Abstract

Abstract
Nowadays, it has been observed an increasing awareness and understanding on the subject of sustainable companies and business models, addressing multi-disciplinary approaches that cover not only economical problems, but also social and environmental challenges. Supply chains and especially collaborative networks managers are increasingly aware of these sustainability issues, continuously seeking to meet current human needs while preserving environmental safety. Only this way, focusing on its sustainable growing, it is possible to preserve companies' steadiness. In order to achieve this goal, sustainable networks must ensure that each partner is fully aligned and committed with economic, environmental and social axes that rule the network operational behaviour. Nevertheless, in order to achieve this level of maturity within such complex and turbulent environments, organisations need to improve the quality of their performance assessment approaches, integrating the different sustainability perspectives. To accomplish this, it is critical to establish specific indicators responsible to formalise and evaluate partners' behaviour, according to well-identified objectives, as well as fuse this information in a comprehensive and user-friendly way. This paper presents a new approach, based on a fuzzy logic-based algorithm, for sustainable network performance and risk assessment. © 2016 Inderscience Enterprises Ltd.

Abstract
Complexity in manufacturing systems appears under a variety of aspects, namely product, processes and operations and systems. Considering that the manufacturing environment is rapidly and constantly changing, with higher levels of customization and complexity, there is higher demand for flexibility and adaptability from companies. In this context, it seems essential to explore new approaches that can support decision-makers to take better decisions concerning the action plans that they need to launch to achieve the expected strategic and operational performance and alignment goals. Companies should become able to analyse their performance drivers, understand their meaning and the feedback loops that affect them. Therefore, decision makers can look into the future, and act even before these causes affect the transformation systems efficiency and effectiveness. This paper presents an approach oriented to multi-performance measurement in complex manufacturing environments. With this approach it is expected to overcome the gap between the operational and strategic layers of a manufacturing system, in order to reduce time when measuring performance and reacting to unexpected behaviours, as well as reduce errors when taking decisions. Moreover, it is expected to decrease the time necessary to calculate an indicator or to introduce a new one into performance management process, reducing the operational costs.

Abstract

Abstract
The Container Loading Problem (CLP) literature has traditionally evaluated the dynamic stability of cargo by applying two metrics to box arrangements: the mean number of boxes supporting the items excluding those placed directly on the floor (M1) and the percentage of boxes with insufficient lateral support (M2). However, these metrics, that aim to be proxies for cargo stability during transportation, fail to translate real-world cargo conditions of dynamic stability. In this paper two new performance indicators are proposed to evaluate the dynamic stability of cargo arrangements: the number of fallen boxes (NFB) and the number of boxes within the Damage Boundary Curve fragility test (NB_DBC). Using 1500 solutions for well-known problem instances found in the literature, these new performance indicators are evaluated using a physics simulation tool (StableCargo), replacing the real-world transportation by a truck with a simulation of the dynamic behaviour of container loading arrangements. Two new dynamic stability metrics that can be integrated within any container loading algorithm are also proposed. The metrics are analytical models of the proposed stability performance indicators, computed by multiple linear regression. Pearson's r correlation coefficient was used as an evaluation parameter for the performance of the models. The extensive computational results show that the proposed metrics are better proxies for dynamic stability in the CLP than the previous widely used metrics.

Abstract
In this paper we consider the problem of minimizing the waste of textile material in a Portuguese home textile manufacturing company. The company has a vertical structure covering the different production stages of the home textile, from weaving until the finished products. Production planning comprises different decisions: the definition of the widths and lengths of the fabric rolls to be produced, the number of fabric rolls to be used from stock or purchased and the definition of the cutting patterns to be applied to each width of the fabric roll, so that the waste is minimized. We propose a MIP model, solved by a column generation method, to tackle the problem.