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Posts tagged distribution
How Deliveroo’s ‘dark kitchens’ are catering from car parks
A tatty car park under a railway line is squeezed between a busy road, an industrial site and a semi-derelict pub covered in graffiti. It’s one of the grittiest parts of east London and probably the last place you would imagine some of the trendiest eateries in the country to be preparing meals. But the grimy spot is just a short moped ride from the gleaming office towers of Canary Wharf and upmarket docklands apartments, and is therefore the perfect location for the latest idea from Deliveroo, the food courier service. It is setting up dozens of “dark kitchens” in prefabricated structures for restaurants that want to expand their businesses without opening expensive high street premises. Ten metal boxes of a similar size to a shipping container are on this site in Blackwall. They are fitted with industrial kitchen equipment, and two or three chefs and kitchen porters are at work in each, preparing food for restaurants including the Thai chain Busaba Eathai, the US-style MeatLiquor diners, the Franco Manca pizza parlours and Motu, an Indian food specialist set up by the family behind Mayfair’s Michelin-starred Gymkhana. The boxes have no windows and many of the chefs work with the doors open, through which they can be seen stirring huge pans or flipping burgers. Outside there are piles of spare equipment, mops in buckets, gas cylinders for the stoves and large cans of cooking oil.
Aequa Europa
Aequa Europa
A Scalable Heuristic for Viral Marketing Under the Tipping Model
In a “tipping” model, each node in a social network, representing an individual, adopts a property or behavior if a certain number of his incoming neighbors currently exhibit the same. In viral marketing, a key problem is to select an initial “seed” set from the network such that the entire network adopts any behavior given to the seed. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds seed sets that are several orders of magnitude smaller than the population size and outperform nodal centrality measures in most cases.