Maximum order tree algorithm for optimal scheduling of product distribution lines

Research on scheduling and planning in the chemical engineering community subscribes to one of two schools of thought. A general‐purpose optimization approach resorts to using conventional mathematical programming techniques on generic models of a scheduling problem, which has a limitation in its application to large‐scale industrial problems in terms of the computational time involved. The other extreme of heuristic methods lacks guarantees on the quality of the solution. A philosophy is proposed of contextual optimization that exploits problem‐specific knowledge to develop efficient algorithms. This concept is applied to a delivery scheduling problem to generate a tailored graph‐based method called the maximum order tree algorithm, which reduces the CPU time dramatically compared to conventional methods without compromising on the quality of the solution. When applied to a single‐site distribution case study, it resulted in savings of over a quarter of a million dollars per year over the existing heuristic‐rule‐based system.