Abstract
In many scenarios, such as the ones related to Data Warehousing Extract-Transform-Load (ETL) processes, logging techniques are usually applied for capturing event metrics across system levels for system auditing and system recovery. The diversity of strategies and architectures of the toolset used to support the ETL implementation introduces another layer of complexity, both for system development and audit. Although a valuable system diagnosis resource for the development team, logging is generally underestimated, being used only when the system reveals unexpected behaviours and not to drive the ETL system evolution. We believe that the use of logs for steering ETL development and maintenance can improve significantly global system quality. However, this approach is only effective if flexible and efficient logging systems exist. In this paper, we describe a Log Pattern used in a pattern-oriented approach for ETL systems development, which provides a configurable and flexible component for using to drive ETL development and maintenance phases.

Abstract
Hub Location Problems are complex combinatorial optimization problems that raised a lot of interest in the literature and have a huge number of practical applications, going from the telecommunications, airline transportation among others. In this paper we propose a primal-dual algorithm to solve the Uncapacitated Multiple Allocation Hub Location Problem (UMAHLP). RAMP algorithm combines information of traditional Dual Ascent procedure on the dual side with an improvement method on the primal side, together with adaptive memory structures. The overall performance of the proposed algorithm was tested on standard Australian Post (AP) and Civil Aeronautics Boarding (CAB) instances, comprising 192 test instances. The effectiveness of our approach has been proven by comparing with other state-of-the-art algorithms. © Springer Nature Switzerland AG 2019.

Abstract
Logistic decisions involving the location of facilities in connection with vehicle routing appear in many contexts and applications. Given a set of potential distribution centers (DC) and a group of clients, the choice of which DC to open together with the design of a number of vehicle routes, satisfying clients' demand, may define Location-Routing Problems (LRP). This paper contributes with a new method, the 4-Phase Method (4-PhM), to deal with Capacitated LRP. Relevant advantages of 4-PhM are its generality, the possibilities of handling Multiple-Criteria and of facing large dimension problems. This last aptitude is a consequence of the sectorization phases, which permit a simplification of the solution space. Sectors are constructed by two Simulated Annealing based procedures, and they follow SectorEl, a sectorization approach inspired by electrostatics. In the last phase, the results obtained are evaluated using multicriteria analysis. Here, decision makers play an important role by reflecting preferences in a pairwise comparison matrix of the Analytic Hierarchy Process. Computational results, based on randomly generated instances, confirm the expectations about 4-PhM and its potentiality to deal with LRP.

Abstract
This paper addresses new Mixed-model Assembly Line Balancing Problems (MALBP) in a real industrial context, the stitching systems of a footwear company. The work is part of large ongoing projects with this industry, and the main purposes are minimising the number of required workstations and smoothing the operators' workload. The company has invested in new flexible automated assembly systems, which accommodate dozens of workstations and many moving boxes. Footwear components are inside boxes (with various quantities) which can move from the warehouses to a convenient workstation or between any workstations (in any order). This is a significant and distinct feature of the MALBP, together with the fact that the assignment of different skilled operators and machines is achieved simultaneously. An optimisation model is developed, in part to facilitate the understanding of the situation and to solve small-size instances. Due to the complexity of the problems, we had to devise an approximate method, based on the Variable Neighbourhood Descent (VND) metaheuristic and integrating an adaptation of the Ranked Positional Weighted (RPW) method. The adapted RPW method is used to create initial feasible solutions, while preassigning special operators and machines. After choosing good initial solutions, VND is applied to improve their quality. The new contributed method, named as RPW-VNDbal, is tested with medium and large instances, in two distinct stitching systems. A Lower Bound of the objective function and Simulation contribute to evaluate the solutions and their practicability. The results implemented by the project team, show that the RPW-VNDbal method is fast enough and offers better solutions than those implemented by the experienced operation managers of the company.

Abstract
The load balance aspect of the Container Loading Problem (CLP) has been handled in an simplified way in the literature. Either load balance has been treated as a soft constraint or the geometrical centre of the container has been assumed to be the ideal location for the centre of gravity of the cargo, or both, which does not meet regulatory directives and transportation legislation. In this paper, we treat load balance as a 'hard constraint and adopt vehicle specific diagrams that define the feasibility domain for the location of the centre of gravity of the cargo, according to the vehicle specific technical characteristics, thus fulfilling and complying with real-world regulations and legislation. We propose a multi-population biased random-key genetic algorithm (BRKGA), with a new fitness function that takes static stability and load balance into account. Extensive computational experiments were performed with different variants of the proposed approach. Also solutions taken from the literature were evaluated in terms of load balance. The computational results show that it is possible to obtain stable and load balanced solutions without compromising the performance in terms of container volume utilization, and demonstrate also the advantage in incorporating load balance in the packing generation algorithm.

Abstract
Retailers' individual products are categorized as part of product families. Merchandising rules specify how the products should be arranged on the shelves using product families, creating more structured displays capable of increasing the viewers' attention. This paper presents a novel mixed integer programming formulation for the Shelf Space Allocation Problem considering two innovative features emerging from merchandising rules: hierarchical product families and display directions. The formulation uses single commodity flow constraints to model product sequencing and explores the product families' hierarchy to reduce the combinatorial nature of the problem. Based on the formulation, a mathematical programming-based heuristic was also developed that uses product families to decompose the problem into a sequence of sub-problems. To improve performance, its original design was adapted following two directions: recovery from infeasible solutions and reduction of solution times. A new set of real case benchmark instances is also provided, which was used to assess the formulation and the matheuristic. This approach will allow retailers to efficiently create planograms capable of following merchandising rules and optimizing shelf space revenue.