Geraldo Regis Mauri

Clustering Search for the Berth Allocation Problem

This work presents a new approach to the Berth Allocation Problem (BAP) for ships in ports. Due to the increasing demand for ships carrying containers, the BAP can be considered as a major optimization problem in marine terminals. In this paper, the BAP is considered as dynamic and modeled in discrete case and we propose a new alternative to solve it. The proposed alternative is based on applying the Clustering Search (CS) method using the Simulated Annealing (SA) for solutions generation. The CS is an iterative method which divides the search space in clusters and it is composed of a metaheuristic for solutions generation, a grouping process and a local search heuristic. The computational results are compared against recent methods found in the literature.

A hybrid column generation approach for the berth allocation problem

Blends of polyphenylene oxide or styrene resin-modified polyphenylene oxide, lactone block copolymers and, optionally, polar resins exhibiting improved impact strength as compared with polyphenylene oxide resin with no additive.

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 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.

General Multiobjective Model and Simulated Annealing Algorithm for Waste-Load Allocation

AbstractWaste-load allocation (WLA) is a difficult problem with multiobjective features, which requires models that consider a full range of competing goals to identify good and practical solutions. In this study, a general and multiobjective optimization model was proposed. This model integrates different decision variables related to multiple waste-removal efficiencies and outflow discharges into water bodies. This model also considered an equity measure and limits for the concentration of dissolved oxygen and biochemical oxygen demand. A simulated annealing (SA) algorithm and the enhanced stream water quality simulation model (QUAL2E) were used to solve the WLA problem. This approach was applied to the Santa Maria da Vitoria River watershed in the state of Espirito Santo, Brazil. Computational results have demonstrated that the proposed optimization model with the SA metaheuristic and QUAL2E could effectively incorporate the expectations and conflicting objectives, providing various good solutions to s...

Clustering search heuristic for solving a continuous berth allocation problem

Due to the increasing demand for ships carrying containers, the Berth Allocation Problem (BAP) can be considered as a major optimization problem in marine terminals. In this context, we propose a heuristic to solve a continuous case of the BAP. This heuristic is based on the application of the Clustering Search (CS) method with the Simulated Annealing (SA) metaheuristic. The results obtained by CS are compared to other methods found in the literature and its competitiveness is verified.

A lagrangean decomposition for the maximum independent set problem applied to map labeling

The Maximum Independent Set (MIS) problem is a well-known problem where the aim is to find the maximum cardinality independent set in an associated graph. Map labeling problems can often be modeled as a MIS problem in a conflict graph, where labels are selected to be placed near graphical features not allowing overlaps (conflicts) between labels or between labels and features. However, the MIS problem is NP-hard and exact techniques present difficulties for solving some instances. Thus, this paper presents a Lagrangean decomposition to solve a map labeling problem, the Point-Feature Label Placement problem. We treated the problem by a conflict graph that is partitioned into small sub-problems and copies of some decision variables are done. These copies are used in the sub-problems constraints and in some constraints to ensure the equality between the original variables and their copies. After these steps, we relax these copy constraints in a Lagrangean way. Using instances proposed in the literature, our approach was able to prove the optimality for all of them, except one, and the results were better than the ones provided by a commercial solver.

A Grasp with Path-Relinking for the Workover Rig Scheduling Problem

Onshore oil wells depend on special services like cleaning, reinstatement and stimulation. These services, which are performed by a short number of workover rigs, are important to keep oil production as optimum as possible. Consequently, scheduling must be determined, where several factors interfere, such as production, service to be performed on each well, and time windows for each service. When a well needs service, its production is interrupted. In this regard, the workover rig scheduling problem consists of finding the best sequence of wells, which minimizes the production loss associated with the wells waiting for maintenance. In this paper, the authors present a Greedy Randomized Adaptive Search Procedure (GRASP) with Path-Relinking (PR) to solve this problem. Computational results are obtained from real problems of a Brazilian oil field.

A new mathematical model and a Lagrangean decomposition for the point-feature cartographic label placement problem

This paper proposes a 0-1 integer linear programming model for the point-feature cartographic label placement problem based on labeling of the largest number of free labels. In addition, one non-trivial valid inequality is presented to strengthen this proposed model. Even with the strengthened model, a commercial solver was not able to solve a representative sample of known instances presented in the literature. Thus, we also present a Lagrangean decomposition technique based on graph partitioning. Our added approaches established optimal solutions for practically all the used instances and the results significantly improved the ones presented in recent studies concerning the problem.