Improving Memory for Solving Dynamic Distributed Factory Coordination Problem

Many real-world problems involve the coordination of multiple agents in dynamic environments. Machines in a factory may need to coordinate the scheduling and execution of jobs to ensure smooth operation as customer demands shift. In the domain of factory operations, adaptive, self- organizing agent-based approaches have been shown to provide very robust solutions. However, these adaptive approaches may require non-trivial amounts of time to respond to large environmental shifts. Techniques exist that have been shown to help many different approaches perform better when problems are dynamic. One common technique is the use of information from the past to improve current performance. In many dynamic problems, the current state of the environment is often similar to previously seen states. Using information from the past may help to make the system more adaptive to large changes in the environment and to perform better over time. One way to maintain and exploit information from the past is the use of memory, where solutions are stored periodically and can be retrieved and refined when the environment changes. This paper introduces several density-estimation memory systems that are inspired by estimation of distribution for solving one of the hard dynamic problems (Factory coordination). In this proposed method, instead of storing only single points in memory, we propose to store clusters of points in each memory entry and to create a model of the points in each cluster. In this proposed method we will be able to store many more points, the computation overhead required for the memory will remain low. The experimental results show the efficiency of the proposed algorithm in comparison with other methods.