## 2 Literature Review  
Various researchers have conducted notable studies for warehouse and inventory management. For example, [PERSON] and [PERSON] [10] developed a simulation-based decision-making tool to evaluate the warehouse model which enables the decision-makers and logistics team to select sufficient labor requirements and use the warehouse space effectively. The data considered in the model creation included the warehouse dimensions, rack location, and rack dimensions. Their model was rendered in Arena software using volume-based storage. In another study, [PERSON] and [PERSON] [16] developed an LP model for inventory management and cost analysis. Furthermore, [PERSON] et al. [17] discussed the multiple objective optimizations of the warehouse, using the genetic algorithm to find the Pareto optimal solution. The authors developed the mathematical model based on objective functions such as minimizing travelling time, minimizing cost, and maximizing the quality of the warehouse. To optimize the space utilization of a storage rack in the garment industry in India, [PERSON] et al. [11] presented an LP model that uses box dimensions, product quantity, and production values. The authors optimized the number of racks in use in warehouse storage, and, using the ABC analysis, the optimized solution allocates the materials in sequential order. [PERSON] et al. [18] developed a novel stochastic MCDA model for emergency warehouse locations in the case of a natural disaster in China. This study accounted for the traffic conditions, stock holding capacity, surrounding environment for reserving relief supply, distance to the disaster-prone area, and cost criteria for the analysis. The authors used the ELimination and ChoiceExpressingReality (ELECTRE)-II methods for the selection of the emergency warehouse location. [PERSON] et al. [12] developed a decision-making framework for warehouse performance measurement to improve the efficiency of logistics systems. The authors suggested key performance indicators (KPIs), and the most significant KPI was determined using AHP. Based on the consistency ratio and KPI weights, the warehouse performance was evaluated and analyzed. The SNORM De Boer normalization process method was employed to obtain the final KPI scores and improve the warehouse efficiency. Similarly, [PERSON] and [PERSON] [9] conducted the optimization of warehouse management within an assembly and distribution company. The authors considered the cost of realization, cost of labelling per piece, handling in case of damage, and other information contained in the analysis and used the traditional weighted sum approach (WSA) to determine the weights of the criteria. Then, they used the TOPSIS method to rank the two alternatives (barcodes and RFID). [PERSON] et al. [13] developed a decision-making model for the warehouse location selection depending on unit price, stockholding capacity, average distance to the factory, flexibility, and layout. The weights of the criteria were determined using AHP, while the warehouses were ranked using the TOPSIS method. In another study, [PERSON] et al. [8] discussed storage assignment strategies and route planning of automated guided vehicle systems in warehouse operation optimization. The authors implemented a simulation-based method to determine the storage location and optimal route for vehicles to travel in a traditional rectangular two-block layout warehouse. [PERSON] et al. [14] utilized genetic algorithms for the optimization of discrete product placement and order-picking routes in a warehouse. In this model, the cost of the given product placement is expressed as the sum of the lengths of the optimized order-picking routes. Finally, [PERSON] et al. [4] proposed a particle swarm optimization-based grey model to predict the performance of a readymade garment warehouse. The authors considered KPIs related to cost, time, productivity, and quality, and predicted warehouse performance using grey-based models.