Job shop scheduling problem with heuristic genetic programming operators

This paper introduces an optimization algorithm for job shop scheduling problem in logistic warehouses. The algorithm is based on genetic programming and uses parallel processing. For better performance a new optimization method called “priority rules” was proposed. We found out that the three proposed priority rules help algorithm to prevent stuck in the local optima and get better results from genetic programming optimization.

Algorithm was tested with batch of tests based on data from real warehouse and with synthetic tests generated randomly (inspired by the real world scenarios). The results indicate interesting reduction of time that is necessary to fulfill all tasks in warehouses, reduction in number of collisions and better optimization performance.

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