The scheduling problems in factory domain applications usually involve many parallel machines, with each machine capable of processing several tasks. In most cases, changing the current machine state to another state to process a different task incurs additional material costs and time. If the overall system can maintain the expected performance, minimizing these state changes is very beneficial, and agent-based approaches inspired by the task allocation strategies of several social insects have gained increasing attention as solutions. The basic concept is based on the stimulus-threshold relation, and an individual agent determines whether it performs a given task or not based on two sets of terms, the environmental external stimuli for the task and the internal threshold values of all possible tasks. In this approach, selecting appropriate threshold values is directly related to the overall system performance, and we present a pheromone-based approach to obtain appropriate threshold values. Each agent maintains a limited, constant-sized task history queue of recently processed tasks, and the information of each agent is individually used to calculate the threshold values of tasks. Based on various experimental results, we show that the performance of the proposed method is comparable to those of other conventional methods.
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