then we developed a multi-objective mixed integer linear programming (MILP) model to generate efficient solutions minimizing cost and assigning more reliable manufacturers to orders with higher priority.

Abstract
The role of urban freight vehicle trips in fulfilling the consumption needs of people in urban areas is often overshadowed by externality-causing parking practices (e.g., double-parking associated with traffic delays). Loading/unloading bays are generally viewed as an effective way to avoid freight vehicles double-parking, but are often misused by non-freight vehicles. We assess the potential of reducing freight vehicles double-parking mobility impacts by changing: (a) the spatial configuration (number, location, size) of loading/unloading bays and, (b) the non-freight vehicles parking rules compliance levels. Parking demand models were created with data from an establishment-based freight survey and a parking observation exercise. Two case studies were defined for 1 km(2) zones in the city of Lisbon, Portugal. Alternative bay systems were derived from an iterative implementation of the "maximize capacitated coverage" algorithm to a range of bays to be located. Parking operations in current and alternative bay systems were compared using a microsimulation. Bay systems' ability in reducing double-parking impacts was assessed via a set of indicators (e.g., average speed). Freight traffic causes a disproportionate amount of externalities and the current bay configuration leads to greater mobility impacts than some of the proposed systems. Enforcement was a crucial element in reducing parking operations impact on traffic flow in one of the case-studies. Road network characteristics were demonstrated to play a role in the adequate strategy of arranging the spatial configuration of bays.

Abstract
This paper presents an exploratory approach to study and identify the main characteristics of the two-dimensional strip packing problem (2D-SPP). A large number of variables was defined to represent the main problem characteristics, aggregated in six groups, established through qualitative knowledge about the context of the problem. Coefficient correlation are used as a quantitative measure to validate the assignment of variables to groups. A principal component analysis (PCA) is used to reduce the dimensions of each group, taking advantage of the relations between variables from the same group. Our analysis indicates that the problem can be reduced to 19 characteristics, retaining most part of the total variance. These characteristics can be used to fit regression models to estimate the strip height necessary to position all items inside the strip.

Abstract
The bi-dimensional irregular strip packing is a difficult problem in the cutting and packing field. Its main feature, and central source of complexity, is the irregularity of the shape of the items. Consequently, mathematical solvers are only able to obtain optimal solutions for small instances and heuristics are often employed in the literature. In such algorithms, it is not possible to guarantee that the optimum solution is found. In such cases, a restricted version of the problem can be adopted in order to improve the performance. One possible restriction is the adoption of pairwise clustering, i.e., elimination of items by joining two pieces. In this work, an automatic pairwise clustering method is proposed for the dotted board model, which limits the placement of items to equally distributed discrete points. The clustered problems are then used as input to an irregular strip packing solver. The results obtained in this paper can be used as an initial guideline for the use of clustering in a discrete grid, which was beneficial in some of the tested cases.

Abstract
Performance assessment is critical in today's competitive environments, where companies need to establish trade-offs between key competitive dimensions. The complexity of these environments calls for new approaches to performance assessment. Thus, in this work, we propose a novel conceptual framework for performance assessment in manufacturing environments combining different production strategies. Focus is laid on MTO/ETO combined environments and a three-stage problem analysis is considered. Firstly, a hybrid SD-DES-ABS model approach addresses the needs of a system that handles different types of orders, processes and workforce allocation requirements; secondly, the model results for different demand scenarios are assessed using a one-way ANOVA analysis followed by a Tukey - Kramer's test, with pairwise comparisons for assessment of significant performance variations under different system operating policies. A full factorial Design of Experiments (DOE) analysis follows, for determining the relevant process parameters influencing the system performance. As an example of application of the proposed framework, we consider the case of an advanced manufacturing company, whose manufacturing environment encompasses combined MTO/ETO production strategies.