文摘
Process scheduling shows much more complexity than machine scheduling, and it has been widely studiedmainly 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 algorithmand 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, greatsimulation experiments are needed to select suitable rules for diverse scheduling objectives. This paper proposesa novel evolutionary approach to tackle rule selection, rule sequence, and subsequent rule combination for acertain 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 tosolve large-size scheduling problems.