Moacir Felizardo de França Filho

Minimizing total tardiness in parallel machine scheduling with setup times: An adaptive memory-based GRASP approach

This paper deals with the problem of scheduling jobs in uniform parallel machines with sequence-dependent setup times in order to minimize the total tardiness relative to job due dates. We propose GRASP versions that incorporate adaptive memory principles for solving this problem. Long-term memory is used in the construction of an initial solution and in a post-optimization procedure which connects high quality local optima by means of path relinking. Computational tests are carried out on a set of benchmark instances and the proposed GRASP versions are compared with heuristic methods from the literature.

An immune-inspired algorithm for an unrelated parallel machines' scheduling problem with sequence and machine dependent setup-times for makespan minimisation

This paper proposes an immune-inspired algorithm to the problem of minimising the makespan on unrelated parallel machines, with sequence dependent setup times. The initial population is generated through the construction phase of the Greedy Randomised Adaptive Search Procedure (GRASP). An evaluation function is proposed to help the algorithm escape from local optima. A Variable Neighbourhood Descent (VND) local search heuristic, which makes significant use of the characteristics of the problem, is proposed as a somatic hypermutation operator to accelerate the convergence of the algorithm. A population re-selection operator, which strategically keeps good quality solutions with a high level of dispersion in the search space, is also proposed. The experiments performed show that the proposed algorithm enables better results than those reported in recent literature studies.

A Hybrid Particle Swarm Optimization for Solving Vehicle Routing Problem with Time Windows

This paper presents a hybrid Particle Swarm Optimization (PSO) for solving Vehicle Routing Problem with Time Windows (VRPTW). Three versions of the algorithm were implemented. The first version is a traditional PSO. In this case, the initialization is random and the best insertion for each customer on the route is evaluated. The second version is a combination of Greedy Randomized Adaptive Search Procedure (GRASP) and Push-Forward Insertion Heuristic (PFIH) with PSO. The last version, in addition to the previous characteristics, features a mutation operator after updating speed and position of each particle. For computational experiments, the

A Clonal Selection Algorithm for Makespan Minimization on Unrelated Parallel Machines with Sequence Dependent Setup Times

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A Variable Neighborhood Descent as ILS local search to the minimization of the total weighted tardiness on unrelated parallel machines and sequence dependent setup times

Solomons instances are used and the results obtained in each version are compared with the best known results from literature. A statistical analysis indicates that the third version has a better performance than the other versions.

Hybrid metaheuristics and multi-agent systems for solving optimization problems: A review of frameworks and a comparative analysis

This work presents a proposal to implement the clonal selection algorithm (CLONALG) for the make span minimization problem on unrelated parallel machines with sequence-dependent setup times. The initial population is created through the GRASP algorithm, whereas an evaluation function based on make span values and on a historical of recent improvements helps the algorithm to escape from local optima. A local search heuristic, based in the VND method, was used as the hyper mutation somatic operator, in order to accelerate the convergence of the algorithm. The performed experiments show that the proposed operators improve the performance of standard CLONALG algorithm as well as allow to obtain better results than those presented in the most recent literature.

Hybrid metaheuristic for combinatorial optimization based on immune network for optimization and VNS

Abstract This paper addresses the total weighted tardiness minimization problem on unrelated parallel machines with sequence dependent setup times and job ready times. The problem consists in scheduling a set of jobs reducing the penalty costs caused by the delays in the job due dates. This is a NP-Hard problem and has been extensively studied in recent literature. In order to solve this, an ILS-VND hybrid metaheuristic is proposed, where a local search heuristic Variable Neighborhood Descent (VND) is integrated with Iterated Local Search (ILS) metaheuristic with multiple restarts. The results is compared with two state-of-art metaheuristics proposed in the literature. The statistical analysis indicates that for the most scenarios the proposed method outperforms the references metaheuristics.

Resolução Do Problema Das p-Medianas Por Meio De Algoritmos Baseados Em GRASP, ILS E Multi-Start

Abstract This article presents a review and a comparative analysis between frameworks for solving optimization problems using metaheuristics. The aim is to identify both the desirable characteristics as the existing gaps in the current state of the art, with a special focus on the use of multi-agent structures in the development of hybrid metaheuristics. A literature review of existing frameworks is introduced, with emphasis on their characteristics of hybridization, cooperation, and parallelism, particularly focusing on issues related to the use of multi-agents. For the comparative analysis, a set of twenty-two characteristics was listed, according to four categories: basics, advanced, multi-agent approach and support to the optimization process. Strategies used in hybridization, such as parallelism, cooperation, decomposition of the search space, hyper-heuristic and multi-agent systems are assessed in respect to their use in the various analyzed frameworks. Specific features of multi-agent systems, such as learning and interaction between agents, are also analyzed. The comparative analysis shows that the hybridization is not a strong feature in existing frameworks. On the other hand, proposals using multi-agent systems stand out in the implementation of hybrid methods, as they allow the interaction between metaheuristics. It also notes that the concept of hyper-heuristic is little explored by the analyzed frameworks, as well as there is a lack of tools that offer support to the optimization process, such as statistical analysis, self-tuning of parameters and graphical interfaces. Based on the presented analysis, it can be said that there are important gaps to be filled in the development of Frameworks for Optimization using metaheuristics, which open important possibilities for future works, particularly by implementing the approach of multi-agent systems.

A Parallel Approach of a Hybrid Particle Swarm Optimization Algorithm to Solve the Vehicle Routing Problem with Time Windows

Metaheuristics for optimization based on the immune network theory are often highlighted by being able to maintain the diversity of candidate solutions present in the population, allowing a greater coverage of the search space. This work, however, shows that algorithms derived from the aiNET family for the solution of combinatorial problems may not present an adequate strategy for search space exploration, leading to premature convergence in local minimums. In order to solve this issue, a hybrid metaheuristic called VNS-aiNET is proposed, integrating aspects of the COPT-aiNET algorithm with characteristics of the trajectory metaheuristic Variable Neighborhood Search (VNS), as well as a new fitness function, which makes it possible to escape from local minima and enables it to a greater exploration of the search space. The proposed metaheuristic is evaluated using a scheduling problem widely studied in the literature. The performed experiments show that the proposed hybrid metaheuristic presents a convergence superior to two approaches of the aiNET family and to the reference algorithms of the literature. In contrast, the solutions present in the resulting immunological memory have less diversity when compared to the aiNET family approaches.

An algorithm based on Evolution Strategies for makespan minimization in hybrid flexible flowshop scheduling problems

Resumo – Este trabalho aborda o Problema das p-Medianas por meio de algoritmos baseados nas ametaheurísticas Greedy Randomized Adaptive Search Procedure (GRASP), Iterated Local Search (ILS) e Multi-Start. Esses algoritmos utilizam, como método de busca local, o algoritmo Fast Swap-based Local Search. Os experimentos computacionais foram realizados com dois conjuntos de instâncias da literatura e mostraram que o algoritmo ILS apresenta o melhor desempenho em termos de tempo de execução e qualidade da solução. Palavras chave – Problema das p-Medianas, GRASP, Iterated Local Search, Multi-Start.