# Multi-Criteria Decision-Making Techniques for Optimal In-House Storage Location Selection
## 2 Literature Review
For a typical warehouse, they expected space savings between 25% and 35%, depending on the number of slot types, and savings of between 15% and 25% in annual operating costs. [PERSON] et al. [6] proposed a warehouse layout design based on a mix of systematic layout planning (SLP) and an ant colony genetic algorithm fusion scheme (GA-ACO). They noted that the modern logistics process necessitates a warehouse performing numerous activities such as storage, processing, and order administration, increasing the number of functional regions within the warehouse. The traditional SLP approach did not yield an optimal layout plan. As a result, they recommended combining SLP with the GA-ACO algorithm. Because the genetic algorithm uses local optimization, the produced layout is not the best solution. The proposed solution improves the layout plan's overall relationship. The experimental results showed the layout plan obtained by the method proposed in this paper had a higher comprehensive relationship. [PERSON] [7] proposed optimizing an automated product dynamic allocation and warehousing model. He emphasized the growing significance of automated cargo warehousing in advancing modern logistics, which is required for the autonomous distribution of goods. He presented an autonomous site allocation model and the particle swarm optimization (PSO) algorithm utilized to optimize the model. He proposed the notion of genetic operator and multi-group co-evolution to improve the algorithm further. MATLAB software was used to do simulation analysis of standard PSO and enhanced PSO. The results showed that the modified PSO method iterated fewer times and produced better solution sets; when compared to the manual allocation scheme, the enhanced PSO computation reduced warehousing time, decreased center of gravity height, and increased shelf stability. The solution and design of optimal in-house storage location is one of the most important decision issues for logistics experts. A suitable solution is presented by applying the decision-making method, especially multi-criteria decision-making methods, which can solve adequate storage and warehouse management issues. [PERSON] [8] mentioned that multi-criteria decision-making is a significant branch of decision-making theory. It is possible to use this approach to solve different issues, but several studies have dealt with MCDM in warehouse management. For example, [PERSON] et al. [9] used a decision-making system to solve container storage management in a seaport. The authors developed a software tool for identifying the best location for a container integrated into a decision model. The connection of comparative analysis of multi-criteria decision-making methodology and their implementation in the warehouse location selection problem was presented by the authors [PERSON] et al. [10]. [PERSON] [11] presented an approach to the solution of the selection of distribution center location with the help of a fuzzy multiple criteria group decision-making. The authors [PERSON] and [PERSON] [12] presented an analytic hierarchy process application for analyzing customer-specific requirements for logistics service and evaluating alternative warehouse operators. The authors provided a systematic framework for the selection of a warehouse network. [PERSON] and [PERSON] [13] used a multi-criteria approach for product classification and storage location assignment. The authors proposed a multi-criteria decision model to perform the product classification and solve the storage location assignment problem in a multi-layer warehouse. [PERSON] et al. [14] emphasized in their research that the storage of products has a strategic role in the supply chain. Also, an efficient production organization increases the effectiveness of the warehouse and the entire warehouse management. It proposes a multicriteria decision support model for ranking products and assigning them to warehouse storage locations.