## 1 Introduction  
Throughout the years, considerable attention has been directed toward the management of warehouse operations [1]. The process of locating items within a warehouse or fulfillment center and preparing them for dispatch to customers is commonly referred to as \"warehouse picking\". One of the main research priorities is order picking, which is generally defined as the recovery of goods from storage sites to fulfill customer orders [2]. This is due to two factors: first, the substantial amount of manual work that choosing orders entails [3], and second, the fact that order picking is a very time-intensive task that has a direct influence on customer service [1, 4]. Today's supply networks are under a lot of demand. There are too many elements upsetting the delicate balance in supply systems, including a lack of raw materials, bad weather, natural catastrophes, and the pandemic. This causes supply chains to break down, which can delay or even cause deliveries to be canceled. This has a significant impact on production and logistics. Technology advancement has accelerated innovation in the supply chain to previously unheard-of levels, e.g., blockchain technology, cloud computing, the Internet of Things (IoT), and big data [5, 6]. In the warehouse, sustainability has taken on more importance. When a warehouse can run in a way that is beneficial to both society and the environment, it is sustainable. Ithas systems in place to reduce waste, emissions, energy use, and environmental effects as a whole. Overall, it can be said that the problem of warehouse logistics is difficult, and it is useful to utilize computer simulations, optimization, and mathematical modeling techniques for its efficient solution [7,8]. Mathematical modeling was utilized by [PERSON] al. (2013) [7] to examine the ideal number, size, and placement of public logistics warehouses. Most of the research investigates the order-picking problem in terms of picker-to-parts, in which the picker walks around the logistic center and picks products for the order. However, many logistic centers are functioning with zone picking rules in which the picker collects the products for selected orders only from his zone. Therefore, in this research, we investigate the process by which the container is moved via the conveyor to the consequent picking zones in which the pickers can collect the orders. We present a practical logistic order-picking model with a one-directional conveyor and buffers to which the racks and pickers are assigned. In the investigated distribution center, three versions of the layout are modeled: (1) each picker is allocated to a single buffer; (2) one picker is allocated to more than one buffer; and (3) more than one picker is allocated to one buffer. The criteria is to minimize the total time needed to complete all orders. Therefore, by using a proposed mathematical model, it is possible to instruct the pickers in what sequence to pick products for the containers during the order-completing phase, which results in a reduction of time on later steps such as packing and shipping. The goal of this study is to find new optimization tactics and develop two versions of the two-step matheuristic for the required order-picking problem. The proposed approach includes the decomposition of the main order picking problem into two consequent subproblems at the first step, of which the pickers/buffers free time minimization problem is solved, and at the following stage, order picking makespan minimization is solved. The acquired knowledge demonstrates the application possibilities of the proposed matheuristics in terms of increased scalability, lower operational expenses, and improved employee productivity. The remainder of the paper is organized as follows: The next section, Section 2, presents a brief literature review. After that, a description of the investigated order-picking problem and its mathematical model are given in Section 3. Section 4 presents the new matheuristics. Section 5 reports the computational results.