When planning for a day of business, how do you calculate the numerous factors that may affect your bottom-line revenue? For Serco, a company which operates a bike-sharing service throughout London, the answer was in their data. In order to find the most efficient and cost-effective way to manage and maintain 12,000 shared bicycles across 800 stations throughout London, Serco teamed up with IBM Partner DecisionBrain to analyze their customer data and usage patterns. For this project, DecisionBrain used IBM Decision Optimization to calculate the optimal number of bikes needed at each station at any given time, and also to plan efficient routes for maintenance teams to repair and redistribute bikes accordingly. The results were seen in a decrease in company costs and an overall more efficient bike sharing service.

Consider this statistic from Enterprise Strategy Group (ESG): "60 percent of respondents indicate that improving operational efficiency is one of the most important objectives their organization expects to achieve from their AI/ML (machine learning) investments." This statistic was a result of looking at the top line data from ESG research that combines responses of enterprise and midmarket organizations. Currently, many companies still have AI and ML firmly in the realm of IT, but the ESG research reveals that AI and ML are becoming more strategic to businesses. Key performance indicators (KPIs) owned by lines of business are cited as one of the top objectives for measuring the effectiveness of AI and ML. It is in this context that we would like to shine the spotlight on the role of optimization technology in supplementing ML to help businesses drive optimal business outcomes through better decisions.

These are the presentations that were made by the IBM Decision Optimization team at the INFORMS Annual Meeting, Seattle, October 2019. Andrea Tramontani, Recent Progress In CPLEX Benders Decomposition In this talk we present the Benders decomposition branch-and-cut that is implemented in CPLEX for Mixed Integer Linear Programming (MILP). We illustrate the main algorithmic components behind our implementation and discuss the latest improvements that are currently work in progress. Finally, we present an extensive computational analysis on some classes of decomposable MILP problems, to assess the performance of Benders branch-and-cut in comparison with the default branch-and-cut of CPLEX. The results show that some models that are out of reach for a "standard" branch-and-cut can instead be solved by Benders decomposition.

ARMONK, N.Y. - 18 Nov 2016: IBM (NYSE: IBM) today announced several cloud data services and features on Bluemix designed to help organizations accelerate the migration of their data to the cloud and more easily generate business insights. Now available on Bluemix, IBM Decision Optimization, Bluemix Lift and dashDB for Transactions can help organizations overcome this challenge and make more informed business decisions by enabling them to more easily aggregate, ingest and analyze expanding workloads. "Cloud is the platform that enables cognitive intelligence," said John Murphy, Vice President, IBM Watson Data Platform. "We're continuing to grow our catalog of cloud data services on Bluemix so that we can help developers and data scientists better manage and more quickly interpret data for business innovation." IBM Decision Optimization on Cloud (including the CPLEX engines) is now in beta on Bluemix. It can ingest large amounts of data including predictions, master and transactional data, business goals, and business rules to prioritize and rank business decisions such as plans and schedules.