64 | Sector Focus: Handling & Storage
SUMMARY
■Mixed-integer linear programming faces inherent limitations
■AI optimisation can handle real- time changes effectively
■It has the potential to automate back-office operations entirely
■AI has a key role to play in strategic decisions, such as the location of facilities, fleet size and composition
DRIVING SEAT AI IN THE Opturion, a leading supply chain optimisation company, highlights how AI is revolutionising the transport and logistics industry
The logistics industry, encompassing transportation, warehousing, and distribution, is undergoing a significant transformation driven mainly by artificial intelligence (AI). Transport and logistics have traditionally adopted planning methods like load optimisation, route planning, scheduling and warehouse operations, often achieved through mixed-integer linear programming (MILP) techniques. However, these methods, though well-proven, are limited by their inability to handle the real-world complex, non-linear nature of transport and logistics. Enter AI-driven optimisation, which has begun to revolutionise the industry by offering dynamic, real-time solutions that can adapt to
changing conditions, optimise resources more effectively, and ultimately enhance efficiency, compliance, and customer satisfaction.
TRADITIONAL METHODS: LIMITATIONS AND CHALLENGES
In the transport and logistics sector, traditional optimisation methods, including day-ahead or week-ahead planning for transport operations, have relied heavily on mathematical methods like linear programming. These models, such as MILP, are effective for load planning, routing and scheduling tasks. In warehouse operations, a typical application is slotting. This process involves positioning stock-keeping units
(SKUs) in locations that minimise pick-path distances.
Despite the advantages, MILP faces several inherent limitations. The primary challenge is that the real world is not linear – routes, customer demands, and regulations create complex relationships. Moreover, MILP struggles with highly complex regulations and non-linear constraints. As a result, solving these models can take an unacceptably long time, and the outcomes may not always reflect the dynamic and multi-faceted nature of real-world logistics challenges. For example, consider a company that needs to dispatch vehicles daily based on fluctuating customer orders. Traditional MILP methods can optimise this dispatch beforehand but fall short when unexpected delays or order changes arise during the day. Additionally, while MILP models work relatively well in tactical planning scenarios where a perfect plan isn’t essential, they fall short in real-time applications. This situation creates a major problem: plans generated by MILP often require further interpretation and manual adjustment by human planners, adding complexity rather than simplifying the process. Thus, the adoption of these systems has remained patchy, with many companies wary of the increased workload without a clear improvement in operational efficiency.
EMERGENCE OF AI-DRIVEN OPTIMISATION
Above: Warehouse layout can be optimised with AI TTJ | November/December 2024 |
www.ttjonline.com
AI-driven optimisation presents a fundamentally different approach. Companies like Opturion have pioneered AI technologies tailored to logistics, focusing on compliance,
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89
