Antonio Gabriel Rodrigues

Analyzing Different Production Times Applied to the Job Shop Scheduling Problem

The classic objective of the Job Shop Scheduling Problem (JSSP) is to find a sequence of parts with minimal time to complete all parts (Nowicki and Smutnicki, 1996). The time spent to finalize all parts is known by makespan. In other words, the makespan is the total length of the schedule (when all the jobs have finished processing). Besides the makespan, other objectives can be considered, such as minimize the number of setups, the idle time of machines, the number of tool switches in a machining workstation and so on. The Scheduling Problem is considered hard to solve, with computational complexity defined as NP-Hard (Nowicki and Smutnicki, 1996). It can be applied in a variety of manufacturing systems, being specially studied in Flexible Manufacturing Systems (FMS) (Jha, 1991).

Tratamento de um problema de escalonamento considerando datas de entrega, turnos de produção e trocas de ferramentas via Busca Tabu

In this paper is studied the Job Shop Scheduling Problem applied to a Flexible Manufacturing System (FMS) environment, considering due dates, production turns and tool switching constraints. The FMS is composed by a versatile machine, a system for transport, handling and storage of materials and a computational system. This problem is represented through an objective function f, in which is possible to define three minimization policies: (i) tardiness time, (ii) stop time and (iii) switching tools time. The implementation of these policies is made through the definition of weights to the decision variables of f. The search for the schedule which minimizes is made through the Tabu Search (TS) technique. It was made experiments in which the impact of five initial solution rules is verified in the TS performance. Experiments with the objective of analyze the behavior of the search considering the minimization policies were performed. In these experiments, it was noticed that tardiness minimization conflicts with tool switching and stops minimization. The Increasing of TS parameters nbmax (number of iterations with no improvement in the global result of the search) and Tabu List size increasing the performance of the search in finding good solutions for tardiness minimization.

A neural approach to classification of satellite images

This paper shows the results obtained using a traditional statistical method (Gaussian Maximum Likelihood) and artificial neural networks (backpropagation and cascade-correlation) applied to multispectral Landsat Thematic Mapper data classification. The studied images cover a region situated in Rio Grande do Sul State Brazil.