INFORMS Journal on Optimization

INFORMS Journal on Optimization aims to publish papers in optimization with particular emphasis on data-driven optimization, optimization methods in machine learning, and exciting real-world applications of optimization. The journal also covers more traditional areas such as: convex and linear optimization; general purpose nonlinear optimization; discrete optimization (combinatorial, integer, mixed integer optimization); optimization under uncertainty (dynamic, stochastic, robust, simulation-based optimization); infinite dimensional optimization; and online optimization). Especially welcomed are contributions studying new and significant applications such as: healthcare; inventory and supply chain management; logistics; revenue management and pricing; energy; the Internet; interfaces with computer science; and finance.