Rule-evolutionary approach for single-stage multiproduct scheduling with parallel units

Process scheduling shows much more complexity than machine scheduling, and it has been widely studied mainly by using mathematic programming (MP). Due to the difficulties for MP to solve large-size problems, simple rule-base methods are often used in the industry. Metaheuristic methods, such as genetic algorithm and tabu search, combined with suitable heuristic rules, are effective to obtain near-optimal solution for large-size problems. The use of good heuristic rules is crucial to cut down the solution space. Traditionally, great simulation experiments are needed to select suitable rules for diverse scheduling objectives. This paper proposes a novel evolutionary approach to tackle rule selection, rule sequence, and subsequent rule combination for a certain scheduling objective. In our approach, the algorithm itself will automatically select the suitable rule/rule sequence to synthesize an evolved order sequence into a high quality schedule. This approach is able to solve large-size scheduling problems.