## 2 Literature Review  
The product allocation problem has been analyzed in many research papers in the research community. The interesting solution was proposed by [PERSON] et al. [17]. The authors present a new approach to the evaluation of the supplier of products and the allocating of the optimal order quantity from each supplier concerning the ecology and resilience of the transport chain. The obtained weights from the analytical hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) were integrated. This integration enables the development of a multi-objective model used as an order allocation planner. The \"\(\epsilon\)-constraint\" model was used to solve the multi-objective optimization problem [18]. The \"\(\epsilon\)-constraint\" algorithm aims to optimize one objective function while considering all other objective functions as constraints. For solving similar problems, the CPLEX software can be used. [PERSON] et al. [19] focus on the research on the problem of allocation to the proper area by the criterion of time horizon in a food company. The algorithm identifies the operation area and the corresponding spaces that are required for the allocation of products [19]. [PERSON] et al. [20] propose a novel shipping mechanism \"Predictive Shipping\", which utilizes demand prediction to arrange a ship before orders are placed. They have formulated and solved a large-scale products allocation problem across warehouses [20]. [PERSON] et al. [21] present a mathematical model for solving the product allocation problem based on the genetic algorithm (GA) [21]. The authors calculate the distances for product order-picking to minimize the routing distances. In 2013, a research team from Italy [22] proposed a mathematical model for the multi-level product allocation problem with compatibility constraints. The goal of this research was to reduce, as much as possible, the delivery times, the inventories and the total logistic costs, and to guarantee, at the same time, higher service levels. Researchers used the mathematical modeling sensitivity analysis, then iterated local search-based heuristic was used to reduce the computing time. Two years later, a similar team compared the heuristics for solving the product allocation problem in the multi-level warehouse. This authors present a rollout-based heuristic whose performances are evaluated on the basis of a detailed computational phase, including also a real case study and comparing it with iterated local search-based heuristic and cluster-based heuristic [23]. Many research studies focus on the allocation of a product in the supply chain network. For example, [PERSON] et al. [24] use a GA for distribution centers, considering processing cost, demand, capacity and related constraints [24]. In 2003, [PERSON] et al. [25] proposed using the genetic algorithm to allocate a set of customers to multiple warehouses. The proposed solution procedure utilizes a genetic algorithm that is designed to find Pareto optimal solutions for this problem in a short period. [PERSON] et al. [26] proposed the method for the optimization of product category allocation in multiple warehouses to minimize the splitting of online supermarket customer orders. Multi-item customer orders often need to be split into multiple shipments because the ordered items may be stored in different warehouses. The method developed by the authors used a heuristic clustering algorithm to optimize the product category allocation among multiple warehouses based on the distribution of multi-item orders to minimize the total number of order splits. According to a literature review, there is a lack of models to support the product classification and allocation problem in modern warehouses. The available literature and market solutions allow for simulating the order-picking process and efficiency analysis. Neither of them, however, allow the easy planning of product allocation in warehouses.