Logistics makes extensive use of human and material resources to achieve a
target level of customer service at the lowest possible cost. It has been recognized
as a major key to success in commerce and industry, and continues to
evolve radically and grow in importance in recent years. Warehousing, as one
of the most costly elements of logistics, is often the central operation in most
logistics networks. Its successful management is critical in terms of both cost
and service. In this thesis, two problem areas in warehouse logistics are studied:
stock loading and order picking.
Stock loading is an essential operation in modern logistics. Improvement on
container capacity utilization and loading efficiency significantly reduces costs.
For a given set of boxes in different sizes and an unlimited number of identical
containers, the basic cargo loading problem is to determine the minimum
number of containers required. The problem is proven NP-hard. To tackle this
problem, a Tabu search optimization with a tree-based cargo loading algorithm
as its inner heuristic is proposed. This approach has flexibility in taking different
box conditions into consideration, and can find better solutions on average
than other recent meta- or heuristic algorithms.
Decreasing order sizes and increasing fuel costs provide a strong incentive for
the inner-city truck loading operation to utilize container space more efficiently
in transporting goods to multiple clients during one trip. This considers not
only traditional loading constraints, but also multi-drop requirements. A wallbuilding
heuristics based on a binary tree data structure is proposed to handle
these side constraints. A dynamic space decomposition approach, together with
a repacking and space amalgamation strategy, permits an efficient and effective
loading plan.
Order picking, one of the most critical warehousing operations, is the second
problem studied in this thesis. An analytical approximation model is proposed
based on probability modeling and queueing network theory applied to a synchronized
zone picker-to-part order picking system with different routing and
ABC-class inventory storage policies. The numerical results are compared and
validated via simulation. The resulting model can therefore be usefully applied
in the design and selection process of order picking systems.
The routing versus storage issues are further investigated with a simulation
model. This extends the existing research by evaluating multiple routing and
storage policies under varying operating conditions. Results show that the midpoint,
return and traversal routing policies generally perform best when paired
with perimeter, across-aisle and within-aisle storage strategies, respectively. Yet
performance is indeed dependent on demand patterns, zone sizes, batch sizes
and order sizes.
At first glance, order picking and stock loading operation seem to pursue
different objectives. However, they are two related operations conducted sequentially
from internal to the outbound side of warehousing. An efficient
order picking system is a precondition for an effective loading operation at the
shipping dock, especially when multiple orders need to be selected for consolidation
in shipment. The proposed loading algorithms and the order picking
system performance evaluation models can be used to further study the effective
integration of these two functions. / published_or_final_version / Mathematics / Doctoral / Doctor of Philosophy
| Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/182281 |
| Date | January 2012 |
| Creators | Pan, Li, 潘莉 |
| Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
| Source Sets | Hong Kong University Theses |
| Language | English |
| Detected Language | English |
| Type | PG_Thesis |
| Source | http://hub.hku.hk/bib/B4784940X |
| Rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License |
| Relation | HKU Theses Online (HKUTO) |
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