Antonio Augusto Chaves

Simple heuristics for the assembly line worker assignment and balancing problem

We propose simple heuristics for the assembly line worker assignment and balancing problem. This problem typically occurs in assembly lines in sheltered work centers for the disabled. Different from the well-known simple assembly line balancing problem, the task execution times vary according to the assigned worker. We develop a constructive heuristic framework based on task and worker priority rules defining the order in which the tasks and workers should be assigned to the workstations. We present a number of such rules and compare their performance across three possible uses: as a stand-alone method, as an initial solution generator for meta-heuristics, and as a decoder for a hybrid genetic algorithm. Our results show that the heuristics are fast, they obtain good results as a stand-alone method and are efficient when used as a initial solution generator or as a solution decoder within more elaborate approaches.

Clustering search algorithm for the capacitated centered clustering problem

The capacitated centered clustering problem (CCCP) consists in partitioning a set of n points into p disjoint clusters with a known capacity. Each cluster is specified by a centroid. The objective is to minimize the total dissimilarity within each cluster, such that a given capacity limit of the cluster is not exceeded. This paper presents a solution procedure for the CCCP, using the hybrid metaheuristic clustering search (CS), whose main idea is to identify promising areas of the search space by generating solutions through a metaheuristic and clustering them into groups that are then further explored with local search heuristics. Computational results in test problems of the literature show that the CS found a significant number of new best-known solutions in reasonable computational times.

Hybrid Metaheuristic for the Assembly Line Worker Assignment and Balancing Problem

The Assembly Line Worker Assignment and Balancing Problem (ALWABP) appears in real assembly lines which we have to assign given tasks to workers where there are some task-worker incompatibilities and considering that the operation time for each task is different depending upon who executes the task. This problem is typical for Sheltered Work Centers for the Disabled and it is well known to be NP-Hard. In this paper, the hybrid method Clustering Search (CS) is implemented to solve the ALWABP. The CS identifies promising regions of the search space by generating solutions with a metaheuristic, such as Iterated Local Search, and clustering them into clusters that are then explored further with local search heuristics. Computational results considering instances available in the literature are presented to demonstrate the efficacy of the CS.

Clustering Search Heuristic for the Capacitated p -Median Problem

In this paper we present a hybrid heuristic for the capacitated p-median problem (CPMP). This problem considers a set of n points, each of them with a known demand, the objective consists of finding p medians and assign each point to exactly one median such that the total distance of assigned points to their corresponding medians is minimized, and the a capacity limit on the medians may not be exceeded. The purpose of this paper is to present a new hybrid heuristic to solve the CPMP, called Clustering Search (CS), which consists in detecting promising search areas based on clustering. Computational results show that the CS found the best known solutions in all most instances.

Metaheuristics for the feedforward artificial neural network (ANN) architecture optimization problem

This article deals with evolutionary artificial neural network (ANN) and aims to propose a systematic and automated way to find out a proper network architecture. To this, we adapt four metaheuristics to resolve the problem posed by the pursuit of optimum feedforward ANN architecture and introduced a new criteria to measure the ANN performance based on combination of training and generalization error. Also, it is proposed a new method for estimating the computational complexity of the ANN architecture based on the number of neurons and epochs needed to train the network. We implemented this approach in software and tested it for the problem of identification and estimation of pollution sources and for three separate benchmark data sets from UCI repository. The results show the proposed computational approach gives better performance than a human specialist, while offering many advantages over similar approaches found in the literature.

Hybrid heuristics 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 constraint in waiting time or queue length for congested systems, with one or more servers per service center. This paper presents one solution procedure for that probabilistic model, considering one server per center, using a Hybrid Heuristic known as Clustering Search (CS), that consists of detecting promising search areas based on clustering. The computational tests provide results for network instances with up to 818 vertices.

Hybrid evolutionary algorithm for the Capacitated Centered Clustering Problem

Research highlights? Minimum dissimilarity on a network. ? CS identifies promising regions of the search space. ? Clusters are explored with local search heuristics. ? Computational results demonstrate the efficacy of CS. The Capacitated Centered Clustering Problem (CCCP) consists of defining a set of p groups with minimum dissimilarity on a network with n points. Demand values are associated with each point and each group has a demand capacity. The problem is well known to be NP-hard and has many practical applications. In this paper, the hybrid method Clustering Search (CS) is implemented to solve the CCCP. This method identifies promising regions of the search space by generating solutions with a metaheuristic, such as Genetic Algorithm, and clustering them into clusters that are then explored further with local search heuristics. Computational results considering instances available in the literature are presented to demonstrate the efficacy of CS.

Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

The minimization of tool switches problem (MTSP) seeks a sequence to process a set of jobs so that the number of tool switches required is minimized. The MTSP is well known to be NP-hard. This paper presents a new hybrid heuristic based on the Biased Random Key Genetic Algorithm (BRKGA) and the Clustering Search (CS). The main idea of CS is to identify promising regions of the search space by generating solutions with a metaheuristic, such as BRKGA, and clustering them to be further explored with local search heuristics. The distinctive feature of the proposed method is to simplify this clustering process. Computational results for the MTSP considering instances available in the literature are presented to demonstrate the efficacy of the CS with BRKGA. HighlightsThe CS+BRKGA is a hybrid method that detects promising areas and applies local search in these areas.We simplify the clustering process of the CS based on the concept of random keys.The results show that the CS+BRKGA is competitive for solving the MTSP.

Hybrid algorithms with detection of promising areas for the prize collecting travelling salesman problem

The prize collecting travelling salesman problem (PCTSP) is a generalization of the travelling salesman problem. It can be associated to a salesman that collects a prize in each city visited and pays a penalty for each city not visited, with travel costs among the cities. The objective is to minimize the sum of the costs of the trip and penalties, including in the tour an enough number of cities that allow collecting a minimum prize. This paper approaches new heuristics to solve the PCTSP, using a hybrid evolutionary algorithm, called evolutionary clustering search (ECS) and an adaptation of this, called *CS, where the evolutionary component is substituted by the metaheuristics GRASP and VNS. The validation of the obtained solutions are through the comparison with the results found by a commercial solver that was able to solve only small size problems.

Pareto clustering search applied for 3D container ship loading plan problem

Pareto Clustering Search (PCS) is a hybrid method to solve multi-objective problems.PCS detects promising areas and applies local search heuristics only in these areas.We apply the PCS to solve the 3D Container ship Loading Plan Problem (CLPP).The PCS provides better solutions for the CLPP than mono-objective methods.Decision maker chooses the solution that best meets their interests in a situation. The 3D Container ship Loading Plan Problem (CLPP) is an important problem that appears in seaport container terminal operations. This problem consists of determining how to organize the containers in a ship in order to minimize the number of movements necessary to load and unload the container ship and the instability of the ship in each port. The CLPP is well known to be NP-hard. In this paper, the hybrid method Pareto Clustering Search (PCS) is proposed to solve the CLPP and obtain a good approximation to the Pareto Front. The PCS aims to combine metaheuristics and local search heuristics, and the intensification is performed only in promising regions. Computational results considering instances available in the literature are presented to show that PCS provides better solutions for the CLPP than a mono-objective Simulated Annealing.