Authors - Sreebala V S, Arun Kumar V N, Agna.S. Nath Abstract - The Commercial Territory Design Problem (CTDP) plays an important role in sales and marketing management. The problem focuses on partitioning some basic units into territories to optimize compactness while ensuring workload balance and connectivity constraints. Due to the NP-hard property of the problem, exact approaches often have limitations in scalability across large datasets. This study proposes a combination of the classical ALNS algorithm framework and an ActorCritic Deep Reinforcement Learning architecture to deal with the large CTDP instances. Our proposed algorithm can automatically select destroy and repair operators, and dynamically fine-tune hyperparameters such as destruction level and acceptance criteria based on the actual state of the search process. Experimental results on benchmark instances with various sizes show that our algorithm not only achieves superior quality solutions compared to traditional ALNS but also surpasses exact solutions in terms of convergence speed within the same runtime limit. It can achieve high-quality solutions within a reasonable execution time and has the potential for real-world applications.
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