Purpose The purpose of this paper is to present a hybrid meta-heuristic based on genetic algorithms (GAs), simulated annealing, variable neighborhood descent and path relinking for solving the variant of the unrelated parallel machine scheduling problem considering sequence-dependent setup times. Design/methodology/approach The authors carried out computational experiments on literature problem instances proposed by Vallada and Ruiz (2011) and Arnaout et al. (2010) to test the performance of the proposed meta-heuristic. The objective function adopted was makespan minimization, and the authors used relative deviation, average and population standard deviation as performance criteria. Findings The results indicate the competitivity of the proposed approach and its superiority in comparison with several other algorithms. In small instances proposed by Vallada and Ruiz (2011) and on small and large instances proposed by Arnaout et al. (2010), the proposed approach presented the best results in most tested problem instances. Practical implications In small instances proposed by Vallada and Ruiz (2011) and on small and large instances proposed by Arnaout et al. (2010), the proposed approach presented the best results in most tested problem instances. Originality/value The proposed approach presented high-quality results, with an innovative hybridization of a GA and neighborhood search algorithms, tested in diverse instances of literature. Furthermore, the case study demonstrated that the proposed approach is recommended for solving real-world problems.

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a b s t r a c tThe oil field development is a hard and critical task that defines the main procedures to be performed during the oil field productive life. Given the complexity of this planning phase, methods to support decision making have been developed to assist in the proper application of high investments. This paper aims to report a 0-1 Linear Programming Model which minimizes the development costs of a given oil field as a whole. The model seeks to define: the number, location and capacities of production platforms; number and positions of wells; points where manifolds must be installed; interconnection between platforms, manifolds and wells; and which sections of each well should be vertical or horizontal. The model was named Multicapacitated Platforms and Wells Location Problem (MPWLP). Two different scenarios were tested and the results were consistent with reality, computationally feasible and presented innovations compared to models found in literature.

This paper summarizes a mathematical model that relates the geometric and geotechnical features of a road construction site with the allocation of materials, searching for a minimum construction cost. This paper proposes a linear programming model to optimize excavation and paving services. With this model, it is possible to evaluate site alternatives with different soil strata and different degrees of compaction. The borrow pit materials are allocated in the most economical way, and it is possible to incorporate more inputs like materials mix. Software was used to solve the model, and a spreadsheet application was used as an interface for data input. The proposed model demonstrated possible cost savings in earthwork planning. It is expected that earthwork and paving optimization with linear programming will reduce road construction costs considerably.

In this paper a smart home controller proposal is formalized as a multi-objective integer linear programming problem that minimizes energy consumption and maximizes comfort. A comfort objective function is tested for several tariff scenarios including one with renewable sources as local off-grid micro-generation. The proposed model specifies best times to activate real household appliances based on energy consumption data, given load-limiting constraint and user preferences, by use of a weighted aggregation function. The proposed scenarios have shown excellent results for energy saving without a significant reduction in comfort.