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The aim of the present paper to select, through a two-class Bayesian classifier, the best priority rule to solve a Job Shop: Open Shop scheduling problem. In a first phase, the design of the classifier, trained with 300 randomly problems, is exposed. In 150 of them, the best priority rule for sequencing was FIFO (First In First Out) while in the rest was the LPT (Long Process Time). In a second phase, a set of 300 different problems, with the same characteristics of the first phase, were randomly generated. These problems were classified previously (without sequencing them) through the proposed Bayesian technique. The results show that, in 96% of cases, the proposed classifier identifies the best priority rule to sequence the orders. [ABSTRACT FROM AUTHOR]

Comparison of some traditional techniques for the solution of production scheduling problems in Job Shop- Open Shop systems (FIFO, LIFO, SPT, LPT) with those of an evolutionary algorithm of the area of artificial intelligence is done. The results of an exhaustive analysis of 12600 production scheduling problems in Job Shop-Open Shop systems are presented. Although in most of the experiments the evolutionary algorithm showed a better performance in terms of the makespan, in some particular cases the use of traditional techniques were equally efficient. Despite the use of advanced techniques often provides better results for most production scheduling problem, this is not always true. [ABSTRACT FROM AUTHOR]

The objective of the present work is to diminish the total process time (Makespan) and to increase the machine process time, by diminishing the idle time in a job-shop environment. Through the design of a hyperheuristic based on an ant colony and genetic algorithms. This work is developed in two phases: in the first phase, a hyperheuristic identification and definition is carried out for sequencing processes in job shop environments. In the second phase, the system effectiveness in the traditional production programming is shown. In the investigation project, an enterprise from the metal mechanic sector was chosen, where by means of a combination of an ant colony and genetic algorithms, the optimal route for an order is scheduled, achieving the optimization or suboptimization of its respective total process time in an upper percentage of 95%. [ABSTRACT FROM AUTHOR]

Synchronization between production scheduling and real demand is an issue of great concern for production practitioners and academics. Put in a nutshell, the solution requires the placement of a decoupling point in order to get a trade-off between efficiency and flexibility. In the context of a postponement production strategy, a decoupling point is an inventory buffer to create independence between the process and final demand. Upstream of the decoupling point, the process is managed under a make-to-stock approach; in contrast, downstream operations meet the final demand under a make-to-order approach. In this sense, the present paper proposes a multicriteria methodology to locate decoupling points in manufacturing systems. The methodology consists of two stages, with two and three steps respectively. In the first stage, the decision criteria and alternative for decoupling points are chosen. In the second one, the final decoupling point placement is determined. By applying the methodology in a metalworking company, the location of the decoupling points for nine production lines was obtained. [ABSTRACT FROM AUTHOR]

A methodology based on neural networks is proposed for solving the job shop-open shop scheduling problem. The job shop scheduling problem consist of defining the best order sequence to minimize the total processing time (makespan) or other relevant variables. Several intelligent techniques have been applied to solve this kind of problem. However, when reprogramming is required these techniques present practical difficulties since they require to be restructured to find a new solution. The proposed methodology trains the network by combining the processing times at each node, through transfer functions that are multiplied by weights obtained from the algorithm of network programming. The proposed neural network obtains solutions not only for a particular problem but also for new situations without requiring a problem reconfiguration. When compared with other techniques, the obtained solution showed a superior performance ranging from 30% to 164% in terms of the makespan. [ABSTRACT FROM AUTHOR]

The development of a methodology that contains a metaheuristic model based on the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) in a job shop production environment. This is to minimize three main process variables: total time of processing (makespan time), energy cost and labor accidents. With the application of this methodology, the variables under study were optimized in 42%, compared with traditional production programming techniques. Based on the application of the proposed methodology, it is suggested to explore other multi objective functions in which the consumption of other resources, such as water and fuel can be analyzed in undesirable situations such as transport strike, landslides and traffic congestion. [ABSTRACT FROM AUTHOR]