Abstract

Nowadays, production scheduling is a greatly debated field of operation research due its potential benefits for improving manufacturing performance. Production scheduling, however, despite the increasing use of APS (Advanced Planning and scheduling Systems) and MES (Manufacturing Enterprise Systems) is still underestimated and one frequently encounters more or less intuitive scheduling using excel spread sheets at workshop level, mainly in SME (Small and Medium Enterprises). Some of the main reasons for this are the complexity of related algorithms and the timespan of the optimization manufacturing operation sequence. The complexity of the algorithms usually leads to a number of operators which are difficult to set up for a usual workshop foreman or manufacturing planner. That is why dispatching rules are widely used in comparison with advanced heuristics, such as Evolution Algorithms (EA). Therefore, operation research should not focus only on getting the best values of the objective function by problem based operators, but also on industrial practice requirements such as operator simplicity and a low timespan of the optimization. This article briefly introduces key principles of the scheduling system developed for the Job Shop Scheduling Problem (JSSP) type of manufacturing. An implemented EA with random key representation, clone and incest control and chromosome repair algorithm is briefly explained. Further, the test results of the evolution operator (e.g. crossover and selection) are presented with respect to the value of the objective function and timespan of the optimization. The research goal is to develop a principle of automatic optimization using EA, where the single parameter to set is required optimization timespan.

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