Toward Reusable Models: System Development for Optimization Analytics Language (OAL)

Decision optimization has been broadly used in many areas including economics, finance, manufacturing, logistics, and engineering. However, optimization modeling used for decision optimization presents two major challenges. First, it requires expertise in operation research and mathematical programming, which most users, including software developers, engineers, business analysts and end-users, typically do not have. Second, optimization models are usually highly customized, not modular, and not extensible. Sustainable Process Analytics Formalism (SPAF) has been recently proposed at National Institute of Standards and Technology (NIST) to address these challenges, by transitioning from the redevelopment of optimization solutions from scratch towards maintaining an extensible library of analytical models, against which declarative optimization queries can be asked. However, the development of practical algorithms and a system for SPAF presents a number of technical challenges, which have not been addressed. In this paper we (1) propose an object-oriented extension of SPAF, named Optimization Analytics Language (OAL); (2) present OAL system development based on methods to reduce OAL models and queries into a formal mathematical programming formulation; (3) showcase OAL through a simple case study in the domain of manufacturing; and (4) conduct a preliminary experimental study to assess the overhead introduced by OAL.