Research off 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 off 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 oft 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.