Glaydston Mattos Ribeiro

An adaptive large neighborhood search heuristic for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem (CCVRP) is a variation of the classical capacitated vehicle routing problem in which the objective is the minimization of the sum of arrival times at customers, instead of the total routing cost. This paper presents an adaptive large neighborhood search heuristic for the CCVRP. This algorithm is applied to a set of benchmark instances and compared with two recently published memetic algorithms.

A greedy randomized adaptive search procedure for the point-feature cartographic label placement

The point-feature cartographic label placement problem (PFCLP) is an NP-hard problem, which appears during the production of maps. The labels must be placed in predefined places avoiding overlaps and considering cartographic preferences. Owing to its high complexity, several heuristics have been presented searching for approximated solutions. This paper proposes a greedy randomized adaptive search procedure (GRASP) for the PFCLP that is based on its associated conflict graph. The computational results show that this metaheuristic is a good strategy for PFCLP, generating better solutions than all those reported in the literature in reasonable computational times.

Reverse logistics network for municipal solid waste management: The inclusion of waste pickers as a Brazilian legal requirement.

This study proposes a reverse logistics network involved in the management of municipal solid waste (MSW) to solve the challenge of economically managing these wastes considering the recent legal requirements of the Brazilian Waste Management Policy. The feasibility of the allocation of MSW material recovery facilities (MRF) as intermediate points between the generators of these wastes and the options for reuse and disposal was evaluated, as well as the participation of associations and cooperatives of waste pickers. This network was mathematically modelled and validated through a scenario analysis of the municipality of São Mateus, which makes the location model more complete and applicable in practice. The mathematical model allows the determination of the number of facilities required for the reverse logistics network, their location, capacities, and product flows between these facilities. The fixed costs of installation and operation of the proposed MRF were balanced with the reduction of transport costs, allowing the inclusion of waste pickers to the reverse logistics network. The main contribution of this study lies in the proposition of a reverse logistics network for MSW simultaneously involving legal, environmental, economic and social criteria, which is a very complex goal. This study can guide practices in other countries that have realities similar to those in Brazil of accelerated urbanisation without adequate planning for solid waste management, added to the strong presence of waste pickers that, through the characteristic of social vulnerability, must be included in the system. In addition to the theoretical contribution to the reverse logistics network problem, this study aids in decision-making for public managers who have limited technical and administrative capacities for the management of solid wastes.

Lagrangean relaxation with clusters for point-feature cartographic label placement problems

This paper presents two new mathematical formulations for the point-feature cartographic label placement problem (PFCLP) and a new Lagrangean relaxation with clusters (LagClus) to provide bounds to these formulations. The PFCLP can be represented by a conflict graph and the relaxation divides the graph in small subproblems (clusters) that are easily solved. The edges connecting clusters are relaxed in a Lagrangean way and a subgradient algorithm improves the bounds. The LagClus was successfully applied to a set of instances up to 1000 points providing the best results of those reported in the literature.

A decomposition approach for the probabilistic maximal covering location-allocation problem

The maximal covering location problem (MCLP) maximizes the population that has a facility within a maximum travel distance or time. Numerous extensions have been proposed to enhance its applicability, like the probabilistic model for the maximum covering location-allocation with a constraint in waiting time or queue length for congested systems, with one or more servers per service center. This paper presents a solution procedure for that probabilistic model, considering one server per center, using a column generation and covering graph approaches. The computational tests report interesting results for network instances up to 818 vertices. The column generation results are competitive solving the instances in reasonable computational times, reaching optimality for some and providing good bounds for the difficult instances.

A comparison of three metaheuristics for the workover rig routing problem

The workover rig routing problem (WRRP) is a variant of the Vehicle Routing Problem with Time Windows (VRPTW) and arises in the operations of onshore oil fields. In this problem, a set of workover rigs located at different positions must service oil wells requesting maintenance as soon as possible. When a well requires maintenance, its production is reduced or stopped for safety reasons and some workover rig must service it within a given deadline. It is therefore important to service the wells in a timely fashion in order to minimize the production loss. Whereas for classical VRPTWs the objective is to minimize route length, in the WRRP the objective is to minimize the total lost production, equal to the sum of arrival times at the wells, multiplied by production loss rates. The WRRP generalizes the Delivery Man Problem with Time Windows by considering multiple open vehicle routes and multiple depots. This paper compares three metaheuristics for the WRRP: an iterated local search, a clustering search, and an Adaptive Large Neighborhood Search (ALNS). All approaches, in particular ALNS, have yielded good solutions for instances derived from a real-life setting.

An adaptive large neighborhood search for the discrete and continuous Berth allocation problem

The Berth Allocation Problem (BAP) consists of assigning ships to berthing positions along a quay in a port. The choice of where and when the ships should move is the main decision to be made in this problem. Considering the berthing positions, there are restrictions related to the water depth and the size of the ships among others. There are also restrictions related to the berthing time of the ships which are modeled as time windows. In this work the ships are represented as rectangles to be placed into a space ×time area, avoiding overlaps and satisfying time window constraints. We consider discrete and continuous models for the BAP and we propose an Adaptive Large Neighborhood Search heuristic to solve the problem. Computational experiments indicate that the proposed algorithm is capable of generating high-quality solutions and outperforms competing algorithms for the same problem. In most cases the improvements are statistically significant. HighlightsWe propose an Adaptive Large Neighborhood Search (ALNS) heuristic for both discrete and continuous cases of the Berth Allocation Problem.A very broad set of instances were used and our heuristic provided good solutions within low computational time.A sensitivity analysis was reported by changing the number of ships and berths.Statistical tests were performed over the obtained results.Our ALNS produced high quality results and was superior to the competing algorithms on most instances.

A simple and effective genetic algorithm for the two-stage capacitated facility location problem ☆

This paper presents a simple and effective Genetic Algorithm (GA) for the two-stage capacitated facility location problem (TSCFLP). The TSCFLP is a typical location problem which arises in freight transportation. In this problem, a single product must be transported from a set of plants to meet customers demands, passing out by intermediate depots. The objective is to minimize the operation costs of the underlying two-stage transportation system thereby satisfying demand and capacity constraints of its agents. For this purpose, a GA is proposed and computational results are reported comparing the heuristic results with those obtained by two state-of-the-art Lagrangian heuristics proposed in the literature for the problem.

Lagrangean relaxation with clusters and column generation for the manufacturer's pallet loading problem

We consider in this paper a new lagrangean relaxation with clusters for the Manufacturers Pallet Loading Problem (MPLP). The relaxation is based on the MPLP formulated as a Maximum Independent Set Problem (MISP) and represented in a conflict graph that can be partitioned in clusters. The edges inter clusters are relaxed in a lagrangean fashion. Computational tests attain the optimality for some instances considered difficult for a lagrangean relaxation. Our results show that this relaxation can be a successful approach for hard combinatorial problems modeled in conflict graphs. Moreover, we propose a column generation approach for the MPLP derived from the idea behind the lagrangean relaxation proposed.

A Clustering Search metaheuristic for the Point-Feature Cartographic Label Placement Problem

The Point-Feature Cartographic Label Placement (PFCLP) problem consists of placing text labels to point features on a map avoiding overlaps to improve map visualization. This paper presents a Clustering Search (CS) metaheuristic as a new alternative to solve the PFCLP problem. Computational experiments were performed over sets of instances with up to 13,206 points. These instances are the same used in several recent and important researches about the PFCLP problem. The results enhance the potential of CS by finding optimal solutions (proven in previous works) and improving the best-known solutions for instances whose optimal solutions are unknown so far.