332 research outputs found
Order picking problems under weight, fragility, and category constraints
Warehouse order picking activities are among the ones that impact the most the bottom lines of warehouses.
They are known to often account for more than half of the total warehousing costs. New practices
and innovations generate new challenges for managers and open new research avenues. Many practical
constraints arising in real-life have often been neglected in the scientific literature. We introduce, model,
and solve a rich 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 full description of the warehouse design which enables us to algebraically
compute the distances between all pairs of products. We then propose two distinct mathematical models
to formulate the problem. We 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. We then implement
a branch-and-cut algorithm and cutting planes to solve the two formulations. 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 bounds provided by the two formulations. Our in-depth
analysis shows which formulation tends to perform better. Extensive computational experiments confirm
the efficiency of the ALNS matheuristic and derive some important insights for managing order picking
in this kind of warehouses
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