Abstract
We address two two-dimensional bin packing problems where the bins are rectangular and have the same size. The items are also rectangular and all of them must be packed with the objective of minimizing the number of bins. In the first problem, the two-stage problem, the items must be packed in levels. In the second problem, the restricted 3-stage problem, items can be grouped in stacks which are packed in levels. We propose a new decomposition model where subproblems are associated with the item that initializes a bin. The decomposition is solved by a heuristic which combines (perturbed) column generation, local search, beam branch-and-price, and the use of a general purpose mixed integer programming solver. This approach is closely related with SearchCol, a framework for solving integer programming / combinatorial optimization decomposition models. Computational results with 500 instances from the literature show that the proposed hybrid heuristic is efficient in obtaining high quality solutions. It uses more 8 and 17 bins than the 7364 and 7340 bins of a compact model from the literature for the 2 and 3-stage problems, respectively, while the sum of the time spent for all instances is 35% and 58% of the time spent by the compact model.

Abstract
To cope with today market challenges and guarantee adequate competitive performances, companies have been decreasing their products life cycles, as well as increasing the number of product varieties and respective services available on their portfolio. Consequently, it has been observed an increasing in complexity in all domains, from product and process development, factory and production planning to factory operation and management. This reality implies that organizations should be able to compile and analyze, in a more agile way, the immense quantity of data generated, as well as apply the suitable tools that, based on this knowledge, will supports stakeholders to take decision envisioning future performance scenarios. Aiming to pursuing this vision was developed a proactive performance management framework, composed by a performance thinking methodology and a performance estimation engine. While the methodology developed is an extension of the Systems Dynamics approach for complex systems' performance management, on the other hand, the performance estimation engine is an innovative IT solution responsible by capturing lagging indicators, as well as estimate future performance behaviors. As main outcome of this research work, it was demonstrated that following a systematic and formal approach, it is possible to identify the feedback loops and respective endogenous and exogenous variables responsible by hindering the systems behavior, in terms of a specific KPI. Moreover, based on this enhanced understanding about manufacturing systems behavior, it was proved to be possible to estimate with high levels of confidence not only the present but also future performance behavior. From the combination of both qualitative and quantitative approaches, it was explored an enhanced learning machine algorithm capable to specify the curve of behavior, characteristic from a specific manufacturing system, and thus estimate future behaviors based on a set of leading indicators. In order to achieve these objectives, both Neural Networks and Unscented Kalman Filter for nonlinear estimation were applied. Important results and conclusions were extracted from an application case performed within a real automotive plant, which demonstrated the feasibility of this research towards a more proactive management approach.

Abstract
The recent past has shown that many companies stressed from the competition, have reduced manufacturing costs as well implemented sustainable practices as much as possible. And yet this effort has proved to be insufficient. This reality is forcing the companies and managers to address the problem of competitiveness and sustainability in a holistic way, by considering the entire supply chain. Due to this pressure from supply chain stakeholders to a comprehensive sustainability assessment of the entire network, extended “performance metrics” are required not only on the economic value of a business, but also in its environmental and social impacts. Increasing numbers of organizations report a massive volume of data, with low consistency and high variability in data quality, and a dispersion of indicators, making it necessary to develop and implement new approaches for Supply Cain Management (SCM) sustainable performance assessment. This paper focuses on this topic, presenting a new approach for performance and risk assessment within dynamic supply chain networks, supported in a new and comprehensive Sustainability Assessment Framework (SAF).

Abstract
In the ambit of Distribute/Agile/Virtual Enterprises, the resources/partners selection process is a critical issue in order to guarantee the success of such enterprises. The selection process is complex in the large sense of the word and for that we advocate the necessity of a broker to perform that task, conveniently assisted by a tool. In order to contribute to its construction, this paper presents the resolution of a resources system selection problem, designated by Dependent or Integral Selection Method without Pre-selection of Transport Resources, with two algorithms, an exact solution algorithm and an approximate one. The results demonstrate that the exact solution algorithm limitations can be covered by the approximate algorithm. With those results, the broker has the knowledge to perform the selection with the most adequate algorithm for each case of the problem (depending of the number of tasks and pre-selected resources) addressed in this paper. This paper brings a contribution to broker performance for the selection process. (C) 2014 The Authors. Published by Elsevier Ltd.

Abstract
In this paper we present a hybrid exact-heuristic method to improve a branch-and-price algorithm to solve the unrelated parallel machines with sequence-dependent setup times scheduling problem. As most of the computational time in the column generation (CG) process is spent in subproblems, two new heuristics to solve the subproblems are embedded in the branch-and-price (BP) framework with the aim to improve the efficiency of the process in obtaining optimal solutions. Computational results show that the proposed method improves a state-of-the-art BP algorithm from the literature, providing optimal solutions for large instances (e. g. 50 machines and 180 jobs) of the parallel machine scheduling problem with sequence dependent setup times, in significantly less time. One of the proposed approaches reduces, in average, to a half the time spent in the root of the branch-and-price tree and to a quarter the time spent in the full branch-and-price algorithm.

Abstract
In this paper, we present a novel mixed integer linear programming (MIP) model to solve the problem of storage space optimization in a port grain cereal silo. This work is based on a real case study for scheduling storage operations in a port cereal silo, where schedulers are faced daily with the problem of finding the best solution for transfers between storage cells, to maximize the number of empty cells, in order to have greater capacity to receive new lots, subject to storage and transportation lines capacity constraints, and receiving dispatching plans. The problem is formulated by a mixed integer linear programming model and implemented in an Excel/VBA platform. The results show that the model optimizes the number of empty cells, in computational time less than 60 seconds, and thereby constitutes a significant added value to the concerned company. © 2015 Taylor & Francis Group, London.