A case study from MemComputing investigates scheduling shipments of goods and fuels to oil rigs. The 20 plus page study reports on joint effort between one of the world’s largest oil and gas companies and MemComputing that developed an optimizer for a complex scheduling problem. The involved balancing the delivery of goods and fuel from an onshore dock location to various offshore facilities within a 30-day period.
Using integer linear programming and client data, a mathematical model was tested on a ‘best-in-class’ commercial solver, Gurobi running in the free NEOS optimization server from the University of Wisconsin. The problem was found to be ‘intractable’ for Gurobi.
The same model running on the MEMCPU platform delivered ‘highly optimized solutions in under an hour’. The MEMCPU calculations showed a potential 16% increase in the delivery of goods, a 42% reduction in the number of required ships, and a 48% reduction in the number of overall transits. All in all a $1.5 million in monthly cost savings. The case study breaks down the scheduling problem into various NP-Hard subsets including the famed traveling salesperson problem and develops a novel mathematical model involving space-time graphs and cost-objective functions.
MemComputing’s self-organizing logic gates (SOLGs) have been widely publicized in research publications and the media. See for instance the 2020 Research Gate paper on an Efficient solution of Boolean satisfiability problems. Most recently the technology has been claimed to herald the ‘cryptopocalypse’ (the death of current encryption), effectively ‘overtaking quantum computers’. For more on MemComputing, read the recent U. Houston article on Revolutionizing Computing Architecture with MemComputing.
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