The use of algorithms in transport planning software can cut bulk material distribution costs significantly and increase customer service levels at the same time. But not all algorithms are the same, and the level of quality can mean the difference between good or optimised planning performance. A look under the bonnet explains how they work.
The supply of bulk material within a network of multiple plants, depots and customer sites is usually performed by a heterogeneous vehicle fleet. Trucks have different capacities and characteristics, may have a single or multiple compartments and different auxiliary equipment. Customer demands are typically larger than the vehicle capacities, so that most customers are visited in serval times within the planning period (split deliveries). While most transports are full truck loads, small orders for partial loads may lead to multi-drop tours. Often, the haulier payment schemes may mean that some trucks are more attractive on certain routes than others.
The goal is to maximise the service level, measured by the order volume delivered on-time and in full, while the same time minimising transport costs. Given the sheer size of the planning task, the many complicating side constraints, and frequent changes in the input data, building a good plan is not easy – it can even be incredibly complex. If done manually, optimization opportunities are usually not used to their full potential. The input data, however, can be used to optimize and speed-up decision-making processes in transport planning. Buzzwords like big data, business intelligence and analytics promise to channel data stream into knowledge, transparency and insights. However the optimisation of processes often requires more than just channeling a flood of data. For those who work under pressure, quick action is needed. There is no time to analyse amounts of data. Quick and wise decisions are required.
The power and quality of transport planning tools
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