Alternative Heuristics for Solving the Multi-Constrained Order Picking Problem

摘要

Warehouse order picking activities are among the ones that impact the most the bottom lines of warehouses. Many practical constraints arising in real-life have often been neglected in the scientific literature. In this paper, we solve a multi-constrained order picking problem under weight, fragility, and category constraints, motivated by our observation of a real-life application arising in the grocery retail industry. This difficult warehousing problem combines complex picking and routing decisions under the objective of minimizing the distance traveled. We first provide a description of the warehouse design. We then develop five heuristic methods, including extensions of the classical largest gap, mid point, S-shape, and combined heuristics. The fifth one is an implementation of the powerful adaptive large neighborhood search algorithm specifically designed for the problem at hand. The performance of the proposed solution methods is assessed on a newly generated and realistic test bed containing up to 100 pickups and seven aisles. We compare the results provided by the five approximate solution methods. Our in-depth analysis shows which heuristic tends to perform better.