'commodity flow' is a valid and usable phrase in written English.
It is used to refer to the movement of goods from one location to another, usually from a seller to a buyer. For example, you could say "The commodity flow between China and the United States is significant."
Then, we aim to find an optimal commodity flow and well-designed network structure through the proposed multi-commodity flow model and simultaneously achieve the equitable distribution of all passengers and the optimal train skip-stopping pattern.
Optimal performance of the network under normal and extreme conditions may be found by minimising the cost of commodity flow.
We model the problem with a single commodity flow formulation and design a branch-and-cut approach to solve it.
The formulation uses single commodity flow constraints to model product sequencing and explores the product families' hierarchy to reduce the combinatorial nature of the problem.
Several models not only include a functional sequence, but also apply a kind of lateral axis as well representing data or commodity flow.
The model is calibrated taking into account the specific trade cost structure of each commodity flow.
The present study, using the 1993 US Commodity Flow Survey Data, models inter-regional commodity flows for 48 continental states of the US with three different artificial neural networks (ANN).
Graph partitioning using single commodity flows.
We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits.
Mechanical equipment is applied to start, stop, heat, and separate the commodity flowing through the piping system.
It is concluded that ANN architecture is a very promising technique for predicting short-term inter-regional commodity flows.
