In the Artificial Bee Colony (ABC) algorithm, the employed bee and the onlooker bee phase involve updating the candidate solutions by changing a value in one dimension, dubbed one-dimension update process. For some problems which the number of dimensions is very high, the one-dimension update process can cause the solution quality and convergence speed drop. This paper proposes a new algorithm, using reinforcement learning for solution updating in ABC algorithm, called R-ABC. After updating a solution by an employed bee, the new solution results in positive or negative reinforcement applied to the solution dimensions in the onlooker bee phase. Positive reinforcement is given when the candidate solution from the employed bee phase provides a better fitness value. The more often a dimension provides a better fitness value when changed, the higher the value of update becomes in the onlooker bee phase. Conversely, negative reinforcement is given when the candidate solution does not provide a better fitness value. The performance of the proposed algorithm is assessed on eight basic numerical benchmark functions in four categories with 100, 500, 700, and 900 dimensions, seven CEC2005’s shifted functions with 100, 500, 700, and 900 dimensions, and six CEC2014’s hybrid functions with 100 dimensions. The results show that the proposed algorithm provides solutions which are significantly better than all other algorithms for all tested dimensions on basic benchmark functions. The number of solutions provided by the R-ABC algorithm which are significantly better than those of other algorithms increases when the number of dimensions increases on the CEC2005’s shifted functions. The R-ABC algorithm is at least comparable to the state-of-the-art ABC variants on the CEC2014’s hybrid functions.

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In paper production, a jumbo reel is cut into multiple intermediate rolls, and each intermediate roll is then sheeted as finished goods. This problem is called a cutting stock problem and is proven to be NPhard. The objective is to minimize material waste or trim loss from all the cuttings. In the case that any intermediate roll is not entirely used for its associated order, the intermediate roll itself will turn to be a dead stock. We use the concept of universal sizes of intermediate rolls to eliminate the dead stock. A pre-defined number of universal sizes of intermediate rolls is to be used to serve all the orders. The problem is solved using Reinforcement Artificial Bee Colony algorithm with Integer Linear Programming subroutine. This proposed approach is then tested with a set of 1,055 orders and 127 different sizes of sheet papers from a paper manufacturer. The results reveal that our method outperforms other competitors. Our method offers the total trim loss of 3.51%, compared to the trim loss reported by the industry of at least 5%. This approach not only reduces the number of partially cut rolls, but also decreases the number of the jumbo reels needed to serve all the orders. Therefore, both the inventory cost and material cost can be saved.

Determining the optimal amount of cash stock reserved in each bank branch is a strategic decision. A certain level of cash stock must be kept and ready for cash withdrawal needs at a branch. However, holding too much cash not only forfeits opportunities to make profit from the exceeding amount of cash in the stock but also increases insurance cost. This paper presents cash stock strategies for bank branches by using deep learning. Deep learning models were applied to historical data collected by a retail bank to predict the cash withdrawals and deposits. Data preparation and feature selection to identify important attributes from the bank branch data were performed. In the prediction process, two Recurrent Neural Network techniques—Long Short-Term Memory and Gated Recurrent Units methods—were compared. Then prediction errors were measured and statistically tested for their probability distributions. These distributions together with the predicted values were used in determining the lower and upper bounds for holding the cash stock. These bounds were employed to recommend the cash stock level strategies by having two options for different situations. The impacts of COVID-19 were also tested and discussed. According to the bank under this study, the proposed strategies can reduce the amount of cash stock by more than 10% for which was their initial target. Hence, the costs of cash management such as insurance cost and cash transportation cost were reduced. Moreover, the excess cash could be used for other purposes of the bank.