Telmo Matos

The Hub Location Problems (HLP) have gathered great interest due to the complexity and to the many applications in industry such as aviation, public transportation, telecommunications, among others. The HLP have many variants regarding allocation (single or multiple) and capacity (uncapacitated or capacitated). This paper presents a variant of the HLP, encompassing single allocation with capacity constraints. The Capacitated Single Allocation p-Hub Location Problem (CSApHLP) objective consists on determine the set of p hubs in a network that minimizes the total cost of allocating all the non-hub nodes to the p hubs. In this work, it is proposed a sophisticated RAMP approach (PD-RAMP) to improve the results obtained previously by the simple version (Dual-RAMP). Thus, a parallel implementation is conducted to assess the effectiveness of a parallel RAMP model applied to the CSApHLP. The first algorithm, the sequential PD-RAMP, incorporates Dual-RAMP with a Scatter Search procedure to c...

This paper presents a Dual-RAMP algorithm for the solution of the multiple allocation hub location problem (UMAHLP). This approach combines information of a lagrangean relaxation procedure with subgradient optimization on the dual side with primal-feasible solutions on primal side, that are obtained by a simple improvement method. 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 our results with other state-of-the-art algorithms.

In 2018, 70% of the Portuguese companies produced thousands of Portuguese Audit Tax documents (SAF-T (PT)) files for tax validation. These documents represent a standardized procedure for the Portuguese companies, providing the necessary data about billing, accounting, and taxation. These files contain valuable information that can represent an important tool for analytical procedures to support decision-making processes. Thus, a Business Intelligence System based on SAF-T (PT) was created to support companies' analytical needs. An important decision-making process involves customer evaluation. So, the proposed system will begin, in a preliminary phase, with RFM analysis, for customer segmentation using Clustering techniques and results were cross-validated using Decision Tree and Linear Discriminant Analysis. Additionally, a brief interpretation of marketing strategies was suggested and a comparison between Rapid Miner and SPSS was synthesized. The results show that it is possi...

In this paper, we address the Capacitated Facility Location Problem (CFLP) in which the assignment of facilities to customers must ensure enough facility capacity and all the customers must be served. We propose both sequential and parallel Relaxation Adaptive Memory Programming approaches for the CFLP, combining a Lagrangean subgradient search with an improvement method to explore primal-dual relationships to create advanced memory structures that integrate information from both primal and dual solution spaces. Computational experiments of the effectiveness of this approach are presented and discussed.

In this paper, we address the Single Source Capacitated Facility Location Problem (SSCFLP) which considers a set of possible locations for opening facilities and a set of clients whose demand must be satisfied. The objective is to minimize the cost of assigning the clients to the facilities, ensuring that all clients are served by only one facility without exceeding the capacity of the facilities. We propose a Relaxation Adaptive Memory Programming (RAMP) heuristic for solving the SSCFLP to efficiently explore the relation between the primal and the dual sides of this combinatorial optimisation problem. Computational experiments demonstrated that the proposed heuristic is very effective in terms of solution quality with reasonable computing times.

We consider the Capacitated Single Allocation Hub Location Problem (CSAHLP) in which the objective is to choose the set of hubs from all nodes in a given network in such way that the allocation of all the nodes to the chosen hubs is optimal. We propose a Relaxation Adaptive Memory Programming (RAMP) approach for the CSAHLP. Our method combines Lagrangean Subgradient search with an improvement method to explore primal-dual relationships and create advanced memory structures that integrate information from both primal and dual solutions spaces. The algorithm was tested on the standard dataset and produced extremely competitive results that include new best-known solutions. Comparisons with the current best performing algorithms for the CSAHLP show that our RAMP algorithm exhibits excellent results.

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.

Facility location embodies a class of problems concerned with locating a set of facilities to serve a geographically distributed population of customers at minimum cost. We address the classical Capacitated Facility Location Problem (CFLP) in which the assignment of facilities to customers must ensure sufficient facility capacity and that each customer is served by only one facility. This is a well-known NP-Hard problem in Combinatorial Optimization that has been extensively studied in the literature. Due to the difficulty of the problem significant research efforts have been devoted to developing advanced heuristics methods aimed at finding high-quality solutions in reasonable computation times. We propose a Relaxation Adaptive Memory Programming (RAMP) approach for the CFLP. Our method combines Lagrangian Subgradient Search with Tabu Search to explore primal-dual relationships as a way to create advanced memory structures that integrate information from both primal and dual soluti...

Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physicochemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certification step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a regression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful for understanding how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production.