Optimization algorithms for improving the efficiency of order picking processes in warehouses

Customers nowadays expect to receive their orders within a specified time window. The process of reaching customers on time is especially challenging for businesses to handle. Because effective management of this process demands the integration of in-warehouse and out-of-warehouse operations. The relevant process begins as soon as the customer order arrives at the company, and the "Order Picking/Batching" process inside the warehouse follows the "Vehicle Routing" process outside the warehouse. These processes become more crucial for companies, especially following the living conditions demanded by causes such as the pandemic, and are a very common problem in real life. The literature refers to this integrated process as the "Order Batching and Vehicle Routing Problem with Time Window." By reviewing the available literature for the related problem, a new mixed integer linear mathematical model was built in light of limitations and assumptions in this study. Small, medium, and large data sets were constructed for the developed issue, and a solution was attempted using the CPLEX solver. However, only a portion of a small data set could be solved with this solver, and no solution could be found for medium or large data sets. As a result, the Genetic Algorithm, a metaheuristic search algorithm, is suggested, and the related problem is solved using various picker routing heuristics for small, medium, and large data samples, with good outcomes. The results were successful, and the best acceptable picker routing heuristic was proposed, based on real and hypothetical data tested on the proposed mathematical model.