Solving Location Assignment and Order Picker-Routing Problems in Warehouse Management

Abstract

One of the critical warehousing processes is the order-picking process. This activity consists of retrieving items from their storage locations to fulfill the demand specified in the pick lists. Therefore, the storage location assignment affects the picking time and, consequently, reduces the operating costs of the warehouse. This work presents two alternative mixed-integer linear models and an adaptive multi-start heuristic (AMH) for solving the integrated storage location and picker-routing problem. The problem considers a warehouse with a general layout and precedence constraints for picking according to the products weight. Experimental work confirms the efficiency of the proposed reformulations since we found out a total of 334 tested instances and optimal solutions for 51 new cases and 62 new feasible solutions. The proposed AMH improved more than 29% of the best-known solutions and required an average execution time of 117 s. Consequently, our proposed algorithm is an attractive decision-making tool to achieve efficiency when solving practical situations in a warehouse.

A New Method for the Determination of Fire Risk Zones in High-Bay Warehouses

“…To determine the most precise parameters related to the locations of transport units, flexibility in terms of the configuration of the layout within the facility itself, and using the approach as in papers [29,30], a procedure was developed for the formation of a threedimensional model of the warehouse with associated elements. The proposed structure of the procedure for determining the parameters of the warehouse, as shown in Figure 2, includes three main phases for the calculation and determination of the necessary parameters related to the storage of materials inside the warehouse.…”

Optimizing Travel Distance in a Synchronized-Zone Order Picking System: A Mathematical Programming Approach

“…The objective function in equation [4] aims to maximize the sum of item correlation score (𝐶 ) for each zone (𝑘) , while considering deviation penalty terms (𝑋 ) and (𝑌 ) , calculated using equations [10] and [11] respectively. For every zone (k), the deviations (𝑅 ) and (𝐹 ) in the penalty terms are calculated by equations [12] and [15] for the achieved item correlation score (𝐶 ) and the picking frequency (𝑃 ).…”

Optimizing Travel Distance in a Synchronized-Zone Order Picking System: A Mathematical Programming Approach

“…𝑇 𝑖𝑠 𝑑𝑒𝑓𝑖𝑛𝑒𝑑 𝑎𝑠 𝑡ℎ𝑒 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝑓𝑜𝑟 𝑖𝑡𝑒𝑚 𝑎 𝑎𝑛𝑑 𝑏. The objective function in equation [4] aims to maximize the sum of item correlation score (𝐶 ) for each zone (𝑘) , while considering deviation penalty terms (𝑋 ) and (𝑌 ) , calculated using equations [10] and [11] respectively. For every zone (k), the deviations (𝑅 ) and (𝐹 ) in the penalty terms are calculated by equations [12] and [15] for the achieved item correlation score (𝐶 ) and the picking frequency (𝑃 ).…”