Clustering search

This paper presents the Clustering Search (CS) as a new hybrid metaheuristic, which works in conjunction with other metaheuristics, managing the implementation of local search algorithms for optimization problems. Usually the local search is costly and should be used only in promising regions of the search space. The CS assists in the discovery of these regions by dividing the search space into clusters. The CS and its applications are reviewed and a case study for a problem of capacitated clustering is presented.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Universidade Federal do MaranhãoUniversidade Federal de São Paulo (UNIFESP)Instituto Nacional de Pesquisas EspaciaisUNIFESPSciEL

Optimal scheduling of field activities using constraint satisfaction problem theory

The challenge of identifying problematic wells and planning their workover operations is common in oil and gas fields. On top of this, the well intervention resources are seldom easily accessible so it is crucial to target the right set of wells at the right time. Oil and gas reservoirs are complex dynamic systems the production and injection patterns of which can significantly affect the reservoir and well response. This represents a complex mathematical optimisation problem where the overall life performance of the field strongly depends on the workover planning decisions. This work presents a reliable and effective tool that is able to screen and explore the large search space of the potential work-overs that adds value to the reservoir management process. The proposed solution considers the overall performance of the field throughout a specified period while respecting all operational limitations as well as considering the risks and costs of the interventions. The proposed workflow combines the commercial optimiser techniques with constraint satisfaction problem optimiser to identify the optimal workover scheduling. The schedule found is guaranteed to satisfy all predefined field constraints. The presented results showed better performance achieved by the proposed hybrid optimiser compared to classical gradient-free optimisation techniques such as Genetic Algorithm in maximising the defined objective function. The suggested workflow can greatly enhance the decisions related to field development and asset management involved with large number of wells and with limited intervention resources

Uma meta-heurística Adaptive Large Neighborhood Search com mecanismos de paralelismo, detecção de estagnação e perturbações para o problema de roteamento de veículos com frota heterogênea, periódico e Multi-Trips

The planning of vehicle routes is a major issue involved in supply chains. In real environment we can find situations involving a very large number of clients or constraints witch indicate that exact methods should be avoided. In this context, this work presents an metaheuristic for solving some variants of the vehicle routing problem (VRP): Heterogeneous VRP, VRP Periodic and VRP with multi-trips. The metaheuristic chosen, called Adaptive Large Neighborhood Search (ALNS), combines the power of successful strategies in the literature as a large neighborhood search and adaptive mechanisms with new features such as parallelism, detection of stagnation and perturbations. Our ALNS was implemented in such a way that all variants of the VRP are solved without changes in the code. The results for several instances proposed in the literature are satisfactory, showing the good performance of the approach.A atribuição e o planejamento de rotas de veículos são problemas importantes envolvidos nas cadeias de suprimentos. Em ambiente real é comum encontrar situações que envolvam uma quantidade muito grande de clientes ou de restrições que consequentemente fogem do alcance de métodos exatos. Neste contexto, este trabalho apresenta uma meta-heurística capaz de resolver algumas variantes do problema de roteamento de veículos (PRV) combinadas: o PRV capacitado com frota heterogênea, o PRV periódico e o PRV com multi-trips. A meta-heurística escolhida, denominada Adaptive Large Neighborhood Search (ALNS), combina a força de estratégias bem-sucedidas na literatura como busca em vizinhança ampla e mecanismos adaptativos e também novos mecanismos como paralelismo, detecção de estagnação e perturbações. O ALNS foi implementado de tal maneira que todas as variantes do PRV citadas pudessem ser resolvidas sem alterações de código. Os resultados obtidos, em diversas instâncias propostas na literatura foram satisfatórios, mostrando o bom desempenho do método proposto