IPS allows product/factory designers in industry to simultaneously consider three strategic factors: flexibility, capacity, and technology (i.e., machines and process structure). It does this by taking an agile manufacturing approach to the design of plant layouts, which means solving the dynamic plant layout problem (DPLP)—an extension of the quadratic assignment problem (QAP), one of the most difficult combinatorial optimization problems in existence.

The core component of IPS is a new hybrid Genetic Algorithm and Tabu Search (GATS) algorithm, which determines ‘optimal' layout sets for any given facility layout problem (FLP) or dynamic plant layout problem (DPLP) instance. One novelty of GATS, as compared to existing solutions, is that it provides multiple ‘optimal' solutions, as opposed to a single solution. This enables users to evaluate different qualitative criteria in order to select a preferred layout.

The GATS software can also be adapted to solve combinatorial optimization problems other than FLP/DPLP, as long as these problems can be solved by a meta-heuristic. Examples include sequencing and scheduling for production management, plant location, routing, and transportation problems.

The GATS software is available for testing on a real-life scenario.

The technology developers are Mr. José María Rodríguez Balbuena (M.Sc., P.Eng.) and Dr. David Bonham, formerly of the Department of Mechanical Engineering at UNB.