## 2 Literature Review
[9] demonstrated a discrete space allocation for forward reserve problems and analyzed the difference between the discrete and the fluid models. They developed three models which serve different purposes. The first model solves the problem of dividing the forward area for a given set of items, which is defined in the paper as a discrete forward-reserve allocation problem (DFRAP). The second model unifies the allocation and assignment problem, and the model's outcome shows which items must be held in the forward area and the size that belongs to the particular items. The final model has an approach to the problem that considers the forward area size as a variable and how the products must be allocated. The results showed that the gap between the discrete and the fluid models is small and can be ignored. The case study presented by [10] has been applied to a cosmetic firm's warehouse where they use a wave picking strategy. The objective is to avoid stockouts in an environment where picking and replenishment operations are performed simultaneously. They proposed three prioritizing policies for replenishments and simulated to compare the computational results and stockouts. Ref. [11] proposed an alternative storage allocation policy to reduce the picking time for the picking area of a warehouse. The proposed policy assumes several empty locations in the picking area that can be used for items whose requested quantities are higher than those in their stock. The study compared the proposed policy with the conventional warehouse layout by simulation and analyzed its performance regarding operator travel and order fulfillment time. The study also considered the impact of congestion on the proposed policy. The paper provides insights to improve the performance of the warehouse order-picking system. Ref. [12] identified the best storage strategies for an automated storage and retrieval system. The research used analytical and numerical methods to compare the performance of different storage strategies. The paper also presents the factors affecting the performance of storage strategies and the experimental results used to determine the best strategies. Ref. [13] aimed to design efficient work schedules for human pickers in mobile rack warehouses using human-robot co-coordinated order picking systems. The article discusses the challenges of designing good work schedules for human pickers in such systems. It proposes a model that allows mobile racks with different workloads to be assigned to pickers and schedules the racks assigned to each picker to minimize the expected total picking time. The paper uses a stochastic dynamic programming model and an approximate dynamic programming-based branch-and-price solution approach to solve the problem. The results show that the proposed approach can solve a moderate-sized problem of 50 racks in less than 2 min and produces high-quality solutions with picking times that are 10% shorter than those that do not account for schedule-induced fluctuations in the pickers' work states. The article concludes that the proposed approach can be applied to other warehouses to detect fluctuations in picker work states, incorporate these fluctuations into robot schedules, and increase the productivity of a picking system. Ref. [14] considered the storage replenishment and routing problem (SRRP) in a warehouse environment where items are stored in separate forward and reserve storage areas and replenished using a common reserve storage area. The authors propose an MIP model and heuristic approaches to solve the problem. Based on the inventory routing problem (IRP) literature, the authors propose an MIP model to solve SRRP. They also propose a priori routing heuristics to solve the problem. These heuristics are based on the shortest path algorithm and graph theory-based heuristics. To further improve the replenishment routes, they propose an a posteriori routing step. Ref. [15] examined the ADP method for solving multidimensional bag problems.