Dario J. Aloise

A Hybrid Improvement Heuristic for the One-Dimensional Bin Packing Problem

We propose in this work a hybrid improvement procedure for the bin packing problem. This heuristic has several features: the use of lower bounding strategies; the generation of initial solutions by reference to the dual min-max problem; the use of load redistribution based on dominance, differencing, and unbalancing; and the inclusion of an improvement process utilizing tabu search. Encouraging results have been obtained for a very wide range of benchmark instances, illustrating the robustness of the algorithm. The hybrid improvement procedure compares favourably with all other heuristics in the literature. It improved the best known solutions for many of the benchmark instances and found the largest number of optimal solutions with respect to the other available approximate algorithms.

Scheduling workover rigs for onshore oil production

Many oil wells in Brazilian onshore fields rely on artificial lift methods. Maintenance services such as cleaning, reinstatement, stimulation and others are essential to these wells. These services are performed by workover rigs, which are available on a limited number with respect to the number of wells demanding service. The decision of which workover rig should be sent to perform some maintenance service is based on factors such as the well production, the current location of the workover rig in relation to the demanding well, and the type of service to be performed. The problem of scheduling workover rigs consists in finding the best schedule for the available workover rigs, so as to minimize the production loss associated with the wells awaiting for service. We propose a variable neighborhood search (VNS) heuristic for this problem. Computational results on real-life problems are reported and their economic impacts are evaluated.

On the Weber facility location problem with limited distances and side constraints

The objective in the continuous facility location problem with limited distances is to minimize the sum of distance functions from the facility to the customers, but with a limit on each of the distances, after which the corresponding function becomes constant. The problem has applications in situations where the service provided by the facility is insensitive after a given threshold distance. In this paper, we propose a global optimization algorithm for the case in which there are in addition lower and upper bounds on the numbers of customers served.

Modeling and solving the bi-objective minimum diameter-cost spanning tree problem

The bi-objective minimum diameter-cost spanning tree problem (bi-MDCST) seeks spanning trees with minimum total cost and minimum diameter. The bi-objective version generalizes the well-known bounded diameter minimum spanning tree problem. The bi-MDCST is a NP-hard problem and models several practical applications in transportation and network design. We propose a bi-objective multiflow formulation for the problem and effective multi-objective metaheuristics: a multi-objective evolutionary algorithm and a fast nondominated sorting genetic algorithm. Some guidelines on how to optimize the problem whenever a priority order can be established between the two objectives are provided. In addition, we present bi-MDCST polynomial cases and theoretical bounds on the search space. Results are reported for four representative test sets.

Modeling the Mobile Oil Recovery Problem as a Multiobjective Vehicle Routing Problem

The Mobile Oil Recovery (MOR) unit is a truck able to pump marginal wells in a petrol field. The goal of the MOR optimization Problem (MORP) is to optimize both the oil extraction and the travel costs. We describe several formulations for the MORP using a single vehicle or a fleet of vehicles. We have also strengthened them by improving the subtour elimination constraints. Optimality is proved for instances close to reality with up to 200 nodes.

An exact method for solving the bi-objective Minimum Diameter-Cost Spanning Tree Problem

In this work, we propose a procedure to compute Pareto-optimal fronts for the bi-objective Minimum Diameter-Cost Spanning Tree problem (bi-MDCST). The bi-MDCST aims at finding spanning trees with minimum total cost and minimum diameter. Strategic decision problems for high-speed trains infrastructure, as well as tactical and operational optimization problems for network design and transportation can be modeled as bi-MDCST. The proposed exact procedure makes use of components from the multi-objective exact method Parallel Partitioning Method, and Pareto-optimal fronts have been computed for two benchmark instances from the literature. To the best of our knowledge, there are no works dedicated to providing Pareto-optimal fronts for the bi-MDCST.

Reducing violence: A proposal based on multicriteria SMARTS method

The use of different public policies for certain areas in the same region is a strategic issue for public safety management. Therefore, this article has developed a multicriteria model based on SMARTS method that prioritizes areas for a given region using demographic and socioeconomic issues. It used the “swing weights” procedure to elicit preferences for scale constants. This model was applied in Recife, where it was divided into human development units. The sensitivity analysis of scale constants helped to show that this model is robust, since there were few and not significant changes in the positioning of zones.