# Multi-Criteria Decision-Making Techniques for Optimal In-House Storage Location Selection
## 2 Literature Review  
Various issues regarding the warehouse management system should be identified in the literature. Also, several exciting studies deal with the solution of effective warehouse systems. For example, [PERSON] et al. [2] examined the storage and retrieval policy for perishable food products in a cold warehouse in Izmir. The study aimed to identify an optimal storage and retrieval strategy to improve performance metrics, reduce processing times, and minimize food waste. The authors developed storage and retrieval algorithms and evaluated them using simulations. They also established warm-up periods and confidence intervals for the average cycle time using the Output Analyzer tool of the ARENA 16.0 commercial simulation software. Additionally, a sensitivity analysis was conducted to investigate the relationship between product arrivals and demand distribution of product types. Finally, they developed a user-friendly decision support system embedded the proposed simulation models. The study found that their proposed storage and retrieval policy improved performance metrics, reduced process time, and decreased food waste. The research provides valuable insights for warehouse managers and practitioners looking to optimize storage and retrieval operations for perishable food products in a cold warehouse. [PERSON] and [PERSON] [3] published an article that investigates the problem of storage location assignment at a warehouse as an optimization problem. The authors found that traditional optimization algorithms do not readily apply to the unique situation of each warehouse. Thus, a specifically designed optimization algorithm is necessary for the location assignment problem at Lazada Thailand Warehouse. To address this, the authors propose Multi-objective Optimization using the Flower Pollination Algorithm (MOFPA) for Storage Location Assignment at Lazada Thailand Warehouse, which includes new operators and multi-objective fitness functions to handle more complex constraints. The experimental results on four real datasets of Lazada Thailand Warehouse indicate that MOFPA can find solutions for almost all datasets, and that it outperforms traditional generic algorithms for all datasets. [PERSON] et al. [4] published a paper that addresses a real-world issue in a food company where the available warehouse space for finished goods constrains production planning. They proposed a novel integrated strategy that combines production planning with a randomized storage assignment policy, taking advantage of the greater visibility and traceability of items provided by IoT-enabled tracking systems to improve space utilization. They formulated the strategy as an integer linear programming model to minimize production and warehouse operations costs. They proposed a heuristic algorithm for a near-optimal solution for large-scale real-world problems. Based on numerical experiments, the results showed that the integrated strategy with a randomized storage policy could significantly reduce the total cost (up to 16.84% with an average of 9.95%) and increase space utilization (up to 26.1% with an average of 14.8%), compared to the strategy with a dedicated policy. The results demonstrate the cost-effectiveness of implementing new technologies, such as IoT-enabled tracking systems, in warehouse management. [PERSON] and [PERSON] [5] described a method for developing layouts for unit-load warehouses that use multiple slot heights to enhance warehouse area utilization. The task was divided into two parts: putting slots into rack bays and arranging rack bays into a pattern. Depending on whether the warehouse has directed picking and put-away, the authors presented two strategies for the first subproblem. They described a simulation model and a greedy approach based on the duration-of-stay storage strategy for the second. They discovered considerable advantages to adopting varied slot heights in unit-load warehouses regarding footprint, estimated trip time, and packing cost.